Testing an integral conceptual model of frailty.
Gobbens, Robbert J; van Assen, Marcel A; Luijkx, Katrien G; Schols, Jos M
2012-09-01
This paper is a report of a study conducted to test three hypotheses derived from an integral conceptual model of frailty. The integral model of frailty describes the pathway from life-course determinants to frailty to adverse outcomes. The model assumes that life-course determinants and the three domains of frailty (physical, psychological, social) affect adverse outcomes, the effect of disease(s) on adverse outcomes is mediated by frailty, and the effect of frailty on adverse outcomes depends on the life-course determinants. In June 2008 a questionnaire was sent to a sample of community-dwelling people, aged 75 years and older (n = 213). Life-course determinants and frailty were assessed using the Tilburg frailty indicator. Adverse outcomes were measured using the Groningen activity restriction scale, the WHOQOL-BREF and questions regarding healthcare utilization. The effect of seven self-reported chronic diseases was examined. Life-course determinants, chronic disease(s), and frailty together explain a moderate to large part of the variance of the seven continuous adverse outcomes (26-57%). All these predictors together explained a significant part of each of the five dichotomous adverse outcomes. The effect of chronic disease(s) on all 12 adverse outcomes was mediated at least partly by frailty. The effect of frailty domains on adverse outcomes did not depend on life-course determinants. Our finding that the adverse outcomes are differently and uniquely affected by the three domains of frailty (physical, psychological, social), and life-course determinants and disease(s), emphasizes the importance of an integral conceptual model of frailty. © 2011 Blackwell Publishing Ltd.
Gobbens, Robbert J J; Krans, Anita; van Assen, Marcel A L M
2015-01-01
The aim of this cross-sectional study was to examine the validity of an integral model of the associations between life-course determinants, disease(s), frailty, and adverse outcomes in older persons who are resident in assisted living facilities. Between June 2013 and May 2014 seven assisted living facilities were contacted. A total of 221 persons completed the questionnaire on life-course determinants, frailty (using the Tilburg Frailty Indicator), self-reported chronic diseases, and adverse outcomes disability, quality of life, health care utilization, and falls. Adverse outcomes were analyzed with sequential (logistic) regression analyses. The integral model is partially validated. Life-course determinants and disease(s) affected only physical frailty. All three frailty domains (physical, psychological, social) together affected disability, quality of life, visits to a general practitioner, and falls. Contrary to the model, disease(s) had no effect on adverse outcomes after controlling for frailty. Life-course determinants affected adverse outcomes, with unhealthy lifestyle having consistent negative effects, and women had more disability, scored lower on physical health, and received more personal and informal care after controlling for all other predictors. The integral model of frailty is less useful for predicting adverse outcomes of residents of assisted living facilities than for community-dwelling older persons, because these residents are much frailer and already have access to healthcare facilities. The present study showed that a multidimensional assessment of frailty, distinguishing three domains of frailty (physical, psychological, social), is beneficial with respect to predicting adverse outcomes in residents of assisted living facilities. Copyright © 2015. Published by Elsevier Ireland Ltd.
Application Of Shared Gamma And Inverse-Gaussian Frailty Models ...
Shared Gamma and Inverse-Gaussian Frailty models are used to analyze the survival times of patients who are clustered according to cancer/tumor types under Parametric Proportional Hazard framework. The result of the ... However, no evidence is strong enough for preference of either Gamma or Inverse Gaussian Frailty.
Log-normal frailty models fitted as Poisson generalized linear mixed models.
Hirsch, Katharina; Wienke, Andreas; Kuss, Oliver
2016-12-01
The equivalence of a survival model with a piecewise constant baseline hazard function and a Poisson regression model has been known since decades. As shown in recent studies, this equivalence carries over to clustered survival data: A frailty model with a log-normal frailty term can be interpreted and estimated as a generalized linear mixed model with a binary response, a Poisson likelihood, and a specific offset. Proceeding this way, statistical theory and software for generalized linear mixed models are readily available for fitting frailty models. This gain in flexibility comes at the small price of (1) having to fix the number of pieces for the baseline hazard in advance and (2) having to "explode" the data set by the number of pieces. In this paper we extend the simulations of former studies by using a more realistic baseline hazard (Gompertz) and by comparing the model under consideration with competing models. Furthermore, the SAS macro %PCFrailty is introduced to apply the Poisson generalized linear mixed approach to frailty models. The simulations show good results for the shared frailty model. Our new %PCFrailty macro provides proper estimates, especially in case of 4 events per piece. The suggested Poisson generalized linear mixed approach for log-normal frailty models based on the %PCFrailty macro provides several advantages in the analysis of clustered survival data with respect to more flexible modelling of fixed and random effects, exact (in the sense of non-approximate) maximum likelihood estimation, and standard errors and different types of confidence intervals for all variance parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Lindley frailty model for a class of compound Poisson processes
Kadilar, Gamze Özel; Ata, Nihal
2013-10-01
The Lindley distribution gain importance in survival analysis for the similarity of exponential distribution and allowance for the different shapes of hazard function. Frailty models provide an alternative to proportional hazards model where misspecified or omitted covariates are described by an unobservable random variable. Despite of the distribution of the frailty is generally assumed to be continuous, it is appropriate to consider discrete frailty distributions In some circumstances. In this paper, frailty models with discrete compound Poisson process for the Lindley distributed failure time are introduced. Survival functions are derived and maximum likelihood estimation procedures for the parameters are studied. Then, the fit of the models to the earthquake data set of Turkey are examined.
Pérez-Zepeda, M U; Ávila-Funes, J A; Gutiérrez-Robledo, L M; García-Peña, C
2016-01-01
The implementation of an aging biomarker into clinical practice is under debate. The Frailty Index is a model of deficit accumulation and has shown to accurately capture frailty in older adults, thus bridging biological with clinical practice. To describe the association of socio-demographic characteristics and the Frailty Index in different age groups (from 20 to over one hundred years) in a representative sample of Mexican subjects. Cross-sectional analysis. Nationwide and population-representative survey. Adults 20-years and older interviewed during the last Mexican National Health and Nutrition Survey (2012). A 30-item Frailty Index following standard construction was developed. Multi-level regression models were performed to test the associations of the Frailty Index with multiple socio-demographic characteristics across age groups. A total of 29,504 subjects was analyzed. The 30-item Frailty Index showed the highest scores in the older age groups, especially in women. No sociodemographic variable was associated with the Frailty Index in all the studied age groups. However, employment, economic income, and smoking status were more consistently found across age groups. To our knowledge, this is the first report describing the Frailty Index in a representative large sample of a Latin American country. Increasing age and gender were closely associated with a higher score.
Parametric overdispersed frailty models for current status data.
Abrams, Steven; Aerts, Marc; Molenberghs, Geert; Hens, Niel
2017-12-01
Frailty models have a prominent place in survival analysis to model univariate and multivariate time-to-event data, often complicated by the presence of different types of censoring. In recent years, frailty modeling gained popularity in infectious disease epidemiology to quantify unobserved heterogeneity using Type I interval-censored serological data or current status data. In a multivariate setting, frailty models prove useful to assess the association between infection times related to multiple distinct infections acquired by the same individual. In addition to dependence among individual infection times, overdispersion can arise when the observed variability in the data exceeds the one implied by the model. In this article, we discuss parametric overdispersed frailty models for time-to-event data under Type I interval-censoring, building upon the work by Molenberghs et al. (2010) and Hens et al. (2009). The proposed methodology is illustrated using bivariate serological data on hepatitis A and B from Flanders, Belgium anno 1993-1994. Furthermore, the relationship between individual heterogeneity and overdispersion at a stratum-specific level is studied through simulations. Although it is important to account for overdispersion, one should be cautious when modeling both individual heterogeneity and overdispersion based on current status data as model selection is hampered by the loss of information due to censoring. © 2017, The International Biometric Society.
Toward a Comprehensive Model of Frailty: An Emerging Concept From the Hong Kong Centenarian Study.
Kwan, Joseph Shiu Kwong; Lau, Bobo Hi Po; Cheung, Karen Siu Lan
2015-06-01
A better understanding of the essential components of frailty is important for future developments of management strategies. We aimed to assess the incremental validity of a Comprehensive Model of Frailty (CMF) over Frailty Index (FI) in predicting self-rated health and functional dependency amongst near-centenarians and centenarians. Cross-sectional, community-based study. Two community-based social and clinical networks. One hundred twenty-four community-dwelling Chinese near-centenarians and centenarians. Frailty was first assessed using a 32-item FI (FI-32). Then, a new CMF was constructed by adding 12 items in the psychological, social/family, environmental, and economic domains to the FI-32. Hierarchical multiple regressions explored whether the new CMF provided significant additional predictive power for self-rated health and instrumental activities of daily living (IADL) dependency. Mean age was 97.7 (standard deviation 2.3) years, with a range from 95 to 108, and 74.2% were female. Overall, 16% of our participants were nonfrail, 59% were prefrail, and 25% were frail. Frailty according to FI-32 significantly predicted self-rated health and IADL dependency beyond the effect of age and gender. Inclusion of the new CMF into the regression models provided significant additional predictive power beyond FI-32 on self-rated health, but not IADL dependency. A CMF should ideally be a multidimensional and multidisciplinary construct including physical, cognitive, functional, psychosocial/family, environmental, and economic factors. Copyright © 2015 AMDA - The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.
A matrix approach to the statistics of longevity in heterogeneous frailty models
Hal Caswell
2014-09-01
Full Text Available Background: The gamma-Gompertz model is a fixed frailty model in which baseline mortality increasesexponentially with age, frailty has a proportional effect on mortality, and frailty at birth follows a gamma distribution. Mortality selects against the more frail, so the marginal mortality rate decelerates, eventually reaching an asymptote. The gamma-Gompertz is one of a wider class of frailty models, characterized by the choice of baseline mortality, effects of frailty, distributions of frailty, and assumptions about the dynamics of frailty. Objective: To develop a matrix model to compute all the statistical properties of longevity from thegamma-Gompertz and related models. Methods: I use the vec-permutation matrix formulation to develop a model in which individuals are jointly classified by age and frailty. The matrix is used to project the age and frailty dynamicsof a cohort and the fundamental matrix is used to obtain the statistics of longevity. Results: The model permits calculation of the mean, variance, coefficient of variation, skewness and all moments of longevity, the marginal mortality and survivorship functions, the dynamics of the frailty distribution, and other quantities. The matrix formulation extends naturally to other frailty models. I apply the analysis to the gamma-Gompertz model (for humans and laboratory animals, the gamma-Makeham model, and the gamma-Siler model, and to a hypothetical dynamic frailty model characterized by diffusion of frailty with reflecting boundaries.The matrix model permits partitioning the variance in longevity into components due to heterogeneity and to individual stochasticity. In several published human data sets, heterogeneity accounts for less than 10Š of the variance in longevity. In laboratory populations of five invertebrate animal species, heterogeneity accounts for 46Š to 83Š ofthe total variance in longevity.
Liquet Benoit
2012-06-01
Full Text Available Abstract Background Multistate models have become increasingly useful to study the evolution of a patient’s state over time in intensive care units ICU (e.g. admission, infections, alive discharge or death in ICU. In addition, in critically-ill patients, data come from different ICUs, and because observations are clustered into groups (or units, the observed outcomes cannot be considered as independent. Thus a flexible multi-state model with random effects is needed to obtain valid outcome estimates. Methods We show how a simple multi-state frailty model can be used to study semi-competing risks while fully taking into account the clustering (in ICU of the data and the longitudinal aspects of the data, including left truncation and right censoring. We suggest the use of independent frailty models or joint frailty models for the analysis of transition intensities. Two distinct models which differ in the definition of time t in the transition functions have been studied: semi-Markov models where the transitions depend on the waiting times and nonhomogenous Markov models where the transitions depend on the time since inclusion in the study. The parameters in the proposed multi-state model may conveniently be computed using a semi-parametric or parametric approach with an existing R package FrailtyPack for frailty models. The likelihood cross-validation criterion is proposed to guide the choice of a better fitting model. Results We illustrate the use of our approach though the analysis of nosocomial infections (ventilator-associated pneumonia infections: VAP in ICU, with “alive discharge” and “death” in ICU as other endpoints. We show that the analysis of dependent survival data using a multi-state model without frailty terms may underestimate the variance of regression coefficients specific to each group, leading to incorrect inferences. Some factors are wrongly significantly associated based on the model without frailty terms. This
Testing homogeneity in Weibull-regression models.
Bolfarine, Heleno; Valença, Dione M
2005-10-01
In survival studies with families or geographical units it may be of interest testing whether such groups are homogeneous for given explanatory variables. In this paper we consider score type tests for group homogeneity based on a mixing model in which the group effect is modelled as a random variable. As opposed to hazard-based frailty models, this model presents survival times that conditioned on the random effect, has an accelerated failure time representation. The test statistics requires only estimation of the conventional regression model without the random effect and does not require specifying the distribution of the random effect. The tests are derived for a Weibull regression model and in the uncensored situation, a closed form is obtained for the test statistic. A simulation study is used for comparing the power of the tests. The proposed tests are applied to real data sets with censored data.
Surrogate screening models for the low physical activity criterion of frailty.
Eckel, Sandrah P; Bandeen-Roche, Karen; Chaves, Paulo H M; Fried, Linda P; Louis, Thomas A
2011-06-01
Low physical activity, one of five criteria in a validated clinical phenotype of frailty, is assessed by a standardized, semiquantitative questionnaire on up to 20 leisure time activities. Because of the time demanded to collect the interview data, it has been challenging to translate to studies other than the Cardiovascular Health Study (CHS), for which it was developed. Considering subsets of activities, we identified and evaluated streamlined surrogate assessment methods and compared them to one implemented in the Women's Health and Aging Study (WHAS). Using data on men and women ages 65 and older from the CHS, we applied logistic regression models to rank activities by "relative influence" in predicting low physical activity.We considered subsets of the most influential activities as inputs to potential surrogate models (logistic regressions). We evaluated predictive accuracy and predictive validity using the area under receiver operating characteristic curves and assessed criterion validity using proportional hazards models relating frailty status (defined using the surrogate) to mortality. Walking for exercise and moderately strenuous household chores were highly influential for both genders. Women required fewer activities than men for accurate classification. The WHAS model (8 CHS activities) was an effective surrogate, but a surrogate using 6 activities (walking, chores, gardening, general exercise, mowing and golfing) was also highly predictive. We recommend a 6 activity questionnaire to assess physical activity for men and women. If efficiency is essential and the study involves only women, fewer activities can be included.
Physical frailty, disability, and dynamics in health perceptions: a preliminary mediation model
Mulasso A
2016-03-01
Full Text Available Anna Mulasso, Mattia Roppolo, Emanuela Rabaglietti Department of Psychology, University of Torino, Torino, Italy Purpose: Frailty is a condition characterized by loss of functional reserve and altered homeostatic capacity. The aging process is related with complex indicators of physiological state. This study aims, with a preliminary mediation model, to reveal the possible role of mediator of health perceptions variability in the relationship between frailty and disability. Patients and methods: A longitudinal study (100 days was performed. Data from 92 institutionalized older adults were used in the analysis. Frailty was assessed in baseline using the Italian version of the Survey of Health, Ageing and Retirement in Europe – Frailty Instrument; health perceptions were assessed on a daily basis by three visual analog scale questions; and disability was measured in baseline and post-test using the Katz Activities of Daily Living questionnaire. The product-of-coefficient mediation approach was used to test direct and indirect effects of frailty. Results: Results showed that daily variability of health perceptions plays the role of mediator between frailty and disability. In all the steps, statistically significant results were found. Conclusion: This preliminary result may indicate that physical frailty increases the variability in health perceptions contributing to disability. Keywords: functional decline, loss of autonomy, variability, health outcomes, dynamic systems
Pre-frailty and frailty of elderly residents in a municipality with a low Human Development Index
Wanderley Matos Reis Júnior
2014-08-01
Full Text Available OBJECTIVE: to identify the prevalence of the factors associated with pre-frailty and frailty of elderly residents in a municipality with a low Human Development IndexMETHOD: Cross-sectional study with a populational and household framework conducted with 316 elderly people. Frailty was determined from the presence of three or more of the following factors: (i self-reported unintentional weight loss; (ii lack of strength and energy; (iii weakness; (iv slowness; (v low level of physical activity. The association between frailty and socio-demographic, behavioral and health factors was measured using the multinomial logistic regression technique.RESULTS: The prevalence of pre-frailty and frailty was 58.7% and 23.8%, respectively. The adjusted regression model showed that the state of pre-frailty was associated with gender, age group and BMI, and frailty was associated with gender, age group, hospitalization, functional capacity, and self-perceived health.CONCLUSION: The evidence presented in this study demonstrates more variables associated with the frailty condition, reinforcing the concept of a multifactorial clinical syndrome that may result in the loss of functionality.
A Bayesian, generalized frailty model for comet assays.
Ghebretinsae, Aklilu Habteab; Faes, Christel; Molenberghs, Geert; De Boeck, Marlies; Geys, Helena
2013-05-01
This paper proposes a flexible modeling approach for so-called comet assay data regularly encountered in preclinical research. While such data consist of non-Gaussian outcomes in a multilevel hierarchical structure, traditional analyses typically completely or partly ignore this hierarchical nature by summarizing measurements within a cluster. Non-Gaussian outcomes are often modeled using exponential family models. This is true not only for binary and count data, but also for, example, time-to-event outcomes. Two important reasons for extending this family are for (1) the possible occurrence of overdispersion, meaning that the variability in the data may not be adequately described by the models, which often exhibit a prescribed mean-variance link, and (2) the accommodation of a hierarchical structure in the data, owing to clustering in the data. The first issue is dealt with through so-called overdispersion models. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. In the case of time-to-event data, one encounters, for example, the gamma frailty model (Duchateau and Janssen, 2007 ). While both of these issues may occur simultaneously, models combining both are uncommon. Molenberghs et al. ( 2010 ) proposed a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. Here, we use this method to model data from a comet assay with a three-level hierarchical structure. Although a conjugate gamma random effect is used for the overdispersion random effect, both gamma and normal random effects are considered for the hierarchical random effect. Apart from model formulation, we place emphasis on Bayesian estimation. Our proposed method has an upper hand over the traditional analysis in that it (1) uses the appropriate distribution stipulated in the literature; (2) deals
Hilbe, Joseph M
2009-01-01
This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...
Estimation in the positive stable shared frailty Cox proportional hazards model
Martinussen, Torben; Pipper, Christian Bressen
2005-01-01
model in situations where the correlated survival data show a decreasing association with time. In this paper, we devise a likelihood based estimation procedure for the positive stable shared frailty Cox model, which is expected to obtain high efficiency. The proposed estimator is provided with large...
(Non) linear regression modelling
Cizek, P.; Gentle, J.E.; Hardle, W.K.; Mori, Y.
2012-01-01
We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1,…,Yl), l ∈ N, which are explained by a model, and independent (exogenous, explanatory) variables X = (X1,…,Xp),p ∈ N, which explain or
Additive gamma frailty models with applications to competing risks in related individuals
Eriksson, Frank; Scheike, Thomas
2015-01-01
Epidemiological studies of related individuals are often complicated by the fact that follow-up on the event type of interest is incomplete due to the occurrence of other events. We suggest a class of frailty models with cause-specific hazards for correlated competing events in related individual...
Inference for shared-frailty survival models with left-truncated data
van den Berg, G.J.; Drepper, B.
2016-01-01
Shared-frailty survival models specify that systematic unobserved determinants of duration outcomes are identical within groups of individuals. We consider random-effects likelihood-based statistical inference if the duration data are subject to left-truncation. Such inference with left-truncated
Gobbens, R.J.J.; Krans, A.; van Assen, M.A.L.M.
2015-01-01
Objective The aim of this cross-sectional study was to examine the validity of an integral model of the associations between life-course determinants, disease(s), frailty, and adverse outcomes in older persons who are resident in assisted living facilities. Methods Between June 2013 and May 2014
Gobbens, Robbert J J; Krans, Anita; van Assen, Marcel A L M
2015-01-01
Objective: The aim of this cross-sectional study was to examine the validity of an integral model of the associations between life-course determinants, disease(s), frailty, and adverse outcomes in older persons who are resident in assisted living facilities. Methods: Between June 2013 and May 2014
Panel Smooth Transition Regression Models
González, Andrés; Terasvirta, Timo; Dijk, Dick van
We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
Performance-Based Measures Associate With Frailty in Patients With End-Stage Liver Disease.
Lai, Jennifer C; Volk, Michael L; Strasburg, Debra; Alexander, Neil
2016-12-01
Physical frailty, as measured by the Fried Frailty Index, is increasingly recognized as a critical determinant of outcomes in patients with cirrhosis. However, its utility is limited by the inclusion of self-reported components. We aimed to identify performance-based measures associated with frailty in patients with cirrhosis. Patients with cirrhosis, aged 50 years or older, underwent: 6-minute walk test (cardiopulmonary endurance), chair stands in 30 seconds (muscle endurance), isometric knee extension (lower extremity strength), unipedal stance time (static balance), and maximal step length (dynamic balance/coordination). Linear regression associated each physical performance test with frailty. Principal components exploratory factor analysis evaluated the interrelatedness of frailty and the 5 physical performance tests. Of 40 patients with cirrhosis, with a median age of 64 years and Model for End-stage Liver Disease (MELD) MELD of 12.10 (25%) were frail by Fried Frailty Index ≥3. Frail patients with cirrhosis had poorer performance in 6-minute walk test distance (231 vs 338 m), 30-second chair stands (7 vs 10), isometric knee extension (86 vs 122 Newton meters), and maximal step length (22 vs 27 in. (P ≤ 0.02 for each). Each physical performance test was significantly associated with frailty (P test to a single factor-frailty. Frailty in cirrhosis is a multidimensional construct that is distinct from liver dysfunction and incorporates endurance, strength, and balance. Our data provide specific targets for prehabilitation interventions aimed at reducing frailty in patients with cirrhosis in preparation for liver transplantation.
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
Regression Models for Repairable Systems
Novák, Petr
2015-01-01
Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf
A joint frailty-copula model between tumour progression and death for meta-analysis.
Emura, Takeshi; Nakatochi, Masahiro; Murotani, Kenta; Rondeau, Virginie
2017-12-01
Dependent censoring often arises in biomedical studies when time to tumour progression (e.g., relapse of cancer) is censored by an informative terminal event (e.g., death). For meta-analysis combining existing studies, a joint survival model between tumour progression and death has been considered under semicompeting risks, which induces dependence through the study-specific frailty. Our paper here utilizes copulas to generalize the joint frailty model by introducing additional source of dependence arising from intra-subject association between tumour progression and death. The practical value of the new model is particularly evident for meta-analyses in which only a few covariates are consistently measured across studies and hence there exist residual dependence. The covariate effects are formulated through the Cox proportional hazards model, and the baseline hazards are nonparametrically modeled on a basis of splines. The estimator is then obtained by maximizing a penalized log-likelihood function. We also show that the present methodologies are easily modified for the competing risks or recurrent event data, and are generalized to accommodate left-truncation. Simulations are performed to examine the performance of the proposed estimator. The method is applied to a meta-analysis for assessing a recently suggested biomarker CXCL12 for survival in ovarian cancer patients. We implement our proposed methods in R joint.Cox package.
Visualising the pedagogic frailty model as a frame for the scholarship of teaching and learning
Ian M. Kinchin
2017-11-01
Full Text Available Purpose – The purpose of this study is to offer exploration of pedagogic frailty as a framework to support professional development of university teachers in a personalised and discipline-sensitive way. Design/methodology/approach – The method involves participants constructing a concept map for each dimension of the model. These maps must have high explanatory power to act as a frame for developing a personal narrative to support reflection on practice. This reflection starts from the academic’s current knowledge structure and provides a bespoke, individualised focus for further learning. Findings – This conceptual paper is informed by case studies of academics’ interactions with the frailty model that have helped to refine it as a faculty development tool. This is clarified by providing explicit requirements of an “excellent” map, and places the reflective process within a learning theory that is aligned with the values that underpin the model. Originality/value – The type of rhizomatic learning that is supported by the model, in which there are no imposed learning outcomes or strictly delineated pathways to success, is particularly suited to support the professional development of more senior academics. This represents an innovative approach to faculty development.
Association between Frailty and Dementia
Kulmala, J; Nykänen, I; Mänty, Minna Regina
2014-01-01
dementia with Lewy bodies and 8 persons (1%) had some other type of dementia. Multivariate logistic regression models showed that frail persons were almost 8 times more likely to have cognitive impairment (OR 7.8, 95% CI 4.0-15.0), 8 times more likely to have some kind of dementia (OR 8.0, 95% CI 4.0...... of the participants was assessed using the Cardiovascular Health Study criteria. Cognitive function was assessed with the Mini-Mental State Examination (MMSE). Clinically diagnosed dementia was assessed by specialists using diagnostic criteria. The associations between frailty and cognition were investigated using...
Interpretation of commonly used statistical regression models.
Kasza, Jessica; Wolfe, Rory
2014-01-01
A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.
Shen, Chung-Wei; Chen, Yi-Hau
2018-03-13
We propose a model selection criterion for semiparametric marginal mean regression based on generalized estimating equations. The work is motivated by a longitudinal study on the physical frailty outcome in the elderly, where the cluster size, that is, the number of the observed outcomes in each subject, is "informative" in the sense that it is related to the frailty outcome itself. The new proposal, called Resampling Cluster Information Criterion (RCIC), is based on the resampling idea utilized in the within-cluster resampling method (Hoffman, Sen, and Weinberg, 2001, Biometrika 88, 1121-1134) and accommodates informative cluster size. The implementation of RCIC, however, is free of performing actual resampling of the data and hence is computationally convenient. Compared with the existing model selection methods for marginal mean regression, the RCIC method incorporates an additional component accounting for variability of the model over within-cluster subsampling, and leads to remarkable improvements in selecting the correct model, regardless of whether the cluster size is informative or not. Applying the RCIC method to the longitudinal frailty study, we identify being female, old age, low income and life satisfaction, and chronic health conditions as significant risk factors for physical frailty in the elderly. © 2018, The International Biometric Society.
Coley, Rebecca Yates; Browna, Elizabeth R.
2016-01-01
Inconsistent results in recent HIV prevention trials of pre-exposure prophylactic interventions may be due to heterogeneity in risk among study participants. Intervention effectiveness is most commonly estimated with the Cox model, which compares event times between populations. When heterogeneity is present, this population-level measure underestimates intervention effectiveness for individuals who are at risk. We propose a likelihood-based Bayesian hierarchical model that estimates the individual-level effectiveness of candidate interventions by accounting for heterogeneity in risk with a compound Poisson-distributed frailty term. This model reflects the mechanisms of HIV risk and allows that some participants are not exposed to HIV and, therefore, have no risk of seroconversion during the study. We assess model performance via simulation and apply the model to data from an HIV prevention trial. PMID:26869051
Hyde, Zoë; Flicker, Leon; Smith, Kate; Atkinson, David; Fenner, Stephen; Skeaf, Linda; Malay, Roslyn; Lo Giudice, Dina
2016-05-01
Frailty represents a loss of homeostasis, markedly increasing the risk of death and disability. Frailty has been measured in several ethnic groups, but not, to our knowledge, in Aboriginal Australians. We aimed to determine the prevalence and incidence of frailty, and associations with mortality and disability, in remote-living Aboriginal people. Between 2004 and 2006, we recruited 363 Aboriginal people aged ≥ 45 years from 6 remote communities and one town in the Kimberley region of Western Australia (wave 1). Between 2011 and 2013, 182 surviving participants were followed-up (wave 2). We assessed frailty with an index, comprising 20 health-related items. Participants with ≥ 4 deficits (frailty index ≥ 0.2) were considered frail. Disability was assessed by family/carer report. Those unable to do ≥ 2 of 6 key or instrumental activities of daily living were considered disabled. We investigated associations between frailty, and disability and mortality, with logistic regression and Cox proportional hazards models. At wave 1 (W1), 188 participants (65.3%) were frail, and of robust people at W1 who participated in wave 2, 38 (51.4%) had become frail. Frailty emerged at a younger age than expected. A total of 109 people died (30.0%), of whom 80 (73.4%) were frail at W1. Frailty at W1 was not associated with becoming disabled, but was associated with mortality (HR = 1.9; 95% CI 1.2, 3.0). Frailty in remote-living Aboriginal Australians is highly prevalent; substantially higher than in other populations. Research to understand the underlying causes of frailty in this population, and if possible, reverse frailty, is urgently needed. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Koopman, S.J.; Lucas, A.; Schwaab, B.
2012-01-01
We develop a high-dimensional, nonlinear, and non-Gaussian dynamic factor model for the decomposition of systematic default risk conditions into latent components for (1) macroeconomic/financial risk, (2) autonomous default dynamics (frailty), and (3) industry-specific effects. We analyze discrete
Najaf Zare
2017-08-01
Full Text Available Background: Time to first pregnancy (TTFP has never been studied in an Iranian setting. Lifestyle, occupational and environmental factors have been suggested to affect the female reproduction. Objective: This study was conducted to measure TTFP in the south of Iran and survey the effects of several similar factors on TTFP by frailty models. Materials and Methods: The data on TTFP were available for 882 women who were randomly selected from the rural population (the south of Iran. Only the first and the planned pregnancies of every woman were included. The data were collected retrospectively by using self-administered questionnaires. Frailty and shared frailty models were used to determine which factors had the highest impact on TTFP. Results: The median TTFP was 6.4 months and several factors were surveyed. However, only the age of marriage, height, maternal education and regularity of menstruation prior to conception were selected in the multivariable models. Conclusion: Among the several factors which were included in the study, the result of frailty model showed that the height, age of marriage and regular menstruation seemed more notable predictors of TTFP.
Association between employee benefits and frailty in community-dwelling older adults.
Avila-Funes, José Alberto; Paniagua-Santos, Diana Leticia; Escobar-Rivera, Vicente; Navarrete-Reyes, Ana Patricia; Aguilar-Navarro, Sara; Amieva, Hélène
2016-05-01
The phenotype of frailty has been associated with an increased vulnerability for the development of adverse health-related outcomes. The origin of frailty is multifactorial and financial issues could be implicated, as they have been associated with health status, well-being and mortality. However, the association between economic benefits and frailty has been poorly explored. Therefore, the objective was to determine the association between employee benefits and frailty. A cross-sectional study of 927 community-dwelling older adults aged 70 years and older participating in the Mexican Study of Nutritional and Psychosocial Markers of Frailty was carried out. Employee benefits were established according to eight characteristics: bonus, profit sharing, pension, health insurance, food stamps, housing credit, life insurance, and Christmas bonus. Frailty was defined according to a slightly modified version of the phenotype proposed by Fried et al. Multinomial logistic regression models were run to determine the association between employee benefits and frailty adjusting by sociodemographic and health covariates. The prevalence of frailty was 14.1%, and 4.4% of participants rated their health status as "poor." Multinomial logistic regression analyses showed that employee benefits were statistically and independently associated with the frail subgroup (OR 0.85; 95% CI 0.74-0.98; P = 0.027) even after adjusting for potential confounders. Fewer employee benefits are associated with frailty. Supporting spreading employee benefits for older people could have a positive impact on the development of frailty and its consequences. Geriatr Gerontol Int 2016; 16: 606-611. © 2015 Japan Geriatrics Society.
Al Saedi, Ahmed; Gunawardene, Piumali; Bermeo, Sandra; Vogrin, Sara; Boersma, Derek; Phu, Steven; Singh, Lakshman; Suriyaarachchi, Pushpa; Duque, Gustavo
2018-02-01
Lamin A is a protein of the nuclear lamina. Low levels of lamin A expression are associated with osteosarcopenia in mice. In this study, we hypothesized that low lamin A expression is also associated with frailty in humans. We aimed to develop a non-invasive method to quantify lamin A expression in epithelial and circulating osteoprogenitor (COP) cells, and to determine the relationship between lamin A expression and frailty in older individuals. COP cells and buccal swabs were obtained from 66 subjects (median age 74; 60% female; 26 non-frail, 23 pre-frail, and 17 frail) participating at the Nepean Osteoporosis and Frailty (NOF) Study. We quantified physical performance and disability, and stratified frailty in this population. Lamin A expression in epithelial and COP cells was quantified by flow cytometry. Linear regression models estimated the relationship between lamin A expression in buccal and COP cells, and prevalent disability and frailty. Lamin A expression in buccal cells showed no association with either disability or frailty. Low lamin A expression values in COP cells were associated with frailty. Frail individuals showed 60% lower levels of lamin A compared to non-frail (95% CI -36 to -74%, p<0.001) and 62% lower levels compared to pre-frail (95%CI -40 to -76%, p<0.001). In summary, we have identified lamin A expression in COP cells as a strong indicator of frailty. Further work is needed to understand lamin A expression as a risk stratifier, biomarker, or therapeutic target in frail older persons. Copyright © 2017 Elsevier Inc. All rights reserved.
Guowei Li
Full Text Available To compare the predictive accuracy of the frailty index (FI of deficit accumulation and the phenotypic frailty (PF model in predicting risks of future falls, fractures and death in women aged ≥55 years.Based on the data from the Global Longitudinal Study of Osteoporosis in Women (GLOW 3-year Hamilton cohort (n = 3,985, we compared the predictive accuracy of the FI and PF in risks of falls, fractures and death using three strategies: (1 investigated the relationship with adverse health outcomes by increasing per one-fifth (i.e., 20% of the FI and PF; (2 trichotomized the FI based on the overlap in the density distribution of the FI by the three groups (robust, pre-frail and frail which were defined by the PF; (3 categorized the women according to a predicted probability function of falls during the third year of follow-up predicted by the FI. Logistic regression models were used for falls and death, while survival analyses were conducted for fractures.The FI and PF agreed with each other at a good level of consensus (correlation coefficients ≥ 0.56 in all the three strategies. Both the FI and PF approaches predicted adverse health outcomes significantly. The FI quantified the risks of future falls, fractures and death more precisely than the PF. Both the FI and PF discriminated risks of adverse outcomes in multivariable models with acceptable and comparable area under the curve (AUCs for falls (AUCs ≥ 0.68 and death (AUCs ≥ 0.79, and c-indices for fractures (c-indices ≥ 0.69 respectively.The FI is comparable with the PF in predicting risks of adverse health outcomes. These findings may indicate the flexibility in the choice of frailty model for the elderly in the population-based settings.
Li, Guowei; Thabane, Lehana; Ioannidis, George; Kennedy, Courtney; Papaioannou, Alexandra; Adachi, Jonathan D
2015-01-01
To compare the predictive accuracy of the frailty index (FI) of deficit accumulation and the phenotypic frailty (PF) model in predicting risks of future falls, fractures and death in women aged ≥55 years. Based on the data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) 3-year Hamilton cohort (n = 3,985), we compared the predictive accuracy of the FI and PF in risks of falls, fractures and death using three strategies: (1) investigated the relationship with adverse health outcomes by increasing per one-fifth (i.e., 20%) of the FI and PF; (2) trichotomized the FI based on the overlap in the density distribution of the FI by the three groups (robust, pre-frail and frail) which were defined by the PF; (3) categorized the women according to a predicted probability function of falls during the third year of follow-up predicted by the FI. Logistic regression models were used for falls and death, while survival analyses were conducted for fractures. The FI and PF agreed with each other at a good level of consensus (correlation coefficients ≥ 0.56) in all the three strategies. Both the FI and PF approaches predicted adverse health outcomes significantly. The FI quantified the risks of future falls, fractures and death more precisely than the PF. Both the FI and PF discriminated risks of adverse outcomes in multivariable models with acceptable and comparable area under the curve (AUCs) for falls (AUCs ≥ 0.68) and death (AUCs ≥ 0.79), and c-indices for fractures (c-indices ≥ 0.69) respectively. The FI is comparable with the PF in predicting risks of adverse health outcomes. These findings may indicate the flexibility in the choice of frailty model for the elderly in the population-based settings.
A Seemingly Unrelated Poisson Regression Model
King, Gary
1989-01-01
This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.
Saleha Jaber Al-Kuwaiti
2015-11-01
Full Text Available BackgroundFrailty describes the ageing-associated loss of physiological and psychological reserves leading to an increased risk of adverse health outcomes. Many developed countries view frailty as a major priority for their health and social care systems. Less is known about frailty in less-developed countries. The purpose of this study was to determine the prevalence of frailty in a sample of community dwelling older people in the United Arab Emirates.MethodsThis was a cross sectional study of community dwelling Emirati adults aged 55 years and older (n=160 in Abu Dhabi, United Arab Emirates. Data was collected at interview by questionnaire and physical measurements. Frailty was defined according to the criteria of the Fried Frailty Index. The prevalence of frailty and its association with selected independent variables was assessed.ResultsThe overall prevalence of frailty (95% CI was 47% (39-55. Higher levels of frailty were seen in older age groups, women, those who were non-married, those with recent hospital admission, those with co-morbid conditions, those on more than five medications and those with lower forced expiratory volume and mini-mental state examination score. After adjustment in a multiple logistic regression model only age and gender were found to be independently associated with frailty.ConclusionA high prevalence of frailty was found amongst older Emiratis. Given that frailty is associated with adverse health outcomes and can be a means of identifying opportunities for intervention in clinical practice and health policy, further attention and consideration within professional and public health policy circles is needed.
Relationship between Sensory Perception and Frailty in a Community-Dwelling Elderly Population.
Somekawa, S; Mine, T; Ono, K; Hayashi, N; Obuchi, S; Yoshida, H; Kawai, H; Fujiwara, Y; Hirano, H; Kojima, M; Ihara, K; Kim, H
2017-01-01
Aging anorexia, defined as loss of appetite and/or reduced food intake, has been postulated as a risk factor for frailty. Impairments of taste and smell perception in elderly people can lead to reduced enjoyment of food and contribute to the anorexia of aging. To evaluate the relationship between frailty and taste and smell perception in elderly people living in urban areas. Data from the baseline evaluation of 768 residents aged ≥ 65 years who enrolled in a comprehensive geriatric health examination survey was analyzed. Fourteen out of 29-items of Appetite, Hunger, Sensory Perception questionnaire (AHSP), frailty, age, sex, BMI, chronic conditions and IADL were evaluated. AHSP was analyzed as the total score of 8 taste items (T) and 6 smell items (S). Frailty was diagnosed using a modified Fried's frailty criteria. The area under the receiver operator curves for detection of frailty demonstrated that T (0.715) had moderate accuracy, but S (0.657) had low accuracy. The cutoffs, sensitivity, specificity and Youden Index (YI) values for each perception were T: Cutoff 26.5 (YI: 0.350, sensitivity: 0.639, specificity: 0.711) and S: Cutoff 18.5 (YI: 0.246, sensitivity: 0.690, specificity: 0.556). Results from multiple logistic regression models, after adjusting for age, sex, IADL and chronic conditions showed that participants under the T cutoff were associated with exhaustion and those below the S cutoff were associated with slow walking speed. The adjusted logistic models for age, sex, IADL and chronic conditions showed significant association between T and frailty (OR 2.81, 95% CI 1.29-6.12), but not between S and frailty (OR 1.73, 95% CI 0.83-3.63). Taste and smell perception, particularly taste perception, were associated with a greater risk of frailty in community-dwelling elderly people. These results suggest that lower taste and smell perception may be an indicator of frailty in old age.
Gaussian Process Regression Model in Spatial Logistic Regression
Sofro, A.; Oktaviarina, A.
2018-01-01
Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.
Trevisan, Caterina; Veronese, Nicola; Maggi, Stefania; Baggio, Giovannella; De Rui, Marina; Bolzetta, Francesco; Zambon, Sabina; Sartori, Leonardo; Perissinotto, Egle; Crepaldi, Gaetano; Manzato, Enzo; Sergi, Giuseppe
2016-06-01
Marital status has been associated with disability and mortality, but its potential role as a factor influencing frailty has yet to be thoroughly investigated. The analysis of gender-related differences in the relationship between marital status and frailty is another interesting matter that remains to be fully elucidated. The aim of our study was to examine the association between marital status and the incidence of frailty in a cohort of older men and women over a 4.4-year follow-up. A sample of 1887 subjects older than 65 years, enrolled under the Progetto Veneto Anziani (Pro.V.A.) and with no evidence of frailty at baseline, were grouped by marital status. The incidence of frailty after 4.4 years was measured as the presence of at least three of the Fried criteria. After the follow-up period, 414 (21.9%) new cases of frailty were identified. Multivariate logistic regression models demonstrated that male gender carried a higher risk of developing frailty among men who had never married (odds ratio [OR] = 3.84, 95% confidence interval [95% CI] = 2.76-5.35; p gender, widows had significantly lower odds of becoming frail than married women (OR = 0.77, 95% CI = 0.66-0.91, p = 0.002). The determinants of frailty more influenced by marital status were unintentional weight loss, low daily energy expenditure, and exhaustion. Marital status seems to significantly influence the onset of frailty, with some gender-specific differences. Unmarried men were at higher risk of frailty, while widowed women carried a lower risk of becoming frail than married women.
A shared frailty model for case-cohort samples: parent and offspring relations in an adoption study
Petersen, Liselotte; Sørensen, Thorkild I A; Andersen, Per Kragh
2010-01-01
of their biological and adoptive parents were collected with the purpose of studying the association of survival between the adoptee and his/her biological or adoptive parents. Motivated by this study, we explored how to make inference in a shared frailty model for case-cohort data. Our approach was to use inverse......The Danish adoption register contains data on the 12 301 Danish nonfamilial adoptions during 1924-1947. From that register a case-cohort sample was selected consisting of all case adoptees, that is those adoptees dying before age 70 years, and a random sample of 1683 adoptees. The survival data...... probability weighting to account for the sampling in a conditional, shared frailty Poisson model and to use the robust variance estimator proposed by Moger et al. (Statist. Med. 2008; 27:1062-1074).To explore the performance of the estimation procedure, a simulation study was conducted. We studied situations...
Lee, A H; Yau, K K
2001-01-01
To identify factors associated with hospital length of stay (LOS) and to model variations in LOS within Diagnosis Related Groups (DRGs). A proportional hazards frailty modelling approach is proposed that accounts for patient transfers and the inherent correlation of patients clustered within hospitals. The investigation is based on patient discharge data extracted for a group of obstetrical DRGs. Application of the frailty approach has highlighted several significant factors after adjustment for patient casemix and random hospital effects. In particular, patients admitted for childbirth with private medical insurance coverage have higher risk of prolonged hospitalization compared to public patients. The determination of pertinent factors provides important information to hospital management and clinicians in assessing the risk of prolonged hospitalization. The analysis also enables the comparison of inter-hospital variations across adjacent DRGs.
Regression models of reactor diagnostic signals
Vavrin, J.
1989-01-01
The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)
Variable importance in latent variable regression models
Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.
2014-01-01
The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable
Farcet, Anaïs; de Decker, Laure; Pauly, Vanessa; Rousseau, Frédérique; Bergman, Howard; Molines, Catherine; Retornaz, Frédérique
2016-01-01
Comprehensive Geriatric Assessment (CGA) is the gold standard to help oncologists select the best cancer treatment for their older patients. Some authors have suggested that the concept of frailty could be a more useful approach in this population. We investigated whether frailty markers are associated with treatment recommendations in an oncogeriatric clinic. This prospective study included 70 years and older patients with solid tumors and referred for an oncogeriatric assessment. The CGA included nine domains: autonomy, comorbidities, medication, cognition, nutrition, mood, neurosensory deficits, falls, and social status. Five frailty markers were assessed (nutrition, physical activity, energy, mobility, and strength). Patients were categorized as Frail (three or more frailty markers), pre-frail (one or two frailty markers), or not-frail (no frailty marker). Treatment recommendations were classified into two categories: standard treatment with and without any changes and supportive/palliative care. Multiple logistic regression models were used to analyze factors associated with treatment recommendations. 217 patients, mean age 83 years (± Standard deviation (SD) 5.3), were included. In the univariate analysis, number of frailty markers, grip strength, physical activity, mobility, nutrition, energy, autonomy, depression, Eastern Cooperative Oncology Group Scale of Performance Status (ECOG-PS), and falls were significantly associated with final treatment recommendations. In the multivariate analysis, the number of frailty markers and basic Activities of Daily Living (ADL) were significantly associated with final treatment recommendations (pmarkers are associated with final treatment recommendations in older cancer patients. Longitudinal studies are warranted to better determine their use in a geriatric oncology setting.
Roe, Lorna; Normand, Charles; Wren, Maev-Ann; Browne, John; O'Halloran, Aisling M
2017-09-05
To examine the impact of frailty on medical and social care utilisation among the Irish community-dwelling older population to inform strategies of integrated care for older people with complex needs. Participants aged ≥65 years from the Irish Longitudinal Study on Ageing (TILDA) representative of the Irish community-dwelling older population were analysed (n = 3507). The frailty index was used to examine patterns of utilisation across medical and social care services. Multivariate logistic and negative binomial regression models were employed to examine the impact of frailty on service utilisation outcomes after controlling for other factors. The prevalence of frailty and pre-frailty was 24% (95% CI: 23, 26%) and 45% (95% CI: 43, 47%) respectively. Frailty was a significant predictor of utilisation of most social care and medical care services after controlling for the main correlates of frailty and observed individual effects. Frailty predicts utilisation of many different types of healthcare services rendering it a useful risk stratification tool for targeting strategies of integrated care. The pattern of care is predominantly medical as few of the frail older population use social care prompting questions about sub-groups of the frail older population with unmet care needs.
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.
Regression Models for Market-Shares
Birch, Kristina; Olsen, Jørgen Kai; Tjur, Tue
2005-01-01
On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretat......On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put...... on the interpretation of the parameters in relation to models for the total sales based on discrete choice models.Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model....
Categorical regression dose-response modeling
The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...
Multistate event history analysis with frailty
Govert Bijwaard
2014-05-01
Full Text Available Background: In survival analysis a large literature using frailty models, or models with unobserved heterogeneity, exists. In the growing literature and modelling on multistate models, this issue is only in its infant phase. Ignoring frailty can, however, produce incorrect results. Objective: This paper presents how frailties can be incorporated into multistate models, with an emphasis on semi-Markov multistate models with a mixed proportional hazard structure. Methods: First, the aspects of frailty modeling in univariate (proportional hazard, Cox and multivariate event history models are addressed. The implications of choosing shared or correlated frailty is highlighted. The relevant differences with recurrent events data are covered next. Multistate models are event history models that can have both multivariate and recurrent events. Incorporating frailty in multistate models, therefore, brings all the previously addressed issues together. Assuming a discrete frailty distribution allows for a very general correlation structure among the transition hazards in a multistate model. Although some estimation procedures are covered the emphasis is on conceptual issues. Results: The importance of multistate frailty modeling is illustrated with data on labour market and migration dynamics of recent immigrants to the Netherlands.
A reliable measure of frailty for a community dwelling older population
Fletcher Astrid
2010-10-01
Full Text Available Abstract Background Frailty remains an elusive concept despite many efforts to define and measure it. The difficulty in translating the clinical profile of frail elderly people into a quantifiable assessment tool is due to the complex and heterogeneous nature of their health problems. Viewing frailty as a 'latent vulnerability' in older people this study aims to derive a model based measurement of frailty and examines its internal reliability in community dwelling elderly. Method The British Women's Heart and Health Study (BWHHS cohort of 4286 women aged 60-79 years from 23 towns in Britain provided 35 frailty indicators expressed as binary categorical variables. These indicators were corrected for measurement error and assigned relative weights in its association with frailty. Exploratory factor analysis (EFA reduced the data to a smaller number of factors and was subjected to confirmatory factor analysis (CFAwhich restricted the model by fitting the EFA-driven structure to observed data. Cox regression analysis compared the hazard ratios for adverse outcomes of the newly developed British frailty index (FI with a widely known FI. This process was replicated in the MRC Assessment study of older people, a larger cohort drawn from 106 general practices in Britain. Results Seven factors explained the association between frailty indicators: physical ability, cardiac symptoms/disease, respiratory symptoms/disease, physiological measures, psychological problems, co-morbidities and visual impairment. Based on existing concepts and statistical indices of fit, frailty was best described using a General Specific Model. The British FI would serve as a better population metric than the FI as it enables people with varying degrees of frailty to be better distinguished over a wider range of scores. The British FI was a better independent predictor of all-cause mortality, hospitalization and institutionalization than the FI in both cohorts. Conclusions
Nonhomogeneous Poisson process with nonparametric frailty
Slimacek, Vaclav; Lindqvist, Bo Henry
2016-01-01
The failure processes of heterogeneous repairable systems are often modeled by non-homogeneous Poisson processes. The common way to describe an unobserved heterogeneity between systems is to multiply the basic rate of occurrence of failures by a random variable (a so-called frailty) having a specified parametric distribution. Since the frailty is unobservable, the choice of its distribution is a problematic part of using these models, as are often the numerical computations needed in the estimation of these models. The main purpose of this paper is to develop a method for estimation of the parameters of a nonhomogeneous Poisson process with unobserved heterogeneity which does not require parametric assumptions about the heterogeneity and which avoids the frequently encountered numerical problems associated with the standard models for unobserved heterogeneity. The introduced method is illustrated on an example involving the power law process, and is compared to the standard gamma frailty model and to the classical model without unobserved heterogeneity. The derived results are confirmed in a simulation study which also reveals several not commonly known properties of the gamma frailty model and the classical model, and on a real life example. - Highlights: • A new method for estimation of a NHPP with frailty is introduced. • Introduced method does not require parametric assumptions about frailty. • The approach is illustrated on an example with the power law process. • The method is compared to the gamma frailty model and to the model without frailty.
Use of the shared frailty model to identify the determinants of child ...
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determinants would be prioritized in order to avoid an eventual misallocation of scarce resources. The analysis of relevant data showed that frailty effects were significant in .... software, R Development Core Team(2009). Analytical ... mortality may change because of changes in background variables operating through ...
Sarcopenia and frailty in chronic respiratory disease
Bone, Anna E; Hepgul, Nilay; Kon, Samantha
2017-01-01
Sarcopenia and frailty are geriatric syndromes characterized by multisystem decline, which are related to and reflected by markers of skeletal muscle dysfunction. In older people, sarcopenia and frailty have been used for risk stratification, to predict adverse outcomes and to prompt intervention aimed at preventing decline in those at greatest risk. In this review, we examine sarcopenia and frailty in the context of chronic respiratory disease, providing an overview of the common assessments tools and studies to date in the field. We contrast assessments of sarcopenia, which consider muscle mass and function, with assessments of frailty, which often additionally consider social, cognitive and psychological domains. Frailty is emerging as an important syndrome in respiratory disease, being strongly associated with poor outcome. We also unpick the relationship between sarcopenia, frailty and skeletal muscle dysfunction in chronic respiratory disease and reveal these as interlinked but distinct clinical phenotypes. Suggested areas for future work include the application of sarcopenia and frailty models to restrictive diseases and population-based samples, prospective prognostic assessments of sarcopenia and frailty in relation to common multidimensional indices, plus the investigation of exercise, nutritional and pharmacological strategies to prevent or treat sarcopenia and frailty in chronic respiratory disease. PMID:27923981
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
Tavakol, Najmeh; Kheiri, Soleiman; Sedehi, Morteza
2016-01-01
Time to donating blood plays a major role in a regular donor to becoming continues one. The aim of this study was to determine the effective factors on the interval between the blood donations. In a longitudinal study in 2008, 864 samples of first-time donors in Shahrekord Blood Transfusion Center, capital city of Chaharmahal and Bakhtiari Province, Iran were selected by a systematic sampling and were followed up for five years. Among these samples, a subset of 424 donors who had at least two successful blood donations were chosen for this study and the time intervals between their donations were measured as response variable. Sex, body weight, age, marital status, education, stay and job were recorded as independent variables. Data analysis was performed based on log-normal hazard model with gamma correlated frailty. In this model, the frailties are sum of two independent components assumed a gamma distribution. The analysis was done via Bayesian approach using Markov Chain Monte Carlo algorithm by OpenBUGS. Convergence was checked via Gelman-Rubin criteria using BOA program in R. Age, job and education were significant on chance to donate blood (Pdonation for the higher-aged donors, clericals, workers, free job, students and educated donors were higher and in return, time intervals between their blood donations were shorter. Due to the significance effect of some variables in the log-normal correlated frailty model, it is necessary to plan educational and cultural program to encourage the people with longer inter-donation intervals to donate more frequently.
Dietary Pattern Associated with Frailty: Results from Nutrition and Health Survey in Taiwan.
Lo, Yen-Li; Hsieh, Yao-Te; Hsu, Li-Lin; Chuang, Shao-Yuan; Chang, Hsing-Yi; Hsu, Chih-Cheng; Chen, Ching-Yu; Pan, Wen-Harn
2017-09-01
To investigate whether dietary patterns are associated with frailty phenotypes in an elderly Taiwanese population. Cross-sectional. Nutrition and Health Survey in Taiwan (NAHSIT), 2014-2016. Noninstitutionalized Taiwanese nationals aged 65 years and older enrolled in the NAHSIT (N = 923). Dietary intake was assessed using a 79-item food-frequency questionnaire (FFQ). Presence of 5 frailty phenotypes was determined using modified Fried criteria and are summed into a frailty score. Using data from the NAHSIT (2014-15), reduced rank regression was used to find a dietary pattern that explained maximal degree of variation of the frailty scores. Logistic regression models were used to estimate the association between frailty and dietary pattern. The findings were validated with data from 2016. The derived dietary pattern was characterized with a high consumption of fruit, nuts and seeds, tea, vegetables, whole grains, shellfish, milk, and fish. The prevalence of frailty was 7.8% and of prefrailty was 50.8%, defined using the modified Fried criteria. Using data from the NAHSIT (2014-15), the dietary pattern score showed an inverse dose-response relationship with prevalence of frailty and pre-frailty. Individuals in the second dietary pattern tertile were one-third as likely to be frail as those in the first tertile (adjusted odds ratio (aOR) = 0.32, 95% confidence interval (CI) = 0.12-0.85), and those in the third tertile were 4% as likely to be frail as those in the first tertile (aOR = 0.04, 95% CI = 0.01-0.18). The dietary pattern score estimated using FFQ data from the NAHSIT 2016 was also significantly and inversely associated with frailty. Individuals with a dietary pattern with more phytonutrient-rich plant foods, tea, omega-3-rich deep-sea fish, and other protein-rich foods such as shellfish and milk had a reduced prevalence of frailty. Further research is necessary to confirm these findings and investigate whether related dietary interventions can reduce frailty
Regression Models For Multivariate Count Data.
Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei
2017-01-01
Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.
Szlejf, Claudia; Parra-Rodríguez, Lorena; Rosas-Carrasco, Oscar
2017-08-01
The aims of this study were to determine the prevalence of osteosarcopenic obesity (OSO) and to investigate its association with frailty and physical performance in Mexican community-dwelling middle-aged and older women. Cross-sectional analysis of a prospective cohort. The FraDySMex study, a 2-round evaluation of community-dwelling adults from 2 municipalities in Mexico City. Participants were 434 women aged 50 years or older, living in the designated area in Mexico City. Body composition was measured with dual-energy X-ray absorptiometry and OSO was defined by the coexistence of sarcopenia, osteopenia, or osteoporosis and obesity. Information regarding demographic characteristics; comorbidities; mental status; nutritional status; and history of falls, fractures, and hospitalization was obtained from questionnaires. Objective measurements of muscle strength and function were grip strength using a hand dynamometer, 6-meter gait speed using a GAIT Rite instrumented walkway, and lower extremity functioning measured by the Short Physical Performance Battery (SPPB). Frailty was assessed using the Frailty Phenotype (Fried criteria), the Gerontopole Frailty Screening Tool (GFST), and the FRAIL scale, to build 3 logistic regression models. The prevalence of OSO was 19% (n = 81). Frailty (according to the Frailty Phenotype and the GFST) and poor physical performance measured by the SPPB were independently associated with OSO, controlled by age. In the logistic regression model assessing frailty with the Frailty Phenotype, the odds ratio (95% confidence interval) for frailty was 4.86 (2.47-9.55), and for poor physical performance it was 2.11 (1.15-3.89). In the model assessing frailty with the GFST, it was 2.12 (1.10-4.11), and for poor physical performance it was 2.15 (1.18-3.92). Finally, in the model with the FRAIL scale, it was 1.69 (0.85-3.36) for frailty and 2.29 (1.27-4.15) for poor physical performance. OSO is a frequent condition in middle-aged and older women
Mixed-effects regression models in linguistics
Heylen, Kris; Geeraerts, Dirk
2018-01-01
When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addres...
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
Influence diagnostics in meta-regression model.
Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua
2017-09-01
This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.
AIRLINE ACTIVITY FORECASTING BY REGRESSION MODELS
Н. Білак
2012-04-01
Full Text Available Proposed linear and nonlinear regression models, which take into account the equation of trend and seasonality indices for the analysis and restore the volume of passenger traffic over the past period of time and its prediction for future years, as well as the algorithm of formation of these models based on statistical analysis over the years. The desired model is the first step for the synthesis of more complex models, which will enable forecasting of passenger (income level airline with the highest accuracy and time urgency.
Modeling oil production based on symbolic regression
Yang, Guangfei; Li, Xianneng; Wang, Jianliang; Lian, Lian; Ma, Tieju
2015-01-01
Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans. -- Highlights: •A data-driven approach has been shown to be effective at modeling the oil production. •The Hubbert model could be discovered automatically from data. •The peak of world oil production is predicted to appear in 2021. •The decline rate after peak is half of the increase rate before peak. •Oil production projected to decline 4% post-peak
Elghafghuf, Adel; Dufour, Simon; Reyher, Kristen; Dohoo, Ian; Stryhn, Henrik
2014-12-01
Mastitis is a complex disease affecting dairy cows and is considered to be the most costly disease of dairy herds. The hazard of mastitis is a function of many factors, both managerial and environmental, making its control a difficult issue to milk producers. Observational studies of clinical mastitis (CM) often generate datasets with a number of characteristics which influence the analysis of those data: the outcome of interest may be the time to occurrence of a case of mastitis, predictors may change over time (time-dependent predictors), the effects of factors may change over time (time-dependent effects), there are usually multiple hierarchical levels, and datasets may be very large. Analysis of such data often requires expansion of the data into the counting-process format - leading to larger datasets - thus complicating the analysis and requiring excessive computing time. In this study, a nested frailty Cox model with time-dependent predictors and effects was applied to Canadian Bovine Mastitis Research Network data in which 10,831 lactations of 8035 cows from 69 herds were followed through lactation until the first occurrence of CM. The model was fit to the data as a Poisson model with nested normally distributed random effects at the cow and herd levels. Risk factors associated with the hazard of CM during the lactation were identified, such as parity, calving season, herd somatic cell score, pasture access, fore-stripping, and proportion of treated cases of CM in a herd. The analysis showed that most of the predictors had a strong effect early in lactation and also demonstrated substantial variation in the baseline hazard among cows and between herds. A small simulation study for a setting similar to the real data was conducted to evaluate the Poisson maximum likelihood estimation approach with both Gaussian quadrature method and Laplace approximation. Further, the performance of the two methods was compared with the performance of a widely used estimation
Geographically weighted regression model on poverty indicator
Slamet, I.; Nugroho, N. F. T. A.; Muslich
2017-12-01
In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.
Prevalence of frailty in middle-aged and older community-dwelling Europeans living in 10 countries.
Santos-Eggimann, Brigitte; Cuénoud, Patrick; Spagnoli, Jacques; Junod, Julien
2009-06-01
Frailty is an indicator of health status in old age. Its frequency has been described mainly for North America; comparable data from other countries are lacking. Here we report on the prevalence of frailty in 10 European countries included in a population-based survey. Cross-sectional analysis of 18,227 randomly selected community-dwelling individuals 50 years of age and older, enrolled in the Survey of Health, Aging and Retirement in Europe (SHARE) in 2004. Complete data for assessing a frailty phenotype (exhaustion, shrinking, weakness, slowness, and low physical activity) were available for 16,584 participants. Prevalences of frailty and prefrailty were estimated for individuals 50-64 years and 65 years of age and older from each country. The latter group was analyzed further after excluding disabled individuals. We estimated country effects in this subset using multivariate logistic regression models, controlling first for age, gender, and then demographics and education. The proportion of frailty (three to five criteria) or prefrailty (one to two criteria) was higher in southern than in northern Europe. International differences in the prevalences of frailty and prefrailty for 65 years and older group persisted after excluding the disabled. Demographic characteristics did not account for international differences; however, education was associated with frailty. Controlling for education, age and gender diminished the effects of residing in Italy and Spain. A higher prevalence of frailty in southern countries is consistent with previous findings of a north-south gradient for other health indicators in SHARE. Our data suggest that socioeconomic factors like education contribute to these differences in frailty and prefrailty.
Adaptive regression for modeling nonlinear relationships
Knafl, George J
2016-01-01
This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the s...
Bayesian Inference of a Multivariate Regression Model
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
General regression and representation model for classification.
Jianjun Qian
Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.
Confidence bands for inverse regression models
Birke, Melanie; Bissantz, Nicolai; Holzmann, Hajo
2010-01-01
We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two periodic functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt (1973 Ann. Stat. 1 1071–95) we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap and prove consistency of the bootstrap procedure. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract
Multitask Quantile Regression under the Transnormal Model.
Fan, Jianqing; Xue, Lingzhou; Zou, Hui
2016-01-01
We consider estimating multi-task quantile regression under the transnormal model, with focus on high-dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based ℓ 1 penalization with positive definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of alternating direction method of multipliers, nearest correlation matrix projection is introduced that inherits sampling properties of the unprojected one. Our work combines strengths of quantile regression and rank-based covariance regularization to simultaneously deal with nonlinearity and nonnormality for high-dimensional regression. Furthermore, the proposed method strikes a good balance between robustness and efficiency, achieves the "oracle"-like convergence rate, and provides the provable prediction interval under the high-dimensional setting. The finite-sample performance of the proposed method is also examined. The performance of our proposed rank-based method is demonstrated in a real application to analyze the protein mass spectroscopy data.
Crime Modeling using Spatial Regression Approach
Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.
2018-01-01
Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.
Pérez-Zepeda, Mario Ulises; Cárdenas-Cárdenas, Eduardo; Cesari, Matteo; Navarrete-Reyes, Ana Patricia; Gutiérrez-Robledo, Luis Miguel
2016-01-01
Purpose Understanding how the convergence between chronic and complex diseases—such as cancer—and emerging conditions of older adults—such as frailty—takes place would help in halting the path that leads to disability in this age group. The objective of this manuscript is to describe the association between a past medical history of cancer and frailty in Mexican older adults. Methods This is a nested in cohort case-control study of the Mexican Health and Aging Study. Frailty was categorized by developing a 55-item frailty index that was also used to define cases in two ways: incident frailty (incident >0.25 frailty index score) and worsening frailty (negative residuals from a regression between 2001 and 2012 frailty index scores). Exposition was defined as self-report of cancer between 2001 and 2012. Older adults with a cancer history were further divided into recently diagnosed (10 years from the initial diagnosis). Odds ratios were estimated by fitting a logistic regression adjusted for confounding variables. Results Out of a total of 8022 older adults with a mean age of 70.6 years, the prevalence of a past medical history of cancer was 3.6 % (n = 288). Among these participants, 45.1 % had been diagnosed with cancer more than 10 years previously. A higher risk of incident frailty compared to controls [odds ratio (OR) 1.53 (95 % confidence interval (CI) 1.04–2.26, p = 0.03); adjusted model OR 1.74 (95 % CI 1.15–2.61, p = 0.008)] was found in the group with a recent cancer diagnosis. Also, an inverse association between a remote cancer diagnosis and worsening frailty was found [OR = 0.56 (95 % CI 0.39–0.8), p = 0.002; adjusted model OR 0.61 (95 % CI 0.38–0.99, p = 0.046)]. Conclusions Cancer is associated with a higher frailty index, with a potential relevant role of the time that has elapsed since the cancer diagnosis. Implications for cancer survivors Cancer survivors may be more likely to develop frailty or worsening of the health status at an
Yu, Ruby; Wang, Dan; Leung, Jason; Lau, Kevin; Kwok, Timothy; Woo, Jean
2018-06-01
To examine whether neighborhood green space was related to frailty risk longitudinally and to examine the relative contributions of green space, physical activity, and individual health conditions to the frailty transitions. Four thousand community-dwelling Chinese adults aged ≥65 years participating in the Mr. and Ms. Os (Hong Kong) study in 2001-2003 were followed up for 2 years. The percentage of green space within a 300-meter radial buffer around the participants' place of residence was derived for each participant at baseline based on the normalized difference vegetation index. Frailty status was classified according to the Fried criteria at baseline and after 2 years. Ordinal logistic regression and path analysis were used to examine associations between green space and the frailty transitions, adjusting for demographics, socioeconomic status, lifestyle factors, health conditions, and baseline frailty status. At baseline, 53.5% of the participants met the criterion for robust, 41.5% were classified as prefrailty, and 5.0% were frail. After 2 years, 3240 participants completed all the measurements. Among these, 18.6% of prefrail or frail participants improved, 66% remained in their frailty state, and 26.8% of robust or prefrail participants progressed in frailty status. In multivariable models, the frailty status of participants living in neighborhoods with more than 34.1% green space (the highest quartile) at baseline was more likely to improve at the 2-year follow-up than it was for those living in neighborhoods with 0 to 4.5% (the lowest quartile) [odds ratio (OR): 1.29, 95% confidence interval (CI): 1.04-1.60; P for trend: 0.022]. When men and women were analyzed separately, the association between green space and frailty remained significant in men (OR: 1.40, 95% CI: 1.03-1.90) but not in women. Path analysis showed that green space directly affects frailty transitions (β = 0.041, P space on the 2-year frailty transitions is comparable to those
AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION
Krzyśko, Mirosław; Smaga, Łukasz
2017-01-01
In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...
Entrepreneurial intention modeling using hierarchical multiple regression
Marina Jeger
2014-12-01
Full Text Available The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.
An Additive-Multiplicative Cox-Aalen Regression Model
Scheike, Thomas H.; Zhang, Mei-Jie
2002-01-01
Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...
Variable selection and model choice in geoadditive regression models.
Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard
2009-06-01
Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.
Frailty and Depression in Older Adults
Brown, Patrick J; Roose, Steven P; Fieo, Robert
2014-01-01
of death was obtained, providing a maximum survival time of 11.08 years (initial evaluation took place between 1988 and 1991). RESULTS: Depressed elders showed greater baseline impairments in each frailty characteristic (gait speed, grip strength, physical activity levels, and fatigue). Simultaneous models......OBJECTIVE: To identify salient characteristics of frailty that increase risk of death in depressed elders. METHODS: Data were from the Nordic Research on Ageing Study from research sites in Denmark, Sweden, and Finland. Participants were 1,027 adults aged 75 years (436 men and 591 women). Time...... including all four frailty characteristics showed slow gait speed (hazard ratio: 1.84; 95% confidence interval: 1.05-3.21) and fatigue (hazard ratio: 1.94; 95% confidence interval: 1.11-3.40) associated with faster progression to death in depressed women; none of the frailty characteristics...
Hierarchical regression analysis in structural Equation Modeling
de Jong, P.F.
1999-01-01
In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main
Poisson Mixture Regression Models for Heart Disease Prediction.
Mufudza, Chipo; Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.
Toward Smart Footwear to Track Frailty Phenotypes—Using Propulsion Performance to Determine Frailty
Hadi Rahemi
2018-06-01
Full Text Available Frailty assessment is dependent on the availability of trained personnel and it is currently limited to clinic and supervised setting. The growing aging population has made it necessary to find phenotypes of frailty that can be measured in an unsupervised setting for translational application in continuous, remote, and in-place monitoring during daily living activity, such as walking. We analyzed gait performance of 161 older adults using a shin-worn inertial sensor to investigate the feasibility of developing a foot-worn sensor to assess frailty. Sensor-derived gait parameters were extracted and modeled to distinguish different frailty stages, including non-frail, pre-frail, and frail, as determined by Fried Criteria. An artificial neural network model was implemented to evaluate the accuracy of an algorithm using a proposed set of gait parameters in predicting frailty stages. Changes in discriminating power was compared between sensor data extracted from the left and right shin sensor. The aim was to investigate the feasibility of developing a foot-worn sensor to assess frailty. The results yielded a highly accurate model in predicting frailty stages, irrespective of sensor location. The independent predictors of frailty stages were propulsion duration and acceleration, heel-off and toe-off speed, mid stance and mid swing speed, and speed norm. The proposed model enables discriminating different frailty stages with area under curve ranging between 83.2–95.8%. Furthermore, results from the neural network suggest the potential of developing a single-shin worn sensor that would be ideal for unsupervised application and footwear integration for continuous monitoring during walking.
Modeling maximum daily temperature using a varying coefficient regression model
Han Li; Xinwei Deng; Dong-Yum Kim; Eric P. Smith
2014-01-01
Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature...
Yaw-Wen Chang
Full Text Available BACKGROUND: The purpose of this study was to identify the incidence of frailty and to investigate the relationship between frailty status and health-related quality of life (HRQoL in the community-dwelling elderly population who utilize preventive health services. METHODS: People aged 65 years and older who visited a medical center in Taipei City from March to August in 2011 for an annual routine check-up provided by the National Health Insurance were eligible. A total of 374 eligible elderly adults without cognitive impairment had a mean age of 74.6±6.3 years. Frailty status was determined according to the Fried frailty criteria. HRQoL was measured with Short Form-36 (SF-36. Multiple regression analyses examined the relationship between frailty status and the two summary scales of SF-36. Models were adjusted for the participants' sociodemographic and health status. RESULTS: After adjusting for sociodemographic and health-related covariables, frailty was found to be more significantly associated (p<0.001 with lower scores on both physical and mental health-related quality of life summary scales compared with robustness. For the frailty phenotypes, slowness represented the major contributing factor in the physical component scale of SF-36, and exhaustion was the primary contributing factor in the mental component scale. CONCLUSION: The status of frailty is closely associated with HRQoL in elderly Taiwanese preventive health service users. The impacts of frailty phenotypes on physical and mental aspects of HRQoL differ.
Introduction to the use of regression models in epidemiology.
Bender, Ralf
2009-01-01
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
Model performance analysis and model validation in logistic regression
Rosa Arboretti Giancristofaro
2007-10-01
Full Text Available In this paper a new model validation procedure for a logistic regression model is presented. At first, we illustrate a brief review of different techniques of model validation. Next, we define a number of properties required for a model to be considered "good", and a number of quantitative performance measures. Lastly, we describe a methodology for the assessment of the performance of a given model by using an example taken from a management study.
Logistic Regression Modeling of Diminishing Manufacturing Sources for Integrated Circuits
Gravier, Michael
1999-01-01
.... The research identified logistic regression as a powerful tool for analysis of DMSMS and further developed twenty models attempting to identify the "best" way to model and predict DMSMS using logistic regression...
Shimizu, Kiyoharu; Sadatomo, Takashi; Hara, Takeshi; Onishi, Shumpei; Yuki, Kiyoshi; Kurisu, Kaoru
2018-05-17
The present study aimed to clarify the relationship between frailty and prognosis of patients with chronic subdural hematoma. This retrospective study involved 211 patients aged ≥65 years with chronic subdural hematoma, who underwent surgery at Higashihiroshima Medical Center, Hiroshima, Japan, between July 2011 and May 2017. The study outcome was the patient's modified Rankin Scale score at 3 months after surgery. A logistic regression analysis was carried out to analyze factors that influenced the outcome. Chronic subdural hematoma patients with frailty had a poorer prognosis than those without (median modified Rankin Scale: 4 and 2, P < 0.001; proportions of patients discharged to home: 35% and 91%, P < 0.001, respectively). After adjusting for patients' background, the patients' modified Rankin Scale scores at 3 months after surgery were found to be associated with age, controlling nutritional status score and recurrence, but not with frailty. However, receiver operating characteristic curves of the model with the Clinical Frailty Scale were more accurately correlated with prognosis than those of the model without this scale (area under the curve 0.98, 95% confidence interval 0.96-0.99; and 0.87, 95% confidence interval 0.82-0.91, respectively.) CONCLUSIONS: Chronic subdural hematoma patients with frailty had poorer prognosis than those without. The evaluation of the presence of frailty on admission can be an important factor in the prediction of the prognosis of chronic subdural hematoma patients. Geriatr Gerontol Int 2018; ••: ••-••. © 2018 Japan Geriatrics Society.
Physical Activity across Frailty Phenotypes in Females with Parkinson’s Disease
Kaitlyn P. Roland
2012-01-01
Full Text Available Females with Parkinson’s disease (PD are vulnerable to frailty. PD eventually leads to decreased physical activity, an indicator of frailty. We speculate PD results in frailty through reduced physical activity. Objective. Determine the contribution of physical activity on frailty in PD (n=15, 65 ± 9 years and non-PD (n=15, 73 ± 14 years females. Methods. Frailty phenotype (nonfrail/prefrail/frail was categorized and 8 hours of physical activity was measured using accelerometer, global positioning system, and self-report. Two-way ANCOVA (age as covariate was used to compare physical activity between disease and frailty phenotypes. Spearman correlation assessed relationships, and linear regression determined associations with frailty. Results. Nonfrail recorded more physical activity (intensity, counts, self-report compared with frail. Self-reported physical activity was greater in PD than non-PD. In non-PD, step counts, light physical activity time, sedentary time, and self-reported physical activity were related to frailty (R=0.91. In PD, only carbidopa-levodopa dose was related to frailty (r=0.61. Conclusion. Physical activity influences frailty in females without PD. In PD females, disease management may be a better indicator of frailty than physical activity. Further investigation into how PD associated factors contribute to frailty is warranted.
Social support, stressors, and frailty among older Mexican American adults.
Peek, M Kristen; Howrey, Bret T; Ternent, Rafael Samper; Ray, Laura A; Ottenbacher, Kenneth J
2012-11-01
There is little research on the effects of stressors and social support on frailty. Older Mexican Americans, in particular, are at higher risk of medical conditions, such as diabetes, that could contribute to frailty. Given that the Mexican American population is rapidly growing in the United States, it is important to determine whether there are modifiable social factors related to frailty in this older group. To address the influence of social support and stressors on frailty among older Mexican Americans, we utilized five waves of the Hispanic Established Populations for the Epidemiologic Study of the Elderly (Hispanic EPESE) to examine the impact of stressors and social support on frailty over a 12-year period. Using a modified version of the Fried and Walston Frailty Index, we estimated the effects of social support and stressors on frailty over time using trajectory modeling (SAS 9.2, PROC TRAJ). We first grouped respondents according to one of three trajectories: low, progressive moderate, and progressive high frailty. Second, we found that the effects of stressors and social support on frailty varied by trajectory and by type of stressor. Health-related stressors and financial strain were related to increases in frailty over time, whereas social support was related to less-steep increases in frailty. Frailty has been hypothesized to reflect age-related physiological vulnerability to stressors, and the analyses presented indicate partial support for this hypothesis in an older sample of Mexican Americans. Future research needs to incorporate measures of stressors and social support in examining those who become frail, especially in minority populations.
Vaeth, Michael; Skovlund, Eva
2004-06-15
For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.
The MIDAS Touch: Mixed Data Sampling Regression Models
Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen
2004-01-01
We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Technically speaking MIDAS models specify conditional expectations as a distributed lag of regressors recorded at some higher sampling frequencies. We examine the asymptotic properties of MIDAS regression estimation and compare it with traditional distributed lag models. MIDAS regressions have wide applicability in macroeconomics and ï¿½nance.
Model selection in kernel ridge regression
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
Haran, John P; Bucci, Vanni; Dutta, Protiva; Ward, Doyle; McCormick, Beth
2018-01-01
The microbiome from nursing home (NH) residents is marked by a loss in diversity that is associated with increased frailty. Our objective was to explore the associations of NH environment, frailty, nutritional status and residents' age to microbiome composition and potential metabolic function. We conducted a prospective longitudinal cohort study of 23 residents, 65 years or older, from one NH that had four floors: two separate medical intensive floors and two floors with active elders. Residents were assessed using the mini nutritional assessment tool and clinical frailty scale. Bacterial composition and metabolic potential of residents' stool samples was determined by metagenomic sequencing. We performed traditional unsupervised correspondence analysis and linear mixed effect modelling regression to assess the bacteria and functional pathways significantly affected by these covariates.Results/Key findings. NH resident microbiomes demonstrated temporal stability (PERMANOVA P=0.001) and differing dysbiotic associations with increasing age, frailty and malnutrition scores. As residents aged, the abundance of microbiota-encoded genes and pathways related to essential amino acid, nitrogenous base and vitamin B production declined. With increasing frailty, residents had lower abundances of butyrate-producing organisms, which are associated with increased health and higher abundances of known dysbiotic species. As residents became malnourished, butyrate-producing organisms declined and dysbiotic bacterial species increased. Finally, the microbiome of residents living in proximity shared similar species and, as demonstrated for Escherichia coli, similar strains. These findings support the conclusion that a signature 'NH' microbiota may exist that is affected by the residents' age, frailty, nutritional status and physical location.
Mixture of Regression Models with Single-Index
Xiang, Sijia; Yao, Weixin
2016-01-01
In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...
Linear regression crash prediction models : issues and proposed solutions.
2010-05-01
The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...
Model-based Quantile Regression for Discrete Data
Padellini, Tullia; Rue, Haavard
2018-01-01
Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite
Castrejón-Pérez, Roberto Carlos; Gutiérrez-Robledo, Luis Miguel; Cesari, Matteo; Pérez-Zepeda, Mario Ulises
2017-06-01
Chronic diseases are frequent in older adults, particularly hypertension and diabetes. The relationship between frailty and these two conditions is still unclear. The aim of the present analyses was to explore the association between frailty with diabetes and hypertension in Mexican older adults. Analyses of the Mexican Health and Nutrition Survey, a cross-sectional survey, are presented. Data on diabetes and hypertension were acquired along with associated conditions (time since diagnosis, pharmacological treatment, among others). A 36-item frailty index was constructed and rescaled to z-values (individual scores minus population mean divided by one standard deviation). Multiple linear regression models were carried out, adjusted for age and sex. From 7164 older adults, 54.8% were women, and their mean age was 70.6 years with a mean frailty index score of 0.175. The prevalence of diabetes was of 22.2%, and 37.3% for hypertension. An independent association between diabetes, hypertension or both conditions (coefficients 0.28, 0.4 and 0.63, respectively, P diabetic complication was significantly associated with frailty with a coefficient of 0.55 (95% CI 0.45-0.65, P Diabetes and hypertension are associated with frailty. In addition, an incremental association was found when both conditions were present or with worse associated features (any complication, more time since diagnosis). Frailty should be of particular concern in populations with a high prevalence of these conditions. Geriatr Gerontol Int 2017; 17: 925-930. © 2016 Japan Geriatrics Society.
Forecasting Ebola with a regression transmission model
Jason Asher
2018-03-01
Full Text Available We describe a relatively simple stochastic model of Ebola transmission that was used to produce forecasts with the lowest mean absolute error among Ebola Forecasting Challenge participants. The model enabled prediction of peak incidence, the timing of this peak, and final size of the outbreak. The underlying discrete-time compartmental model used a time-varying reproductive rate modeled as a multiplicative random walk driven by the number of infectious individuals. This structure generalizes traditional Susceptible-Infected-Recovered (SIR disease modeling approaches and allows for the flexible consideration of outbreaks with complex trajectories of disease dynamics. Keywords: Ebola, Forecasting, Mathematical modeling, Bayesian inference
Frailty Intervention Trial (FIT
Lockwood Keri
2008-10-01
Full Text Available Abstract Background Frailty is a term commonly used to describe the condition of an older person who has chronic health problems, has lost functional abilities and is likely to deteriorate further. However, despite its common use, only a small number of studies have attempted to define the syndrome of frailty and measure its prevalence. The criteria Fried and colleagues used to define the frailty syndrome will be used in this study (i.e. weight loss, fatigue, decreased grip strength, slow gait speed, and low physical activity. Previous studies have shown that clinical outcomes for frail older people can be improved using multi-factorial interventions such as comprehensive geriatric assessment, and single interventions such as exercise programs or nutritional supplementation, but no interventions have been developed to specifically reverse the syndrome of frailty. We have developed a multidisciplinary intervention that specifically targets frailty as defined by Fried et al. We aim to establish the effects of this intervention on frailty, mobility, hospitalisation and institutionalisation in frail older people. Methods and Design A single centre randomised controlled trial comparing a multidisciplinary intervention with usual care. The intervention will target identified characteristics of frailty, functional limitations, nutritional status, falls risk, psychological issues and management of chronic health conditions. Two hundred and thirty people aged 70 and over who meet the Fried definition of frailty will be recruited from clients of the aged care service of a metropolitan hospital. Participants will be followed for a 12-month period. Discussion This research is an important step in the examination of specifically targeted frailty interventions. This project will assess whether an intervention specifically targeting frailty can be implemented, and whether it is effective when compared to usual care. If successful, the study will establish a
Forecasting Ebola with a regression transmission model
Asher, Jason
2017-01-01
We describe a relatively simple stochastic model of Ebola transmission that was used to produce forecasts with the lowest mean absolute error among Ebola Forecasting Challenge participants. The model enabled prediction of peak incidence, the timing of this peak, and final size of the outbreak. The underlying discrete-time compartmental model used a time-varying reproductive rate modeled as a multiplicative random walk driven by the number of infectious individuals. This structure generalizes ...
Model Selection in Kernel Ridge Regression
Exterkate, Peter
Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...
Jafri, Y.Z.; Kamal, L.
2007-01-01
Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)
Corporate prediction models, ratios or regression analysis?
Bijnen, E.J.; Wijn, M.F.C.M.
1994-01-01
The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in
STREAMFLOW AND WATER QUALITY REGRESSION MODELING ...
... downstream Obigbo station show: consistent time-trends in degree of contamination; linear and non-linear relationships for water quality models against total dissolved solids (TDS), total suspended sediment (TSS), chloride, pH and sulphate; and non-linear relationship for streamflow and water quality transport models.
Association between frailty and delirium in older adult patients discharged from hospital
Verloo H
2016-01-01
Full Text Available Henk Verloo,1 Céline Goulet,2 Diane Morin,3,4 Armin von Gunten51Department Nursing Sciences, University of Applied Sciences, Lausanne, Switzerland; 2Faculty of Nursing Science, University of Montreal, Montreal, QC, Canada; 3Institut Universitaire de Formation et Recherche en Soins (IUFRS, Faculty of Biology and Medicine, University of Lausanne, Lausanne University Hospital, Lausanne, Switzerland; 4Faculty of Nursing Science, Université Laval, Québec, Canada; 5Department of Psychiatry, Service Universitaire de Psychiatrie de l’Age Avancé (SUPAA, Lausanne University Hospital, Prilly, SwitzerlandBackground: Delirium and frailty – both potentially reversible geriatric syndromes – are seldom studied together, although they often occur jointly in older patients discharged from hospitals. This study aimed to explore the relationship between delirium and frailty in older adults discharged from hospitals.Methods: Of the 221 patients aged >65 years, who were invited to participate, only 114 gave their consent to participate in this study. Delirium was assessed using the confusion assessment method, in which patients were classified dichotomously as delirious or nondelirious according to its algorithm. Frailty was assessed using the Edmonton Frailty Scale, which classifies patients dichotomously as frail or nonfrail. In addition to the sociodemographic characteristics, covariates such as scores from the Mini-Mental State Examination, Instrumental Activities of Daily Living scale, and Cumulative Illness Rating Scale for Geriatrics and details regarding polymedication were collected. A multidimensional linear regression model was used for analysis.Results: Almost 20% of participants had delirium (n=22, and 76.3% were classified as frail (n=87; 31.5% of the variance in the delirium score was explained by frailty (R2=0.315. Age; polymedication; scores of the Confusion Assessment Method (CAM, instrumental activities of daily living, and Cumulative
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami
2017-06-01
A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
Multiattribute shopping models and ridge regression analysis
Timmermans, H.J.P.
1981-01-01
Policy decisions regarding retailing facilities essentially involve multiple attributes of shopping centres. If mathematical shopping models are to contribute to these decision processes, their structure should reflect the multiattribute character of retailing planning. Examination of existing
Linear Regression Models for Estimating True Subsurface ...
47
The objective is to minimize the processing time and computer memory required. 10 to carry out inversion .... to the mainland by two long bridges. .... term. In this approach, the model converges when the squared sum of the differences. 143.
Moderation analysis using a two-level regression model.
Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott
2014-10-01
Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.
Alternative regression models to assess increase in childhood BMI
Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M
2008-01-01
Abstract Background Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 childre...
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Poisson Mixture Regression Models for Heart Disease Prediction
Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611
[Impact of frailty over the functional state of hospitalized elderly].
García-Cruz, Juan Carlos; García-Peña, Carmen
2016-01-01
Frailty in elderly results from impaired physiological reserve in multiple systems. Establishing if frail elderly inpatients develop more functional impairment at discharge, will allow the development of strategies for preventing or limiting the deterioration in this vulnerable group. Prospective cohort in 133 elderly inpatients. At admission, frailty, functional status, comorbidity and comprehensive geriatric evaluation were determined. The main outcome was functional state at hospital discharge. 64 patients presented frailty (48.1%) and 69 did not present that state (51.9%), with a mean age of 73 and 68 years, respectively. Mean decrement in functional state at discharge was -8.06 % (IC 95 % -10.38 to -5.74), from 97.97 % to 89.91 % (p model, frailty (beta -14.73, IC 95 % -19.39 to -10.07, p decrement. Frailty independently predicts functional impairment at hospital discharge.
A test for the parameters of multiple linear regression models ...
A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...
Mixed Frequency Data Sampling Regression Models: The R Package midasr
Eric Ghysels
2016-08-01
Full Text Available When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002. In this article we define a general autoregressive MIDAS regression model with multiple variables of different frequencies and show how it can be specified using the familiar R formula interface and estimated using various optimization methods chosen by the researcher. We discuss how to check the validity of the estimated model both in terms of numerical convergence and statistical adequacy of a chosen regression specification, how to perform model selection based on a information criterion, how to assess forecasting accuracy of the MIDAS regression model and how to obtain a forecast aggregation of different MIDAS regression models. We illustrate the capabilities of the package with a simulated MIDAS regression model and give two empirical examples of application of MIDAS regression.
Impact of multicollinearity on small sample hydrologic regression models
Kroll, Charles N.; Song, Peter
2013-06-01
Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.
The linear transformation model with frailties for the analysis of item response times.
Wang, Chun; Chang, Hua-Hua; Douglas, Jeffrey A
2013-02-01
The item response times (RTs) collected from computerized testing represent an underutilized source of information about items and examinees. In addition to knowing the examinees' responses to each item, we can investigate the amount of time examinees spend on each item. In this paper, we propose a semi-parametric model for RTs, the linear transformation model with a latent speed covariate, which combines the flexibility of non-parametric modelling and the brevity as well as interpretability of parametric modelling. In this new model, the RTs, after some non-parametric monotone transformation, become a linear model with latent speed as covariate plus an error term. The distribution of the error term implicitly defines the relationship between the RT and examinees' latent speeds; whereas the non-parametric transformation is able to describe various shapes of RT distributions. The linear transformation model represents a rich family of models that includes the Cox proportional hazards model, the Box-Cox normal model, and many other models as special cases. This new model is embedded in a hierarchical framework so that both RTs and responses are modelled simultaneously. A two-stage estimation method is proposed. In the first stage, the Markov chain Monte Carlo method is employed to estimate the parametric part of the model. In the second stage, an estimating equation method with a recursive algorithm is adopted to estimate the non-parametric transformation. Applicability of the new model is demonstrated with a simulation study and a real data application. Finally, methods to evaluate the model fit are suggested. © 2012 The British Psychological Society.
Prospective association between added sugars and frailty in older adults.
Laclaustra, Martin; Rodriguez-Artalejo, Fernando; Guallar-Castillon, Pilar; Banegas, Jose R; Graciani, Auxiliadora; Garcia-Esquinas, Esther; Ordovas, Jose; Lopez-Garcia, Esther
2018-04-09
Sugar-sweetened beverages and added sugars (monosaccharides and disaccharides) in the diet are associated with obesity, diabetes, and cardiovascular disease, which are all risk factors for decline in physical function among older adults. The aim of this study was to examine the association between added sugars in the diet and incidence of frailty in older people. Data were taken from 1973 Spanish adults ≥60 y old from the Seniors-ENRICA cohort. In 2008-2010 (baseline), consumption of added sugars (including those in fruit juices) was obtained using a validated diet history. Study participants were followed up until 2012-2013 to assess frailty based on Fried's criteria. Statistical analyses were performed with logistic regression adjusted for age, sex, education, smoking status, body mass index, energy intake, self-reported comorbidities, Mediterranean Diet Adherence Score (excluding sweetened drinks and pastries), TV watching time, and leisure-time physical activity. Compared with participants consuming added sugars (lowest tertile), those consuming ≥36 g/d (highest tertile) were more likely to develop frailty (OR: 2.27; 95% CI: 1.34, 3.90; P-trend = 0.003). The frailty components "low physical activity" and "unintentional weight loss" increased dose dependently with added sugars. Association with frailty was strongest for sugars added during food production. Intake of sugars naturally appearing in foods was not associated with frailty. The consumption of added sugars in the diet of older people was associated with frailty, mainly when present in processed foods. The frailty components that were most closely associated with added sugars were low level of physical activity and unintentional weight loss. Future research should determine whether there is a causal relation between added sugars and frailty.
A generalized multivariate regression model for modelling ocean wave heights
Wang, X. L.; Feng, Y.; Swail, V. R.
2012-04-01
In this study, a generalized multivariate linear regression model is developed to represent the relationship between 6-hourly ocean significant wave heights (Hs) and the corresponding 6-hourly mean sea level pressure (MSLP) fields. The model is calibrated using the ERA-Interim reanalysis of Hs and MSLP fields for 1981-2000, and is validated using the ERA-Interim reanalysis for 2001-2010 and ERA40 reanalysis of Hs and MSLP for 1958-2001. The performance of the fitted model is evaluated in terms of Pierce skill score, frequency bias index, and correlation skill score. Being not normally distributed, wave heights are subjected to a data adaptive Box-Cox transformation before being used in the model fitting. Also, since 6-hourly data are being modelled, lag-1 autocorrelation must be and is accounted for. The models with and without Box-Cox transformation, and with and without accounting for autocorrelation, are inter-compared in terms of their prediction skills. The fitted MSLP-Hs relationship is then used to reconstruct historical wave height climate from the 6-hourly MSLP fields taken from the Twentieth Century Reanalysis (20CR, Compo et al. 2011), and to project possible future wave height climates using CMIP5 model simulations of MSLP fields. The reconstructed and projected wave heights, both seasonal means and maxima, are subject to a trend analysis that allows for non-linear (polynomial) trends.
Frequency of frailty and its association with cognitive status and survival in older Chileans
Albala C
2017-06-01
Full Text Available Cecilia Albala,1 Lydia Lera,1 Hugo Sanchez,1 Barbara Angel,1 Carlos Márquez,1 Patricia Arroyo,2 Patricio Fuentes2 1Public Health Nutrition Unit, Institute of Nutrition and Food Technology (INTA, University of Chile, 2Faculty of Medicine, Clinical Hospital, University of Chile, Santiago, Chile Background: Age-associated brain physiologic decline and reduced mobility are key elements of increased age-associated vulnerability.Objective: To study the frequency of frailty phenotype and its association with mental health and survival in older Chileans.Methods: Follow-up of ALEXANDROS cohorts designed to study disability associated with obesity in community-dwelling people 60 years and older living in Santiago, Chile. At baseline, 2,098 (67% women of 2,372 participants were identified as having the frailty phenotype: weak handgrip dynamometry, unintentional weight loss, fatigue/exhaustion, five chair-stands/slow walking speed and difficulty walking (low physical activity. After 10–15 years, 1,298 people were evaluated and 373 had died. Information regarding deaths was available for the whole sample.Results: The prevalence of frailty at baseline (≥3 criteria in the whole sample was 13.9% (women 16.4%; men 8.7% and the pre-frailty prevalence (1–2 criteria was 63.8% (65.0% vs 61.4%, respectively. Frailty was associated with cognitive impairment (frail 48.1%; pre-frail 21.7%; nonfrail 20.5%, P<0.001 and depression (frail 55.1%; pre-frail 27.3%; nonfrail 18.8%, P<0.001. Logistic regression models for frailty adjusted for sex and age showed a strong association between frailty and mild cognitive impairment (MCI (odds ratio [OR] =3.93; 95% CI: 1.41–10.92. Furthermore, an important association was found for depression and frailty (OR =2.36; 95% CI 1.82–3.06. Age- and sex-adjusted hazard ratios (HRs for death showed an increased risk with increasing frailty: pre-frail HR =1.56 (95% CI: 1.07–2.29, frail HR =1.91 (95% CI: 1.15–3.19; after
Clegg, A.; Young, J.; Iliffe, S.; Olde Rikkert, M.G.M.; Rockwood, K.
2013-01-01
Frailty is the most problematic expression of population ageing. It is a state of vulnerability to poor resolution of homoeostasis after a stressor event and is a consequence of cumulative decline in many physiological systems during a lifetime. This cumulative decline depletes homoeostatic reserves
Identification of Influential Points in a Linear Regression Model
Jan Grosz
2011-03-01
Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.
Does Frailty Predict Health Care Utilization in Community-Living Older Romanians?
Marinela Olaroiu
2016-01-01
Full Text Available Background. The predictive value of frailty assessment is still debated. We analyzed the predictive value of frailty of independent living elderly. The outcomes variables were visits to the general practitioner, hospital admission, and occurrence of new health problems. Methods. A one-year follow-up study was executed among 215 community-living old Romanians. General practitioners reported the outcome variables of patients, whose frailty was assessed one year before, using the Groningen Frailty Indicator. The predictive validity is analyzed by descriptive and regression analysis. Results. Three-quarters of all participants visited their general practitioner three times more last year and one-third were at least once admitted to a hospital. Patients who scored frail one year before were more often admitted to a hospital. Visits to the general practitioner and occurrence of new health problems were not statistically significant related to frailty scores. The frailty items polypharmacy, social support, and activities in daily living were associated with adverse outcomes. Conclusions. The predictive value of frailty instruments as the Groningen Frailty Indicator is still limited. More research is needed to predict health outcomes, health care utilization, and quality of life of frailty self-assessment instruments. Validation research on frailty in different “environments” is recommended to answer the question to what extent contextual characteristics influence the predictive value.
An estimating equation for parametric shared frailty models with marginal additive hazards
Pipper, Christian Bressen; Martinussen, Torben
2004-01-01
Multivariate failure time data arise when data consist of clusters in which the failure times may be dependent. A popular approach to such data is the marginal proportional hazards model with estimation under the working independence assumption. In some contexts, however, it may be more reasonable...
Detection of epistatic effects with logic regression and a classical linear regression model.
Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata
2014-02-01
To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.
Frailty and outcomes after implantation of left ventricular assist device as destination therapy.
Dunlay, Shannon M; Park, Soon J; Joyce, Lyle D; Daly, Richard C; Stulak, John M; McNallan, Sheila M; Roger, Véronique L; Kushwaha, Sudhir S
2014-04-01
Frailty is recognized as a major prognostic indicator in heart failure. There has been interest in understanding whether pre-operative frailty is associated with worse outcomes after implantation of a left ventricular assist device (LVAD) as destination therapy. Patients undergoing LVAD implantation as destination therapy at the Mayo Clinic, Rochester, Minnesota, from February 2007 to June 2012, were included in this study. Frailty was assessed using the deficit index (31 impairments, disabilities and comorbidities) and defined as the proportion of deficits present. We divided patients based on tertiles of the deficit index (>0.32 = frail, 0.23 to 0.32 = intermediate frail, <0.23 = not frail). Cox proportional hazard regression models were used to examine the association between frailty and death. Patients were censored at death or last follow-up through October 2013. Among 99 patients (mean age 65 years, 18% female, 55% with ischemic heart failure), the deficit index ranged from 0.10 to 0.65 (mean 0.29). After a mean follow-up of 1.9 ± 1.6 years, 79% of the patients had been rehospitalized (range 0 to 17 hospitalizations, median 1 per person) and 45% had died. Compared with those who were not frail, patients who were intermediate frail (adjusted HR 1.70, 95% CI 0.71 to 4.31) and frail (HR 3.08, 95% CI 1.40 to 7.48) were at increased risk for death (p for trend = 0.004). The mean (SD) number of days alive out of hospital the first year after LVAD was 293 (107) for not frail, 266 (134) for intermediate frail and 250 (132) for frail patients. Frailty before destination LVAD implantation is associated with increased risk of death and may represent a significant patient selection consideration. Copyright © 2014 International Society for Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.
Random regression models for detection of gene by environment interaction
Meuwissen Theo HE
2007-02-01
Full Text Available Abstract Two random regression models, where the effect of a putative QTL was regressed on an environmental gradient, are described. The first model estimates the correlation between intercept and slope of the random regression, while the other model restricts this correlation to 1 or -1, which is expected under a bi-allelic QTL model. The random regression models were compared to a model assuming no gene by environment interactions. The comparison was done with regards to the models ability to detect QTL, to position them accurately and to detect possible QTL by environment interactions. A simulation study based on a granddaughter design was conducted, and QTL were assumed, either by assigning an effect independent of the environment or as a linear function of a simulated environmental gradient. It was concluded that the random regression models were suitable for detection of QTL effects, in the presence and absence of interactions with environmental gradients. Fixing the correlation between intercept and slope of the random regression had a positive effect on power when the QTL effects re-ranked between environments.
Keith, Timothy Z
2014-01-01
Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.
Frailty, HIV infection, and mortality in an aging cohort of injection drug users.
Damani A Piggott
Full Text Available Frailty is associated with morbidity and premature mortality among elderly HIV-uninfected adults, but the determinants and consequences of frailty in HIV-infected populations remain unclear. We evaluated the correlates of frailty, and the impact of frailty on mortality in a cohort of aging injection drug users (IDUs.Frailty was assessed using standard criteria among HIV-infected and uninfected IDUs in 6-month intervals from 2005 to 2008. Generalized linear mixed-model analyses assessed correlates of frailty. Cox proportional hazards models estimated risk for all-cause mortality.Of 1230 participants at baseline, the median age was 48 years and 29% were HIV-infected; the frailty prevalence was 12.3%. In multivariable analysis of 3,365 frailty measures, HIV-infected IDUs had an increased likelihood of frailty (OR, 1.66; 95% CI, 1.24-2.21 compared to HIV-uninfected IDUs; the association was strongest (OR, 2.37; 95% CI, 1.62-3.48 among HIV-infected IDUs with advanced HIV disease (CD4<350 cells/mm3 and detectable HIV RNA. No significant association was seen with less advanced disease. Sociodemographic factors, comorbidity, depressive symptoms, and prescription drug abuse were also independently associated with frailty. Mortality risk was increased with frailty alone (HR 2.63, 95% CI, 1.23-5.66, HIV infection alone (HR 3.29, 95% CI, 1.85-5.88, and being both HIV-infected and frail (HR, 7.06; 95%CI 3.49-14.3.Frailty was strongly associated with advanced HIV disease, but IDUs with well-controlled HIV had a similar prevalence to HIV-uninfected IDUs. Frailty was independently associated with mortality, with a marked increase in mortality risk for IDUs with both frailty and HIV infection.
Tutorial on Using Regression Models with Count Outcomes Using R
A. Alexander Beaujean
2016-02-01
Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.
Frailty and postoperative outcomes in patients undergoing surgery for degenerative spine disease.
Flexman, Alana M; Charest-Morin, Raphaële; Stobart, Liam; Street, John; Ryerson, Christopher J
2016-11-01
Frailty is defined as a state of decreased reserve and susceptibility to stressors. The relationship between frailty and postoperative outcomes after degenerative spine surgery has not been studied. This study aimed to (1) determine prevalence of frailty in the degenerative spine population; (2) describe patient characteristics associated with frailty; and (3) determine the association between frailty and postoperative complications, mortality, length of stay, and discharge disposition. This is a retrospective analysis on a prospectively collected cohort from the National Surgical Quality Improvement Program (NSQIP). A total of 53,080 patients who underwent degenerative spine surgery between 2006 and 2012 were included in the study. A modified frailty index (mFI) with 11 variables derived from the NSQIP dataset was used to determine prevalence of frailty and its correlation with a composite outcome of perioperative complications as well as hospital length of stay, mortality, and discharge disposition. After calculating the mFI for each patient, the prevalence and predictors of frailty were determined for our cohort. The association of frailty with postoperative outcomes was determined after adjusting for known and suspected confounders using multivariate logistic regression. Frailty was present in 2,041 patients within the total population (4%) and in 8% of patients older than 65 years. Frailty severity increased with increasing age, male sex, African American race, higher body mass index, recent weight loss, paraplegia or quadriplegia, American Society of Anesthesiologists (ASA) score, and preadmission residence in a care facility. Frailty severity was an independent predictor of major complication (OR 1.15 for every 0.10 increase in mFI, 95%CI 1.09-1.21, pdegenerative spine surgery. Preoperative recognition of frailty may be useful for perioperative optimization, risk stratification, and patient counseling. Copyright © 2016 Elsevier Inc. All rights reserved.
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi
2014-01-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both
Correlation-regression model for physico-chemical quality of ...
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areas, suggesting that groundwater quality in urban areas is closely related with land use ... the ground water, with correlation and regression model is also presented. ...... WHO (World Health Organization) (1985). Health hazards from nitrates.
Development and validation of the FRAGIRE tool for assessment an older person's risk for frailty.
Vernerey, Dewi; Anota, Amelie; Vandel, Pierre; Paget-Bailly, Sophie; Dion, Michele; Bailly, Vanessa; Bonin, Marie; Pozet, Astrid; Foubert, Audrey; Benetkiewicz, Magdalena; Manckoundia, Patrick; Bonnetain, Franck
2016-11-17
Frailty is highly prevalent in elderly people. While significant progress has been made to understand its pathogenesis process, few validated questionnaire exist to assess the multidimensional concept of frailty and to detect people frail or at risk to become frail. The objectives of this study were to construct and validate a new frailty-screening instrument named Frailty Groupe Iso-Ressource Evaluation (FRAGIRE) that accurately predicts the risk for frailty in older adults. A prospective multicenter recruitment of the elderly patients was undertaken in France. The subjects were classified into financially-helped group (FH, with financial assistance) and non-financially helped group (NFH, without any financial assistance), considering FH subjects are more frail than the NFH group and thus representing an acceptable surrogate population for frailty. Psychometric properties of the FRAGIRE grid were assessed including discrimination between the FH and NFH groups. Items reduction was made according to statistical analyses and experts' point of view. The association between items response and tests with "help requested status" was assessed in univariate and multivariate unconditional logistic regression analyses and a prognostic score to become frail was finally proposed for each subject. Between May 2013 and July 2013, 385 subjects were included: 338 (88%) in the FH group and 47 (12%) in the NFH group. The initial FRAGIRE grid included 65 items. After conducting the item selection, the final grid of the FRAGIRE was reduced to 19 items. The final grid showed fair discrimination ability to predict frailty (area under the curve (AUC) = 0.85) and good calibration (Hosmer-Lemeshow P-value = 0.580), reflecting a good agreement between the prediction by the final model and actual observation. The Cronbach's alpha for the developed tool scored as high as 0.69 (95% Confidence Interval: 0.64 to 0.74). The final prognostic score was excellent, with an AUC of 0
Oral health conditions and frailty in Mexican community-dwelling elderly: a cross sectional analysis
Castrejón-Pérez Roberto
2012-09-01
Full Text Available Abstract Background Oral health is an important component of general well-being for the elderly. Oral health-related problems include loss of teeth, nonfunctional removable dental prostheses, lesions of the oral mucosa, periodontitis, and root caries. They affect food selection, speaking ability, mastication, social relations, and quality of life. Frailty is a geriatric syndrome that confers vulnerability to negative health-related outcomes. The association between oral health and frailty has not been explored thoroughly. This study sought to identify associations between the presence of some oral health conditions, and frailty status among Mexican community-dwelling elderly. Methods Analysis of baseline data of the Mexican Study of Nutritional and Psychosocial Markers of Frailty, a cohort study carried out in a representative sample of people aged 70 and older residing in one district of Mexico City. Frailty was defined as the presence of three or more of the following five components: weight loss, exhaustion, slowness, weakness, and low physical activity. Oral health variables included self-perception of oral health compared with others of the same age; utilization of dental services during the last year, number of teeth, dental condition (edentate, partially edentate, or completely dentate, utilization and functionality of removable partial or complete dentures, severe periodontitis, self-reported chewing problems and xerostomia. Covariates included were gender, age, years of education, cognitive performance, smoking status, recent falls, hospitalization, number of drugs, and comorbidity. The association between frailty and dental variables was determined performing a multivariate logistic regression analysis. Final models were adjusted by socio-demographic and health factors Results Of the 838 participants examined, 699 had the information needed to establish the criteria for diagnosis of frailty. Those who had a higher probability of being
#Frailty: A snapshot Twitter report on frailty knowledge translation.
Jha, Sunita R; McDonagh, Julee; Prichard, Ros; Newton, Phillip J; Hickman, Louise D; Fung, Erik; Macdonald, Peter S; Ferguson, Caleb
2018-05-07
The objectives of this short report are to: (i) explore #Frailty Twitter activity over a six-month period; and (ii) provide a snapshot Twitter content analysis of #Frailty usage. A mixed-method study was conducted to explore Twitter data related to frailty. The primary search term was #Frailty. Objective 1: data were collected using Symplur analytics, including variables for total number of tweets, unique tweeters (users) and total number of impressions. Objective 2: a retrospectively conducted snapshot content analysis of 1500 #Frailty tweets was performed using TweetReach ™ . Over a six-month period (1 January 2017-31 June 2017), there was a total of 6545 #Frailty tweets, generating 14.8 million impressions across 3986 participants. Of the 1500 tweets (814 retweets; 202 replies; 484 original tweets), 56% were relevant to clinical frailty. The main contributors ('who') were as follows: the public (29%), researchers (25%), doctors (21%), organisations (18%) and other allied health professionals (7%). Tweet main message intention ('what') was public health/advocacy (41%), social communication (28%), research-based evidence/professional education (24%), professional opinion/case studies (15%) and general news/events (7%). Twitter is increasingly being used to communicate about frailty. It is important that thought leaders contribute to the conversation. There is potential to leverage Twitter to promote and disseminate frailty-related knowledge and research. © 2018 AJA Inc.
Schoenenberger, Andreas W; Moser, André; Bertschi, Dominic; Wenaweser, Peter; Windecker, Stephan; Carrel, Thierry; Stuck, Andreas E; Stortecky, Stefan
2018-02-26
This study sought to evaluate whether frailty improves mortality prediction in combination with the conventional scores. European System for Cardiac Operative Risk Evaluation (EuroSCORE) or Society of Thoracic Surgeons (STS) score have not been evaluated in combined models with frailty for mortality prediction after transcatheter aortic valve replacement (TAVR). This prospective cohort comprised 330 consecutive TAVR patients ≥70 years of age. Conventional scores and a frailty index (based on assessment of cognition, mobility, nutrition, and activities of daily living) were evaluated to predict 1-year all-cause mortality using Cox proportional hazards regression (providing hazard ratios [HRs] with confidence intervals [CIs]) and measures of test performance (providing likelihood ratio [LR] chi-square test statistic and C-statistic [CS]). All risk scores were predictive of the outcome (EuroSCORE, HR: 1.90 [95% CI: 1.45 to 2.48], LR chi-square test statistic 19.29, C-statistic 0.67; STS score, HR: 1.51 [95% CI: 1.21 to 1.88], LR chi-square test statistic 11.05, C-statistic 0.64; frailty index, HR: 3.29 [95% CI: 1.98 to 5.47], LR chi-square test statistic 22.28, C-statistic 0.66). A combination of the frailty index with either EuroSCORE (LR chi-square test statistic 38.27, C-statistic 0.72) or STS score (LR chi-square test statistic 28.71, C-statistic 0.68) improved mortality prediction. The frailty index accounted for 58.2% and 77.6% of the predictive information in the combined model with EuroSCORE and STS score, respectively. Net reclassification improvement and integrated discrimination improvement confirmed that the added frailty index improved risk prediction. This is the first study showing that the assessment of frailty significantly enhances prediction of 1-year mortality after TAVR in combined risk models with conventional risk scores and relevantly contributes to this improvement. Copyright © 2018 American College of Cardiology Foundation
Wavelet regression model in forecasting crude oil price
Hamid, Mohd Helmie; Shabri, Ani
2017-05-01
This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.
Real estate value prediction using multivariate regression models
Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav
2017-11-01
The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.
Application of random regression models to the genetic evaluation ...
The model included fixed regression on AM (range from 30 to 138 mo) and the effect of herd-measurement date concatenation. Random parts of the model were RRM coefficients for additive and permanent environmental effects, while residual effects were modelled to account for heterogeneity of variance by AY. Estimates ...
The APT model as reduced-rank regression
Bekker, P.A.; Dobbelstein, P.; Wansbeek, T.J.
Integrating the two steps of an arbitrage pricing theory (APT) model leads to a reduced-rank regression (RRR) model. So the results on RRR can be used to estimate APT models, making estimation very simple. We give a succinct derivation of estimation of RRR, derive the asymptotic variance of RRR
Alternative regression models to assess increase in childhood BMI
Mansmann Ulrich
2008-09-01
Full Text Available Abstract Background Body mass index (BMI data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs, quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS. We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. Results GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. Conclusion GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.
Alternative regression models to assess increase in childhood BMI.
Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M
2008-09-08
Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.
Nutrition, Frailty, Cognitive Frailty and Prevention of Disabilities with Aging.
Guyonnet, Sophie; Secher, Marion; Vellas, Bruno
2015-01-01
Older adults can be categorized into three subgroups to better design and develop personalized interventions: the disabled (those needing assistance in the accomplishment of basic activities of daily living), the 'frail' (those presenting limitations and impairments in the absence of disability) and the 'robust' (those without frailty or disability). However, despite evidence linking frailty with a poor outcome, frailty is not implemented clinically in most countries. Since many people are not identified as frail, their treatment is frequently inappropriate in health care settings. Assessing the frail and prefrail older adults can no longer be delayed, we should rather act preventively before the irreversible disabling cascade is in place. Clinical characteristics of frailty such as weakness, low energy, slow walking speed, low physical activity and weight loss underline the links between nutrition and frailty. Physical frailty is also associated with cognitive frailty. We need to better understand cognitive frailty, a syndrome which must be differentiated from Alzheimer's disease. At the Gérontopôle frailty clinics, we have found that almost 40% of the patients referred to our center by their primary care physicians to evaluate frailty had significant weight loss in the past 3 months, 83.9% of patients presented slow gait speed, 53.8% a sedentary lifestyle and 57.7% poor muscle strength. Moreover, 43% had a Mini-Nutritional Assessment less than 23.5 and 9% less than 17, which reflects protein-energy undernutrition. More than 60% had some cognitive impairment associated with physical frailty. © 2015 Nestec Ltd., Vevey/S. Karger AG, Basel.
Robust mislabel logistic regression without modeling mislabel probabilities.
Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun
2018-03-01
Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.
Yamaguchi, Miwa; Yamada, Yosuke; Nanri, Hinako; Nozawa, Yoshizu; Itoi, Aya; Yoshimura, Eiichi; Watanabe, Yuya; Yoshida, Tsukasa; Yokoyama, Keiichi; Goto, Chiho; Ishikawa-Takata, Kazuko; Kobayashi, Hisamine; Kimura, Misaka
2018-01-13
We aimed to investigate whether frequencies of protein-rich food intake were associated with frailty among older Japanese adults. A cross-sectional study was conducted in 2011 among 3843 men and 4331 women in a population-based cohort of Kameoka city, Kyoto Prefecture, Japan. Frailty was assessed by the weighted score based on the 25-item Kihon-Checklist. The frequency of protein-rich food intake was examined as "seafood", "meat", "dairy products", "eggs", and "soy products". The outcome of frailty was analyzed with a multiple logistic regression model using the frequency of protein-rich food intake. When compared to the first quartile, it was observed that there was a significant association between the lower adjusted prevalence ratio (PR) for frailty and the frequency of seafood intake in the fourth quartile among men (PR 0.64, 95% confidence interval (CI), 0.42, 0.99) and from the second quartile to the third quartile among women (PR 0.61, 95% CI, 0.43, 0.85; PR 0.64, 95% CI, 0.46, 0.91). The frequency of dairy products intake in the third quartile among women was significantly associated with a lower PR for frailty ( p -value = 0.013). Our findings suggest that the consumption of seafood and dairy products may help older adults in maintaining their independence.
Miwa Yamaguchi
2018-01-01
Full Text Available We aimed to investigate whether frequencies of protein-rich food intake were associated with frailty among older Japanese adults. A cross-sectional study was conducted in 2011 among 3843 men and 4331 women in a population-based cohort of Kameoka city, Kyoto Prefecture, Japan. Frailty was assessed by the weighted score based on the 25-item Kihon-Checklist. The frequency of protein-rich food intake was examined as “seafood”, “meat”, “dairy products”, “eggs”, and “soy products”. The outcome of frailty was analyzed with a multiple logistic regression model using the frequency of protein-rich food intake. When compared to the first quartile, it was observed that there was a significant association between the lower adjusted prevalence ratio (PR for frailty and the frequency of seafood intake in the fourth quartile among men (PR 0.64, 95% confidence interval (CI, 0.42, 0.99 and from the second quartile to the third quartile among women (PR 0.61, 95% CI, 0.43, 0.85; PR 0.64, 95% CI, 0.46, 0.91. The frequency of dairy products intake in the third quartile among women was significantly associated with a lower PR for frailty (p-value = 0.013. Our findings suggest that the consumption of seafood and dairy products may help older adults in maintaining their independence.
Linear regression models for quantitative assessment of left ...
Changes in left ventricular structures and function have been reported in cardiomyopathies. No prediction models have been established in this environment. This study established regression models for prediction of left ventricular structures in normal subjects. A sample of normal subjects was drawn from a large urban ...
Geographically Weighted Logistic Regression Applied to Credit Scoring Models
Pedro Henrique Melo Albuquerque
Full Text Available Abstract This study used real data from a Brazilian financial institution on transactions involving Consumer Direct Credit (CDC, granted to clients residing in the Distrito Federal (DF, to construct credit scoring models via Logistic Regression and Geographically Weighted Logistic Regression (GWLR techniques. The aims were: to verify whether the factors that influence credit risk differ according to the borrower’s geographic location; to compare the set of models estimated via GWLR with the global model estimated via Logistic Regression, in terms of predictive power and financial losses for the institution; and to verify the viability of using the GWLR technique to develop credit scoring models. The metrics used to compare the models developed via the two techniques were the AICc informational criterion, the accuracy of the models, the percentage of false positives, the sum of the value of false positive debt, and the expected monetary value of portfolio default compared with the monetary value of defaults observed. The models estimated for each region in the DF were distinct in their variables and coefficients (parameters, with it being concluded that credit risk was influenced differently in each region in the study. The Logistic Regression and GWLR methodologies presented very close results, in terms of predictive power and financial losses for the institution, and the study demonstrated viability in using the GWLR technique to develop credit scoring models for the target population in the study.
Physics constrained nonlinear regression models for time series
Majda, Andrew J; Harlim, John
2013-01-01
A central issue in contemporary science is the development of data driven statistical nonlinear dynamical models for time series of partial observations of nature or a complex physical model. It has been established recently that ad hoc quadratic multi-level regression (MLR) models can have finite-time blow up of statistical solutions and/or pathological behaviour of their invariant measure. Here a new class of physics constrained multi-level quadratic regression models are introduced, analysed and applied to build reduced stochastic models from data of nonlinear systems. These models have the advantages of incorporating memory effects in time as well as the nonlinear noise from energy conserving nonlinear interactions. The mathematical guidelines for the performance and behaviour of these physics constrained MLR models as well as filtering algorithms for their implementation are developed here. Data driven applications of these new multi-level nonlinear regression models are developed for test models involving a nonlinear oscillator with memory effects and the difficult test case of the truncated Burgers–Hopf model. These new physics constrained quadratic MLR models are proposed here as process models for Bayesian estimation through Markov chain Monte Carlo algorithms of low frequency behaviour in complex physical data. (paper)
Model-based Quantile Regression for Discrete Data
Padellini, Tullia
2018-04-10
Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution\\'s parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.
Frailty and life satisfaction in Shanghai older adults: The roles of age and social vulnerability.
Yang, Fang; Gu, Danan; Mitnitski, Arnold
2016-01-01
This study aims to examine the relationship between frailty and life satisfaction and the roles of age and social vulnerability underlying the links in Chinese older adults. Using a cross-sectional sample of 1970 adults aged 65 and older in 2013 in Shanghai, we employed regression analyses to investigate the interaction between frailty and age on life satisfaction in the whole sample and in different social vulnerability groups. Life satisfaction was measured using a sum score of satisfaction with thirteen domains. Using a cumulative deficit approach, frailty was constructed from fifty-two variables and social vulnerability was derived from thirty-five variables. Frailty was negatively associated with life satisfaction. The interaction between frailty and age was significant for life satisfaction, such that the negative association between frailty and life satisfaction was stronger among the young-old aged 65-79 than among the old-old aged 80+. Moreover, frailty's stronger association with life satisfaction in the young-old than in the old-old was only found among those in the 2nd and 3rd tertiles of social vulnerability, but not for those in the 1st tertile of social vulnerability. Relation between frailty and life satisfaction likely weakens with age. A higher level of social vulnerability enlarges the negative impact of frailty on life satisfaction with a greater extent in the young-old. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
A Study of relationship between frailty and physical performance in elderly women.
Jeoung, Bog Ja; Lee, Yang Chool
2015-08-01
Frailty is a disorder of multiple inter-related physiological systems. It is unclear whether the level of physical performance factors can serve as markers of frailty and a sign. The purpose of this study was to examine the relationship between frailty and physical performance in elderly women. One hundred fourteen elderly women participated in this study, their aged was from 65 to 80. We were measured 6-min walk test, grip-strength, 30-sec arm curl test, 30-sec chair stand test, 8 foot Up- and Go, Back scratch, chair sit and reach, unipedal stance, BMI, and the frailty with questionnaire. The collected data were analyzed by descriptive statistics, frequencies, correlation analysis, ANOVA, and simple liner regression using the IBM 21. SPSS program. In results, statistic tests showed that there were significant differences between frailty and 6-min walk test, 30-sec arm curl test, 30-sec chair stand test, grip-strength, Back scratch, and BMI. However, we did not find significant differences between frailty and 8 foot Up- and Go, unipedal stance. When the subjects were divided into five groups according to physical performance level, subjects with high 6-min walk, 30-sec arm curl test, chair sit and reach test, and high grip strength had low score frailty. Physical performance factors were strongly associated with decreased frailty, suggesting that physical performance improvements play an important role in preventing or reducing the frailty.
Moffatt, Heather; Moorhouse, Paige; Mallery, Laurie; Landry, David; Tennankore, Karthik
2018-01-01
Recent evidence supports the prognostic significance of frailty for functional decline and poor health outcomes in patients with chronic kidney disease. Yet, despite the development of clinical tools to screen for frailty, little is known about the experiential impact of screening for frailty in this setting. The Frailty Assessment for Care Planning Tool (FACT) evaluates frailty across 4 domains: mobility, function, social circumstances, and cognition. The purpose of this qualitative study was as follows: 1) explore the nurse experience of screening for frailty using the FACT tool in a specialized outpatient renal clinic; 2) determine how, if at all, provider perceptions of frailty changed after implementation of the frailty screening tool; and 3) determine the perceived factors that influence uptake and administration of the FACT screening tool in a specialized clinical setting. A semi-structured interview of 5 nurses from the Nova Scotia Health Authority, Central Zone Renal Clinic was conducted. A grounded theory approach was used to generate thematic categories and analysis models. Four primary themes emerged in the data analysis: "we were skeptical", "we made it work", "we learned how", and "we understand". As the renal nurses gained a sense of confidence in their ability to implement the FACT tool, initial barriers to implementation were attenuated. Implementation factors - such as realistic goals, clear guidelines, and ongoing training - were important factors for successful uptake of the frailty screening initiative. Nurse participants reported an overall positive experience using the FACT method to screen for frailty and indicated that their understanding of the multiple dimensions and subtleties of "frailty" were enhanced. Future nurse-led FACT screening initiatives should incorporate those factors identified as being integral to program success: realistic goals, clear guidelines, and ongoing training. Adopting the evaluation of frailty as a priority
Akın, Sibel; Mazıcıoglu, Mumtaz M; Mucuk, Salime; Gocer, Semsinnur; Deniz Şafak, Elif; Arguvanlı, Sibel; Ozturk, Ahmet
2015-10-01
The purpose of this study is to determine the prevalence of frailty with the Fried Frailty Index (FFI) and FRAIL scales (Fatigue, Resistance, Ambulation, Illness, Low weight) and also its associated factors in the community-dwelling Turkish elderly. This is a cross-sectional population-based study in an urban area with a population of over 1,200,000. We sampled 1/100 of the elderly population. Frailty prevalence was assessed with a modified version of the FFI and FRAIL scale. Nutritional status was assessed by Mini Nutritional Assessment. Cognitive function was assessed by Mini-Mental State Examination. Depressive mood was assessed by GDS. Functional capacity was assessed by the instrumental activities of daily living scale. Falls and fear of falling were noted. Uni- and multivariate analyses were done to determine associated factors for frailty. A total of 906 community-dwelling elderly were included, in whom the mean age and standard deviation (SD) of age were 71.5 (5.6) years (50.6 % female). We detected frailty (female 30.4 %, male 25.2 %), pre-frailty and non-frailty prevalence with FFI as 27.8, 34.8, and 37.4 %, respectively. The prevalence of frailty (female 14.5 %, male 5.4 %), pre-frailty and non-frailty with the FRAIL scale was detected as 10, 45.6, and 44.4 %. Coexisting associated factors related with frailty in both models were found as depressive mood, cognitive impairment, and malnutrition in multivariate analysis. According to both scales, frailty was strongly associated with cognitive impairment, depressive mood, and malnutrition in the community-dwelling Turkish elderly population.
Frailty and incident depression in community-dwelling older people: results from the ELSA study.
Veronese, Nicola; Solmi, Marco; Maggi, Stefania; Noale, Marianna; Sergi, Giuseppe; Manzato, Enzo; Prina, A Matthew; Fornaro, Michele; Carvalho, André F; Stubbs, Brendon
2017-12-01
Frailty and pre-frailty are two common conditions in the older people, but whether these conditions could predict depression is still limited to a few longitudinal studies. In this paper, we aimed to investigate whether frailty and pre-frailty are associated with an increased risk of depression in a prospective cohort of community-dwelling older people. Four thousand seventy-seven community-dwelling men and women over 60 years without depression at baseline were included from the English Longitudinal Study of Ageing. Frailty status was defined according to modified Fried's criteria (weakness, weight loss, slow gait speed, low physical activity and exhaustion) and categorized as frailty (≥3 criteria), pre-frailty (1-2 criteria) or robustness (0 criterion). Depression was diagnosed as ≥4 out of 8 points of Center for Epidemiologic Studies Depression Scale, after 2 years of follow-up. Over a 2-year follow-up, 360 individuals developed depression. In a logistic regression analysis, adjusted for 18 potential baseline confounders, pre-frailty (odds ratio (OR) = 0.89; 95% confidence interval (CI), 0.54-1.46; p = 0.64) and frailty (OR = 1.22; 95% CI, 0.90-1.64; p = 0.21) did not predict the onset of depression at follow-up. Among the criteria included in the frailty definition, only slow gait speed (OR = 1.82; 95% CI, 1.00-3.32; p = 0.05) appeared to predict a higher risk of depression. Among older community dwellers, frailty and pre-frailty did not predict the onset of depression during 2 years of follow-up, when accounting for potential confounders, whilst slow gait speed considered alone may predict depression in the older people. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.
Chatzis, Sotirios P; Andreou, Andreas S
2015-11-01
Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.
Forecasting daily meteorological time series using ARIMA and regression models
Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir
2018-04-01
The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.
Multiple Response Regression for Gaussian Mixture Models with Known Labels.
Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng
2012-12-01
Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.
Thermal Efficiency Degradation Diagnosis Method Using Regression Model
Jee, Chang Hyun; Heo, Gyun Young; Jang, Seok Won; Lee, In Cheol
2011-01-01
This paper proposes an idea for thermal efficiency degradation diagnosis in turbine cycles, which is based on turbine cycle simulation under abnormal conditions and a linear regression model. The correlation between the inputs for representing degradation conditions (normally unmeasured but intrinsic states) and the simulation outputs (normally measured but superficial states) was analyzed with the linear regression model. The regression models can inversely response an associated intrinsic state for a superficial state observed from a power plant. The diagnosis method proposed herein is classified into three processes, 1) simulations for degradation conditions to get measured states (referred as what-if method), 2) development of the linear model correlating intrinsic and superficial states, and 3) determination of an intrinsic state using the superficial states of current plant and the linear regression model (referred as inverse what-if method). The what-if method is to generate the outputs for the inputs including various root causes and/or boundary conditions whereas the inverse what-if method is the process of calculating the inverse matrix with the given superficial states, that is, component degradation modes. The method suggested in this paper was validated using the turbine cycle model for an operating power plant
Harrell , Jr , Frank E
2015-01-01
This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes. This text realistically...
Flexible competing risks regression modeling and goodness-of-fit
Scheike, Thomas; Zhang, Mei-Jie
2008-01-01
In this paper we consider different approaches for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause...... models that is easy to fit and contains the Fine-Gray model as a special case. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy...... of the flexible regression models to analyze competing risks data when non-proportionality is present in the data....
The art of regression modeling in road safety
Hauer, Ezra
2015-01-01
This unique book explains how to fashion useful regression models from commonly available data to erect models essential for evidence-based road safety management and research. Composed from techniques and best practices presented over many years of lectures and workshops, The Art of Regression Modeling in Road Safety illustrates that fruitful modeling cannot be done without substantive knowledge about the modeled phenomenon. Class-tested in courses and workshops across North America, the book is ideal for professionals, researchers, university professors, and graduate students with an interest in, or responsibilities related to, road safety. This book also: · Presents for the first time a powerful analytical tool for road safety researchers and practitioners · Includes problems and solutions in each chapter as well as data and spreadsheets for running models and PowerPoint presentation slides · Features pedagogy well-suited for graduate courses and workshops including problems, solutions, and PowerPoint p...
Model building strategy for logistic regression: purposeful selection.
Zhang, Zhongheng
2016-03-01
Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.
Predicting factors associated with frailty in aged patients with bone-arthrosis pain in the clinic.
Li, Bao-Lin; Li, Wei; Bi, Jia-Qi; Meng, Qing-Gang; Fei, Jian-Feng
2016-11-01
To identify frail and pre-frail patients in a group of patients older than 60 years. The phenotype model of Fried's method was used to identify frailty and pre-frailty in total of 78 participants. Cognitive ability and psychosocial function tests were also given to 59 of the 78 patients. Prevalence of frailty and pre-frailty was 14.1% (11/78) and 46.2% (36/78), respectively. Of the 5 phenotype variables, weak grip strength was the most commonly seen variable with 53.8% of all participants and 100% in the frail group. Low energy expenditure, however, was not self-reported by any participant in the current study (0%). Prevalence of frailty in the present study is associated with chronological age. The current study indicates that 4 phenotypic variables (unintentional weight loss, self-reported exhaustion, gait speed and grip strength) contribute to the development to frailty, and that cognitive impairment and psychosocial frailty also predict frailty or pre-frailty in the patients older than 60 years old irrespective of chronic pain or osteoarthritis. The findings of the current study suggest frailty and pre-frailty are common in senior Chinese patients with chronic diseases. Recognition and identification of frailty in a rehabilitation clinic or hospital might help physicians to provide appropriate counseling to patients and families about adverse outcomes of certain treatments such as surgery, and could optimize management of coexisting chronic diseases that might contribute to or be affected by frailty.
Regression analysis of a chemical reaction fouling model
Vasak, F.; Epstein, N.
1996-01-01
A previously reported mathematical model for the initial chemical reaction fouling of a heated tube is critically examined in the light of the experimental data for which it was developed. A regression analysis of the model with respect to that data shows that the reference point upon which the two adjustable parameters of the model were originally based was well chosen, albeit fortuitously. (author). 3 refs., 2 tabs., 2 figs
Frailty phenotypes in the elderly based on cluster analysis
Dato, Serena; Montesanto, Alberto; Lagani, Vincenzo
2012-01-01
groups of subjects homogeneous for their frailty status and characterized by different survival patterns. A subsequent survival analysis availing of Accelerated Failure Time models allowed us to formulate an operative index able to correlate classification variables with survival probability. From......Frailty is a physiological state characterized by the deregulation of multiple physiologic systems of an aging organism determining the loss of homeostatic capacity, which exposes the elderly to disability, diseases, and finally death. An operative definition of frailty, useful...... for the classification of the individual quality of aging, is needed. On the other hand, the documented heterogeneity in the quality of aging among different geographic areas suggests the necessity for a frailty classification approach providing population-specific results. Moreover, the contribution of the individual...
Spatial stochastic regression modelling of urban land use
Arshad, S H M; Jaafar, J; Abiden, M Z Z; Latif, Z A; Rasam, A R A
2014-01-01
Urbanization is very closely linked to industrialization, commercialization or overall economic growth and development. This results in innumerable benefits of the quantity and quality of the urban environment and lifestyle but on the other hand contributes to unbounded development, urban sprawl, overcrowding and decreasing standard of living. Regulation and observation of urban development activities is crucial. The understanding of urban systems that promotes urban growth are also essential for the purpose of policy making, formulating development strategies as well as development plan preparation. This study aims to compare two different stochastic regression modeling techniques for spatial structure models of urban growth in the same specific study area. Both techniques will utilize the same datasets and their results will be analyzed. The work starts by producing an urban growth model by using stochastic regression modeling techniques namely the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The two techniques are compared to and it is found that, GWR seems to be a more significant stochastic regression model compared to OLS, it gives a smaller AICc (Akaike's Information Corrected Criterion) value and its output is more spatially explainable
Direction of Effects in Multiple Linear Regression Models.
Wiedermann, Wolfgang; von Eye, Alexander
2015-01-01
Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.
Logistic regression for risk factor modelling in stuttering research.
Reed, Phil; Wu, Yaqionq
2013-06-01
To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.
Modeling and prediction of flotation performance using support vector regression
Despotović Vladimir
2017-01-01
Full Text Available Continuous efforts have been made in recent year to improve the process of paper recycling, as it is of critical importance for saving the wood, water and energy resources. Flotation deinking is considered to be one of the key methods for separation of ink particles from the cellulose fibres. Attempts to model the flotation deinking process have often resulted in complex models that are difficult to implement and use. In this paper a model for prediction of flotation performance based on Support Vector Regression (SVR, is presented. Representative data samples were created in laboratory, under a variety of practical control variables for the flotation deinking process, including different reagents, pH values and flotation residence time. Predictive model was created that was trained on these data samples, and the flotation performance was assessed showing that Support Vector Regression is a promising method even when dataset used for training the model is limited.
Bayesian approach to errors-in-variables in regression models
Rozliman, Nur Aainaa; Ibrahim, Adriana Irawati Nur; Yunus, Rossita Mohammad
2017-05-01
In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.
Time series regression model for infectious disease and weather.
Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro
2015-10-01
Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Variable Selection for Regression Models of Percentile Flows
Fouad, G.
2017-12-01
Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high
Linearity and Misspecification Tests for Vector Smooth Transition Regression Models
Teräsvirta, Timo; Yang, Yukai
The purpose of the paper is to derive Lagrange multiplier and Lagrange multiplier type specification and misspecification tests for vector smooth transition regression models. We report results from simulation studies in which the size and power properties of the proposed asymptotic tests in small...
Application of multilinear regression analysis in modeling of soil ...
The application of Multi-Linear Regression Analysis (MLRA) model for predicting soil properties in Calabar South offers a technical guide and solution in foundation designs problems in the area. Forty-five soil samples were collected from fifteen different boreholes at a different depth and 270 tests were carried out for CBR, ...
Efficient estimation of an additive quantile regression model
Cheng, Y.; de Gooijer, J.G.; Zerom, D.
2009-01-01
In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By
Efficient estimation of an additive quantile regression model
Cheng, Y.; de Gooijer, J.G.; Zerom, D.
2010-01-01
In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By
Efficient estimation of an additive quantile regression model
Cheng, Y.; de Gooijer, J.G.; Zerom, D.
2011-01-01
In this paper, two non-parametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a more viable alternative to existing kernel-based approaches. The second estimator
A binary logistic regression model with complex sampling design of ...
2017-09-03
Sep 3, 2017 ... Bi-variable and multi-variable binary logistic regression model with complex sampling design was fitted. .... Data was entered into STATA-12 and analyzed using. SPSS-21. .... lack of access/too far or costs too much. 35. 1.2.
Transpiration of glasshouse rose crops: evaluation of regression models
Baas, R.; Rijssel, van E.
2006-01-01
Regression models of transpiration (T) based on global radiation inside the greenhouse (G), with or without energy input from heating pipes (Eh) and/or vapor pressure deficit (VPD) were parameterized. Therefore, data on T, G, temperatures from air, canopy and heating pipes, and VPD from both a
Approximating prediction uncertainty for random forest regression models
John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne
2016-01-01
Machine learning approaches such as random forest haveÂ increased for the spatial modeling and mapping of continuousÂ variables. Random forest is a non-parametric ensembleÂ approach, and unlike traditional regression approaches thereÂ is no direct quantification of prediction error. UnderstandingÂ prediction uncertainty is important when using model-basedÂ continuous maps as...
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....
On concurvity in nonlinear and nonparametric regression models
Sonia Amodio
2014-12-01
Full Text Available When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM. The term concurvity describes nonlinear dependencies among the predictor variables. As collinearity results in inflated variance of the estimated regression coefficients in the linear regression model, the result of the presence of concurvity leads to instability of the estimated coefficients in GAMs. Even if the backfitting algorithm will always converge to a solution, in case of concurvity the final solution of the backfitting procedure in fitting a GAM is influenced by the starting functions. While exact concurvity is highly unlikely, approximate concurvity, the analogue of multicollinearity, is of practical concern as it can lead to upwardly biased estimates of the parameters and to underestimation of their standard errors, increasing the risk of committing type I error. We compare the existing approaches to detect concurvity, pointing out their advantages and drawbacks, using simulated and real data sets. As a result, this paper will provide a general criterion to detect concurvity in nonlinear and non parametric regression models.
Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate
Minh Vu Trieu
2017-03-01
Full Text Available This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS, Brazilian tensile strength (BTS, rock brittleness index (BI, the distance between planes of weakness (DPW, and the alpha angle (Alpha between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP. Four (4 statistical regression models (two linear and two nonlinear are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2 of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.
Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate
Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno
2017-03-01
This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.
Detection of Outliers in Regression Model for Medical Data
Stephen Raj S
2017-07-01
Full Text Available In regression analysis, an outlier is an observation for which the residual is large in magnitude compared to other observations in the data set. The detection of outliers and influential points is an important step of the regression analysis. Outlier detection methods have been used to detect and remove anomalous values from data. In this paper, we detect the presence of outliers in simple linear regression models for medical data set. Chatterjee and Hadi mentioned that the ordinary residuals are not appropriate for diagnostic purposes; a transformed version of them is preferable. First, we investigate the presence of outliers based on existing procedures of residuals and standardized residuals. Next, we have used the new approach of standardized scores for detecting outliers without the use of predicted values. The performance of the new approach was verified with the real-life data.
Hierarchical Neural Regression Models for Customer Churn Prediction
Golshan Mohammadi
2013-01-01
Full Text Available As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN, self-organizing maps (SOM, alpha-cut fuzzy c-means (α-FCM, and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, and α-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, the α-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.
Exercise prescription to reverse frailty.
Bray, Nick W; Smart, Rowan R; Jakobi, Jennifer M; Jones, Gareth R
2016-10-01
Frailty is a clinical geriatric syndrome caused by physiological deficits across multiple systems. These deficits make it challenging to sustain homeostasis required for the demands of everyday life. Exercise is likely the best therapy to reverse frailty status. Literature to date suggests that pre-frail older adults, those with 1-2 deficits on the Cardiovascular Health Study-Frailty Phenotype (CHS-frailty phenotype), should exercise 2-3 times a week, for 45-60 min. Aerobic, resistance, flexibility, and balance training components should be incorporated but resistance and balance activities should be emphasized. On the other hand, frail (CHS-frailty phenotype ≥ 3 physical deficits) older adults should exercise 3 times per week, for 30-45 min for each session with an emphasis on aerobic training. During aerobic, balance, and flexibility training, both frail and pre-frail older adults should work at an intensity equivalent to a rating of perceived exertion of 3-4 ("somewhat hard") on the Borg CR10 scale. Resistance-training intensity should be based on a percentage of 1-repetition estimated maximum (1RM). Program onset should occur at 55% of 1RM (endurance) and progress to higher intensities of 80% of 1RM (strength) to maximize functional gains. Exercise is the medicine to reverse or mitigate frailty, preserve quality of life, and restore independent functioning in older adults at risk of frailty.
Electricity consumption forecasting in Italy using linear regression models
Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio [DIAM, Seconda Universita degli Studi di Napoli, Via Roma 29, 81031 Aversa (CE) (Italy)
2009-09-15
The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of {+-}1% for the best case and {+-}11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)
Electricity consumption forecasting in Italy using linear regression models
Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio
2009-01-01
The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)
Regression Model to Predict Global Solar Irradiance in Malaysia
Hairuniza Ahmed Kutty
2015-01-01
Full Text Available A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE, mean bias error (MBE, and the coefficient of determination (R2 with other models available from literature studies. Seven models based on single parameters (PM1 to PM7 and five multiple-parameter models (PM7 to PM12 are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%, R2 ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.
Grip strength as a frailty diagnostic component in geriatric inpatients
Dudzińska-Griszek J
2017-07-01
Full Text Available Joanna Dudzińska-Griszek, Karolina Szuster, Jan Szewieczek Department of Geriatrics, School of Health Sciences in Katowice, Medical University of Silesia, Katowice, Poland Background: Frailty has emerged as a key medical syndrome predictive of comorbidity, disability, institutionalization and death. As a component of the five frailty phenotype diagnostic criteria, patient grip strength deserves attention as a simple and objective measure of the frailty syndrome. The aim of this study was to assess conditions that influence grip strength in geriatric inpatients.Patients and methods: The study group consisted of 80 patients aged 78.6±7.0 years ( X ± SD, with 68.8% women, admitted to the Department of Geriatrics. A comprehensive geriatric assessment was complemented with assessment for the frailty phenotype as described by Fried et al for all patients in the study group. Functional assessment included Barthel Index of Activities of Daily Living (Barthel Index, Instrumental Activities of Daily Living Scale and Mini-Mental State Examination.Results: Three or more frailty criteria were positive in 32 patients (40%, while 56 subjects (70% fulfilled the frailty criterion of weakness (grip strength test. Multivariate linear regression analysis revealed that two independent measures showed positive association with grip strength – Mini-Mental State Examination score (β=0.239; P=0.001 and statin use (β=0.213; P=0.002 – and four independent measures were negatively associated with grip strength – female sex (β=–0.671; P<0.001, C-reactive protein (β=–0.253; P<0.001, prior myocardial infarction (β=–0.190; P=0.006 and use of an antidepressant (β=–0.163; P=0.018. Low physical activity was identified as the only independent qualitative frailty component associated with 2-year mortality in multivariate logistic regression analysis after adjustment for age and sex (odds ratio =6.000; 95% CI =1.357–26.536; P=0.018.Conclusion: Cognitive
Wade, Katie F; Marshall, Alan; Vanhoutte, Bram; Wu, Frederick C W; O'Neill, Terence W; Lee, David M
2017-03-01
Pain has been suggested to act as a stressor during aging, potentially accelerating declines in health and functioning. Our objective was to examine the longitudinal association between self-reported pain and the development, or worsening, of frailty among older men and women. The study population consisted of 5,316 men and women living in private households in England, mean age 64.5 years, participating in the English Longitudinal Study of Ageing (ELSA). Data from Waves 2 and 6 of ELSA were used in this study with 8 years of follow-up. At Wave 2, participants were asked whether they were "often troubled with pain" and for those who reported yes, further information regarding the intensity of their pain (mild, moderate, or severe) was collected. Socioeconomic status (SES) was assessed using information about the current/most recent occupation and also net wealth. A frailty index (FI) was generated, with the presence of frailty defined as an FI >0.35. Among those without frailty at Wave 2, the association between pain at Wave 2 and frailty at Wave 6 was examined using logistic regression. We investigated whether pain predicted change in FI between Waves 2 and 6 using a negative binomial regression model. For both models adjustments were made for age, gender, lifestyle factors, depressive symptoms, and socioeconomic factors. At Wave 2, 455 (19.7%) men and 856 (28.7%) women reported they often experienced moderate or severe pain. Of the 5,159 participants who were nonfrail at Wave 2, 328 (6.4%) were frail by Wave 6. The mean FI was 0.11 (standard deviation [SD] = 0.1) at Wave 2 and 0.15 (SD = 0.1) at Wave 6. After adjustment for age, gender, body mass index, lifestyle factors, and depressive symptoms, compared to participants reporting no pain at Wave 2 those reporting moderate (odds ratio [OR] = 3.08, 95% confidence interval [CI] = 2.28, 4.16) or severe pain (OR = 3.78, 95% CI = 2.51, 5.71) were significantly more likely to be frail at Wave 6. This association
Two-step variable selection in quantile regression models
FAN Yali
2015-06-01
Full Text Available We propose a two-step variable selection procedure for high dimensional quantile regressions, in which the dimension of the covariates, pn is much larger than the sample size n. In the first step, we perform ℓ1 penalty, and we demonstrate that the first step penalized estimator with the LASSO penalty can reduce the model from an ultra-high dimensional to a model whose size has the same order as that of the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying the adaptive LASSO penalty to the reduced model obtained from the first step. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. We conduct a simulation study and a real data analysis to evaluate the finite sample performance of the proposed approach.
New robust statistical procedures for the polytomous logistic regression models.
Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro
2018-05-17
This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.
THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE
Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan
2015-01-01
Background: The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran?s universities. Methods: This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran?s public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For pr...
Online Statistical Modeling (Regression Analysis) for Independent Responses
Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus
2017-06-01
Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.
Lutomski, Jennifer E; Baars, Maria A E; Boter, Han; Buurman, Bianca M; den Elzen, Wendy P J; Jansen, Aaltje P D; Kempen, Gertrudis I J M; Steunenberg, Bas; Steyerberg, Ewout W; Olde Rikkert, Marcel G M; Melis, René J F
2014-01-01
To assess the independent and combined impact of frailty, multi-morbidity, and activities of daily living (ADL) limitations on self-reported quality of life and healthcare costs in elderly people. Cross-sectional, descriptive study. Data came from The Older Persons and Informal Caregivers Minimum DataSet (TOPICS-MDS), a pooled dataset with information from 41 projects across the Netherlands from the Dutch national care for the Elderly programme. Frailty, multi-morbidity and ADL limitations, and the interactions between these domains, were used as predictors in regression analyses with quality of life and healthcare costs as outcome measures. Analyses were stratified by living situation (independent or care home). Directionality and magnitude of associations were assessed using linear mixed models. A total of 11,093 elderly people were interviewed. A substantial proportion of elderly people living independently reported frailty, multi-morbidity, and/or ADL limitations (56.4%, 88.3% and 41.4%, respectively), as did elderly people living in a care home (88.7%, 89.2% and 77,3%, respectively). One-third of elderly people living at home (31.9%) reported all three conditions compared with two-thirds of elderly people living in a care home (68.3%). In the multivariable analysis, frailty had a strong impact on outcomes independently of multi-morbidity and ADL limitations. Elderly people experiencing problems across all three domains reported the poorest quality-of-life scores and the highest healthcare costs, irrespective of their living situation. Frailty, multi-morbidity and ADL limitations are complementary measurements, which together provide a more holistic understanding of health status in elderly people. A multi-dimensional approach is important in mapping the complex relationships between these measurements on the one hand and the quality of life and healthcare costs on the other.
Reconstruction of missing daily streamflow data using dynamic regression models
Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault
2015-12-01
River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.
Predicting and Modelling of Survival Data when Cox's Regression Model does not hold
Scheike, Thomas H.; Zhang, Mei-Jie
2002-01-01
Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...
Extended cox regression model: The choice of timefunction
Isik, Hatice; Tutkun, Nihal Ata; Karasoy, Durdu
2017-07-01
Cox regression model (CRM), which takes into account the effect of censored observations, is one the most applicative and usedmodels in survival analysis to evaluate the effects of covariates. Proportional hazard (PH), requires a constant hazard ratio over time, is the assumptionofCRM. Using extended CRM provides the test of including a time dependent covariate to assess the PH assumption or an alternative model in case of nonproportional hazards. In this study, the different types of real data sets are used to choose the time function and the differences between time functions are analyzed and discussed.
A test of inflated zeros for Poisson regression models.
He, Hua; Zhang, Hui; Ye, Peng; Tang, Wan
2017-01-01
Excessive zeros are common in practice and may cause overdispersion and invalidate inference when fitting Poisson regression models. There is a large body of literature on zero-inflated Poisson models. However, methods for testing whether there are excessive zeros are less well developed. The Vuong test comparing a Poisson and a zero-inflated Poisson model is commonly applied in practice. However, the type I error of the test often deviates seriously from the nominal level, rendering serious doubts on the validity of the test in such applications. In this paper, we develop a new approach for testing inflated zeros under the Poisson model. Unlike the Vuong test for inflated zeros, our method does not require a zero-inflated Poisson model to perform the test. Simulation studies show that when compared with the Vuong test our approach not only better at controlling type I error rate, but also yield more power.
Development and validation of the FRAGIRE tool for assessment an older person’s risk for frailty
Dewi Vernerey
2016-11-01
Full Text Available Abstract Background Frailty is highly prevalent in elderly people. While significant progress has been made to understand its pathogenesis process, few validated questionnaire exist to assess the multidimensional concept of frailty and to detect people frail or at risk to become frail. The objectives of this study were to construct and validate a new frailty-screening instrument named Frailty Groupe Iso-Ressource Evaluation (FRAGIRE that accurately predicts the risk for frailty in older adults. Methods A prospective multicenter recruitment of the elderly patients was undertaken in France. The subjects were classified into financially-helped group (FH, with financial assistance and non-financially helped group (NFH, without any financial assistance, considering FH subjects are more frail than the NFH group and thus representing an acceptable surrogate population for frailty. Psychometric properties of the FRAGIRE grid were assessed including discrimination between the FH and NFH groups. Items reduction was made according to statistical analyses and experts’ point of view. The association between items response and tests with “help requested status” was assessed in univariate and multivariate unconditional logistic regression analyses and a prognostic score to become frail was finally proposed for each subject. Results Between May 2013 and July 2013, 385 subjects were included: 338 (88% in the FH group and 47 (12% in the NFH group. The initial FRAGIRE grid included 65 items. After conducting the item selection, the final grid of the FRAGIRE was reduced to 19 items. The final grid showed fair discrimination ability to predict frailty (area under the curve (AUC = 0.85 and good calibration (Hosmer-Lemeshow P-value = 0.580, reflecting a good agreement between the prediction by the final model and actual observation. The Cronbach's alpha for the developed tool scored as high as 0.69 (95% Confidence Interval: 0.64 to 0.74. The final
Conceptualizations of frailty in relation to older adults.
Markle-Reid, Maureen; Browne, Gina
2003-10-01
The aim of this article is to discuss the concept of frailty and its adequacy in identifying and describing older adults as frail. Despite the dramatic increase in use of the term 'frailty' over the past two decades, there is a lack of consensus in the literature about its meaning and use, and no clear conceptual guidelines for identifying and describing older adults as frail. Differences in theoretical perspectives will influence policy decisions regarding eligibility for, and allocation of, scarce health care resources among older adults. The article presents a literature review and synthesis of definitions and conceptual models of frailty in relation to older adults. The first part of the paper is a summary of the synonyms, antonyms and definitions of the term frailty. The second part is a critical evaluation of conceptual models of frailty. Six conceptual models are analysed on the basis of four main categories of assumptions about: (1) the nature of scientific knowledge; (2) the level of analysis; (3) the ageing process; (4) the stability of frailty. The implications of these are discussed in relation to clinical practice, policy and research. The review gives guidelines for a new theoretical approach to the concept of frailty in older adults: (1) it must be a multidimensional concept that considers the complex interplay of physical, psychological, social and environmental factors; (2) the concept must not be age-related, suggesting a negative and stereotypical view of ageing; (3) the concept must take into account an individual's context and incorporate subjective perceptions; (4) the concept must take into account the contribution of both individual and environmental factors.
Multivariate Frequency-Severity Regression Models in Insurance
Edward W. Frees
2016-02-01
Full Text Available In insurance and related industries including healthcare, it is common to have several outcome measures that the analyst wishes to understand using explanatory variables. For example, in automobile insurance, an accident may result in payments for damage to one’s own vehicle, damage to another party’s vehicle, or personal injury. It is also common to be interested in the frequency of accidents in addition to the severity of the claim amounts. This paper synthesizes and extends the literature on multivariate frequency-severity regression modeling with a focus on insurance industry applications. Regression models for understanding the distribution of each outcome continue to be developed yet there now exists a solid body of literature for the marginal outcomes. This paper contributes to this body of literature by focusing on the use of a copula for modeling the dependence among these outcomes; a major advantage of this tool is that it preserves the body of work established for marginal models. We illustrate this approach using data from the Wisconsin Local Government Property Insurance Fund. This fund offers insurance protection for (i property; (ii motor vehicle; and (iii contractors’ equipment claims. In addition to several claim types and frequency-severity components, outcomes can be further categorized by time and space, requiring complex dependency modeling. We find significant dependencies for these data; specifically, we find that dependencies among lines are stronger than the dependencies between the frequency and average severity within each line.
Augmented Beta rectangular regression models: A Bayesian perspective.
Wang, Jue; Luo, Sheng
2016-01-01
Mixed effects Beta regression models based on Beta distributions have been widely used to analyze longitudinal percentage or proportional data ranging between zero and one. However, Beta distributions are not flexible to extreme outliers or excessive events around tail areas, and they do not account for the presence of the boundary values zeros and ones because these values are not in the support of the Beta distributions. To address these issues, we propose a mixed effects model using Beta rectangular distribution and augment it with the probabilities of zero and one. We conduct extensive simulation studies to assess the performance of mixed effects models based on both the Beta and Beta rectangular distributions under various scenarios. The simulation studies suggest that the regression models based on Beta rectangular distributions improve the accuracy of parameter estimates in the presence of outliers and heavy tails. The proposed models are applied to the motivating Neuroprotection Exploratory Trials in Parkinson's Disease (PD) Long-term Study-1 (LS-1 study, n = 1741), developed by The National Institute of Neurological Disorders and Stroke Exploratory Trials in Parkinson's Disease (NINDS NET-PD) network. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Bayesian semiparametric regression models to characterize molecular evolution
Datta Saheli
2012-10-01
Full Text Available Abstract Background Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments. Results The Bayesian semiparametric approach is illustrated with simulated data and the abalone lysin sperm data. Our method identifies groups of properties which, for this particular dataset, have a similar effect on evolution. The model also provides nonparametric site-specific estimates for the strength of conservation of these properties. Conclusions The model described here is distinguished by its ability to handle a large number of amino acid properties simultaneously, while taking into account that such data can be correlated. The multi-level clustering ability of the model allows for appealing interpretations of the results in terms of properties that are roughly equivalent from the standpoint of molecular evolution.
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu
2014-06-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both the regression coefficient and inverse scale matrices simultaneously. The sparsity is introduced through penalizing the negative log-likelihood by adding L1-penalties on the entries of the two matrices. Taking advantage of the hierarchical representation of skew-t distributions, and using the expectation conditional maximization (ECM) algorithm, we reduce the problem to penalized normal likelihood and develop a procedure to minimize the ensuing objective function. Using a simulation study the performance of the method is assessed, and the methodology is illustrated using a real data set with a 24-dimensional response vector. © 2014 Elsevier B.V.
Modeling the number of car theft using Poisson regression
Zulkifli, Malina; Ling, Agnes Beh Yen; Kasim, Maznah Mat; Ismail, Noriszura
2016-10-01
Regression analysis is the most popular statistical methods used to express the relationship between the variables of response with the covariates. The aim of this paper is to evaluate the factors that influence the number of car theft using Poisson regression model. This paper will focus on the number of car thefts that occurred in districts in Peninsular Malaysia. There are two groups of factor that have been considered, namely district descriptive factors and socio and demographic factors. The result of the study showed that Bumiputera composition, Chinese composition, Other ethnic composition, foreign migration, number of residence with the age between 25 to 64, number of employed person and number of unemployed person are the most influence factors that affect the car theft cases. These information are very useful for the law enforcement department, insurance company and car owners in order to reduce and limiting the car theft cases in Peninsular Malaysia.
Dynamic Regression Intervention Modeling for the Malaysian Daily Load
Fadhilah Abdrazak
2014-05-01
Full Text Available Malaysia is a unique country due to having both fixed and moving holidays. These moving holidays may overlap with other fixed holidays and therefore, increase the complexity of the load forecasting activities. The errors due to holidays’ effects in the load forecasting are known to be higher than other factors. If these effects can be estimated and removed, the behavior of the series could be better viewed. Thus, the aim of this paper is to improve the forecasting errors by using a dynamic regression model with intervention analysis. Based on the linear transfer function method, a daily load model consists of either peak or average is developed. The developed model outperformed the seasonal ARIMA model in estimating the fixed and moving holidays’ effects and achieved a smaller Mean Absolute Percentage Error (MAPE in load forecast.
Learning Supervised Topic Models for Classification and Regression from Crowds.
Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete; Pereira, Francisco C
2017-12-01
The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.
Continuous validation of ASTEC containment models and regression testing
Nowack, Holger; Reinke, Nils; Sonnenkalb, Martin
2014-01-01
The focus of the ASTEC (Accident Source Term Evaluation Code) development at GRS is primarily on the containment module CPA (Containment Part of ASTEC), whose modelling is to a large extent based on the GRS containment code COCOSYS (COntainment COde SYStem). Validation is usually understood as the approval of the modelling capabilities by calculations of appropriate experiments done by external users different from the code developers. During the development process of ASTEC CPA, bugs and unintended side effects may occur, which leads to changes in the results of the initially conducted validation. Due to the involvement of a considerable number of developers in the coding of ASTEC modules, validation of the code alone, even if executed repeatedly, is not sufficient. Therefore, a regression testing procedure has been implemented in order to ensure that the initially obtained validation results are still valid with succeeding code versions. Within the regression testing procedure, calculations of experiments and plant sequences are performed with the same input deck but applying two different code versions. For every test-case the up-to-date code version is compared to the preceding one on the basis of physical parameters deemed to be characteristic for the test-case under consideration. In the case of post-calculations of experiments also a comparison to experimental data is carried out. Three validation cases from the regression testing procedure are presented within this paper. The very good post-calculation of the HDR E11.1 experiment shows the high quality modelling of thermal-hydraulics in ASTEC CPA. Aerosol behaviour is validated on the BMC VANAM M3 experiment, and the results show also a very good agreement with experimental data. Finally, iodine behaviour is checked in the validation test-case of the THAI IOD-11 experiment. Within this test-case, the comparison of the ASTEC versions V2.0r1 and V2.0r2 shows how an error was detected by the regression testing
Modeling of the Monthly Rainfall-Runoff Process Through Regressions
Campos-Aranda Daniel Francisco
2014-10-01
Full Text Available To solve the problems associated with the assessment of water resources of a river, the modeling of the rainfall-runoff process (RRP allows the deduction of runoff missing data and to extend its record, since generally the information available on precipitation is larger. It also enables the estimation of inputs to reservoirs, when their building led to the suppression of the gauging station. The simplest mathematical model that can be set for the RRP is the linear regression or curve on a monthly basis. Such a model is described in detail and is calibrated with the simultaneous record of monthly rainfall and runoff in Ballesmi hydrometric station, which covers 35 years. Since the runoff of this station has an important contribution from the spring discharge, the record is corrected first by removing that contribution. In order to do this a procedure was developed based either on the monthly average regional runoff coefficients or on nearby and similar watershed; in this case the Tancuilín gauging station was used. Both stations belong to the Partial Hydrologic Region No. 26 (Lower Rio Panuco and are located within the state of San Luis Potosi, México. The study performed indicates that the monthly regression model, due to its conceptual approach, faithfully reproduces monthly average runoff volumes and achieves an excellent approximation in relation to the dispersion, proved by calculation of the means and standard deviations.
Genetic evaluation of European quails by random regression models
Flaviana Miranda Gonçalves
2012-09-01
Full Text Available The objective of this study was to compare different random regression models, defined from different classes of heterogeneity of variance combined with different Legendre polynomial orders for the estimate of (covariance of quails. The data came from 28,076 observations of 4,507 female meat quails of the LF1 lineage. Quail body weights were determined at birth and 1, 14, 21, 28, 35 and 42 days of age. Six different classes of residual variance were fitted to Legendre polynomial functions (orders ranging from 2 to 6 to determine which model had the best fit to describe the (covariance structures as a function of time. According to the evaluated criteria (AIC, BIC and LRT, the model with six classes of residual variances and of sixth-order Legendre polynomial was the best fit. The estimated additive genetic variance increased from birth to 28 days of age, and dropped slightly from 35 to 42 days. The heritability estimates decreased along the growth curve and changed from 0.51 (1 day to 0.16 (42 days. Animal genetic and permanent environmental correlation estimates between weights and age classes were always high and positive, except for birth weight. The sixth order Legendre polynomial, along with the residual variance divided into six classes was the best fit for the growth rate curve of meat quails; therefore, they should be considered for breeding evaluation processes by random regression models.
Interpreting parameters in the logistic regression model with random effects
Larsen, Klaus; Petersen, Jørgen Holm; Budtz-Jørgensen, Esben
2000-01-01
interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects......interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects...
Chen, S; Honda, T; Narazaki, K; Chen, T; Kishimoto, H; Haeuchi, Y; Kumagai, S
2018-01-01
To assess the relationship between physical frailty and subsequent decline in global cognitive function in the non-demented elderly. A prospective population-based study in a west Japanese suburban town, with two-year follow-up. Community-dwellers aged 65 and older without placement in long-term care, and not having a history of dementia, Parkinson's disease and depression at baseline, who participated in the cohort of the Sasaguri Genkimon Study and underwent follow-up assessments two years later (N = 1,045). Global cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Physical frailty was identified according to the following five components: weight loss, low grip strength, exhaustion, slow gait speed and low physical activities. Linear regression models were used to examine associations between baseline frailty status and the MoCA scores at follow-up. Logistic regression models were used to estimate the risk of cognitive decline (defined as at least two points decrease of MoCA score) according to baseline frailty status. Seven hundred and eight non-demented older adults were included in the final analyses (mean age: 72.6 ± 5.5 years, male 40.3%); 5.8% were frail, and 40.8% were prefrail at baseline. One hundred and fifty nine (22.5%) participants experienced cognitive decline over two years. After adjustment for baseline MoCA scores and all confounders, being frail at baseline was significantly associated with a decline of 1.48 points (95% confidence interval [CI], -2.37 to -0.59) in MoCA scores, as compared with non-frailty. Frail persons were over two times more likely to experience cognitive decline (adjusted odds ratio 2.28; 95% CI, 1.02 to 5.08), compared to non-frail persons. Physical frailty is associated with longitudinal decline in global cognitive function in the non-demented older adults over a period of two years. Physically frail older community-dwellers should be closely monitored for cognitive decline that can be
Learning Supervised Topic Models for Classification and Regression from Crowds
Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete
2017-01-01
problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages...... annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression...
Preference learning with evolutionary Multivariate Adaptive Regression Spline model
Abou-Zleikha, Mohamed; Shaker, Noor; Christensen, Mads Græsbøll
2015-01-01
This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing...... for function approximation as well as being relatively easy to interpret. MARS models are evolved based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed...
Predicting Performance on MOOC Assessments using Multi-Regression Models
Ren, Zhiyun; Rangwala, Huzefa; Johri, Aditya
2016-01-01
The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a stu- dent may achieve on a given grade-related assessment based on information, considered as prior performance or prior ac- tivity in the course. We develop a personalized linear mul- tiple regression (PLMR) model to predict the grade for a student, prior to attempt...
Analytical and regression models of glass rod drawing process
Alekseeva, L. B.
2018-03-01
The process of drawing glass rods (light guides) is being studied. The parameters of the process affecting the quality of the light guide have been determined. To solve the problem, mathematical models based on general equations of continuum mechanics are used. The conditions for the stable flow of the drawing process have been found, which are determined by the stability of the motion of the glass mass in the formation zone to small uncontrolled perturbations. The sensitivity of the formation zone to perturbations of the drawing speed and viscosity is estimated. Experimental models of the drawing process, based on the regression analysis methods, have been obtained. These models make it possible to customize a specific production process to obtain light guides of the required quality. They allow one to find the optimum combination of process parameters in the chosen area and to determine the required accuracy of maintaining them at a specified level.
Regression Models for Predicting Force Coefficients of Aerofoils
Mohammed ABDUL AKBAR
2015-09-01
Full Text Available Renewable sources of energy are attractive and advantageous in a lot of different ways. Among the renewable energy sources, wind energy is the fastest growing type. Among wind energy converters, Vertical axis wind turbines (VAWTs have received renewed interest in the past decade due to some of the advantages they possess over their horizontal axis counterparts. VAWTs have evolved into complex 3-D shapes. A key component in predicting the output of VAWTs through analytical studies is obtaining the values of lift and drag coefficients which is a function of shape of the aerofoil, ‘angle of attack’ of wind and Reynolds’s number of flow. Sandia National Laboratories have carried out extensive experiments on aerofoils for the Reynolds number in the range of those experienced by VAWTs. The volume of experimental data thus obtained is huge. The current paper discusses three Regression analysis models developed wherein lift and drag coefficients can be found out using simple formula without having to deal with the bulk of the data. Drag coefficients and Lift coefficients were being successfully estimated by regression models with R2 values as high as 0.98.
Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks
Kanevski, Mikhail
2015-04-01
The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press
Conditional Monte Carlo randomization tests for regression models.
Parhat, Parwen; Rosenberger, William F; Diao, Guoqing
2014-08-15
We discuss the computation of randomization tests for clinical trials of two treatments when the primary outcome is based on a regression model. We begin by revisiting the seminal paper of Gail, Tan, and Piantadosi (1988), and then describe a method based on Monte Carlo generation of randomization sequences. The tests based on this Monte Carlo procedure are design based, in that they incorporate the particular randomization procedure used. We discuss permuted block designs, complete randomization, and biased coin designs. We also use a new technique by Plamadeala and Rosenberger (2012) for simple computation of conditional randomization tests. Like Gail, Tan, and Piantadosi, we focus on residuals from generalized linear models and martingale residuals from survival models. Such techniques do not apply to longitudinal data analysis, and we introduce a method for computation of randomization tests based on the predicted rate of change from a generalized linear mixed model when outcomes are longitudinal. We show, by simulation, that these randomization tests preserve the size and power well under model misspecification. Copyright © 2014 John Wiley & Sons, Ltd.
Genomic breeding value estimation using nonparametric additive regression models
Solberg Trygve
2009-01-01
Full Text Available Abstract Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped was predicted using data from the next last generation (genotyped and phenotyped. The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.
Global Land Use Regression Model for Nitrogen Dioxide Air Pollution.
Larkin, Andrew; Geddes, Jeffrey A; Martin, Randall V; Xiao, Qingyang; Liu, Yang; Marshall, Julian D; Brauer, Michael; Hystad, Perry
2017-06-20
Nitrogen dioxide is a common air pollutant with growing evidence of health impacts independent of other common pollutants such as ozone and particulate matter. However, the worldwide distribution of NO 2 exposure and associated impacts on health is still largely uncertain. To advance global exposure estimates we created a global nitrogen dioxide (NO 2 ) land use regression model for 2011 using annual measurements from 5,220 air monitors in 58 countries. The model captured 54% of global NO 2 variation, with a mean absolute error of 3.7 ppb. Regional performance varied from R 2 = 0.42 (Africa) to 0.67 (South America). Repeated 10% cross-validation using bootstrap sampling (n = 10,000) demonstrated a robust performance with respect to air monitor sampling in North America, Europe, and Asia (adjusted R 2 within 2%) but not for Africa and Oceania (adjusted R 2 within 11%) where NO 2 monitoring data are sparse. The final model included 10 variables that captured both between and within-city spatial gradients in NO 2 concentrations. Variable contributions differed between continental regions, but major roads within 100 m and satellite-derived NO 2 were consistently the strongest predictors. The resulting model can be used for global risk assessments and health studies, particularly in countries without existing NO 2 monitoring data or models.
Testosterone and frailty in elderly men
Emmelot-Vonk, M.H.
2009-01-01
With aging, there is an increase of the incidence of frailty. Frailty is associated with adverse health outcomes, like falls and fractures, disabilities, hospitalization, institutionalization and mortality. It is generally accepted that frailty, unlike the aging process, is in part reversible and
Drought Patterns Forecasting using an Auto-Regressive Logistic Model
del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.
2014-12-01
Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.
A Gompertz regression model for fern spores germination
Gabriel y Galán, Jose María
2015-06-01
Full Text Available Germination is one of the most important biological processes for both seed and spore plants, also for fungi. At present, mathematical models of germination have been developed in fungi, bryophytes and several plant species. However, ferns are the only group whose germination has never been modelled. In this work we develop a regression model of the germination of fern spores. We have found that for Blechnum serrulatum, Blechnum yungense, Cheilanthes pilosa, Niphidium macbridei and Polypodium feuillei species the Gompertz growth model describe satisfactorily cumulative germination. An important result is that regression parameters are independent of fern species and the model is not affected by intraspecific variation. Our results show that the Gompertz curve represents a general germination model for all the non-green spore leptosporangiate ferns, including in the paper a discussion about the physiological and ecological meaning of the model.La germinación es uno de los procesos biológicos más relevantes tanto para las plantas con esporas, como para las plantas con semillas y los hongos. Hasta el momento, se han desarrollado modelos de germinación para hongos, briofitos y diversas especies de espermatófitos. Los helechos son el único grupo de plantas cuya germinación nunca ha sido modelizada. En este trabajo se desarrolla un modelo de regresión para explicar la germinación de las esporas de helechos. Observamos que para las especies Blechnum serrulatum, Blechnum yungense, Cheilanthes pilosa, Niphidium macbridei y Polypodium feuillei el modelo de crecimiento de Gompertz describe satisfactoriamente la germinación acumulativa. Un importante resultado es que los parámetros de la regresión son independientes de la especie y que el modelo no está afectado por variación intraespecífica. Por lo tanto, los resultados del trabajo muestran que la curva de Gompertz puede representar un modelo general para todos los helechos leptosporangiados
Stoicea, Nicoleta; Baddigam, Ramya; Wajahn, Jennifer; Sipes, Angela C; Arias-Morales, Carlos E; Gastaldo, Nicholas; Bergese, Sergio D
2016-01-01
The elderly population in the United States is increasing exponentially in tandem with risk for frailty. Frailty is described by a clinically significant state where a patient is at risk for developing complications requiring increased assistance in daily activities. Frailty syndrome studied in geriatric patients is responsible for an increased risk for falls, and increased mortality. In efforts to prepare for and to intervene in perioperative complications and general frailty, a universal scale to measure frailty is necessary. Many methods for determining frailty have been developed, yet there remains a need to define clinical frailty and, therefore, the most effective way to measure it. This article reviews six popular scales for measuring frailty and evaluates their clinical effectiveness demonstrated in previous studies. By identifying the most time-efficient, criteria comprehensive, and clinically effective scale, a universal scale can be implemented into standard of care and reduce complications from frailty in both non-surgical and surgical settings, especially applied to the perioperative surgical home model. We suggest further evaluation of the Edmonton Frailty Scale for inclusion in patient care.
Nicoleta Stoicea
2016-07-01
Full Text Available The elderly population in the United States is increasing exponentially in tandem with risk for frailty. Frailty is described by a clinically significant state where a patient is at risk for developing complications requiring increased assistance in daily activities. Frailty syndrome studied in geriatric patients is responsible for an increased risk for falls, and increased mortality. In efforts to prepare for and to intervene in perioperative complications and general frailty, a universal scale to measure frailty is necessary. Many methods for determining frailty have been developed, yet there remains a need to define clinical frailty and therefore the most effective way to measure it. This article reviews six popular scales for measuring frailty and evaluates their clinical effectiveness demonstrated in previous studies. By identifying the most time-efficient, criteria comprehensive, and clinically effective scale, a universal scale can be implemented into standard of care and reduce complications from frailty in both non-surgical and surgical settings, especially applied to the perioperative surgical home model. We suggest further evaluation of the Edmonton Frailty Scale for inclusion in patient care.
Collision prediction models using multivariate Poisson-lognormal regression.
El-Basyouny, Karim; Sayed, Tarek
2009-07-01
This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only (PDO) and injuries plus fatalities (I+F). This correlation was estimated at 0.758, which is highly significant, suggesting that higher PDO rates are associated with higher I+F rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in roadway design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models.
THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE.
Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan
2015-10-01
The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran's universities. This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran's public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For predicting the organizational climate pattern of the libraries is used from the multivariate linear regression and track diagram. of the 9 variables affecting organizational climate, 5 variables of innovation, teamwork, customer service, psychological safety and deep diversity play a major role in prediction of the organizational climate of Iran's libraries. The results also indicate that each of these variables with different coefficient have the power to predict organizational climate but the climate score of psychological safety (0.94) plays a very crucial role in predicting the organizational climate. Track diagram showed that five variables of teamwork, customer service, psychological safety, deep diversity and innovation directly effects on the organizational climate variable that contribution of the team work from this influence is more than any other variables. Of the indicator of the organizational climate of climateQual, the contribution of the team work from this influence is more than any other variables that reinforcement of teamwork in academic libraries can be more effective in improving the organizational climate of this type libraries.
Frailty measurements and dysphagia in the outpatient setting.
Hathaway, Bridget; Vaezi, Alec; Egloff, Ann Marie; Smith, Libby; Wasserman-Wincko, Tamara; Johnson, Jonas T
2014-09-01
Deconditioning and frailty may contribute to dysphagia and aspiration. Early identification of patients at risk of aspiration is important. Aspiration prevention would lead to reduced morbidity and health care costs. We therefore wondered whether objective measurements of frailty could help identify patients at risk for dysphagia and aspiration. Consecutive patients (n = 183) were enrolled. Patient characteristics and objective measures of frailty were recorded prospectively. Variables tested included age, body mass index, grip strength, and 5 meter walk pace. Statistical analysis tested for association between these parameters and dysphagia or aspiration, diagnosed by instrumental swallowing examination. Of variables tested for association with grip strength, only age category (P = .003) and ambulatory status (P dysphagia or aspiration, ambulatory status was significantly associated with dysphagia and aspiration in multivariable model building. Nonambulatory status is a predictor of aspiration and should be included in risk assessments for dysphagia. The relationship between frailty and dysphagia deserves further investigation. Frailty assessments may help identify those at risk for complications of dysphagia. © The Author(s) 2014.
Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing
Stinstra, E.; Rennen, G.; Teeuwen, G.J.A.
2006-01-01
The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression for the estimation of constants.Interval
Modeling Information Content Via Dirichlet-Multinomial Regression Analysis.
Ferrari, Alberto
2017-01-01
Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.
Variable selection in Logistic regression model with genetic algorithm.
Zhang, Zhongheng; Trevino, Victor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, Arumugam Manivanna; Zhang, Ping; Gorunescu, Florin; Subha, Velappan; Dai, Songshi
2018-02-01
Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.
Electricity prices forecasting by automatic dynamic harmonic regression models
Pedregal, Diego J.; Trapero, Juan R.
2007-01-01
The changes experienced by electricity markets in recent years have created the necessity for more accurate forecast tools of electricity prices, both for producers and consumers. Many methodologies have been applied to this aim, but in the view of the authors, state space models are not yet fully exploited. The present paper proposes a univariate dynamic harmonic regression model set up in a state space framework for forecasting prices in these markets. The advantages of the approach are threefold. Firstly, a fast automatic identification and estimation procedure is proposed based on the frequency domain. Secondly, the recursive algorithms applied offer adaptive predictions that compare favourably with respect to other techniques. Finally, since the method is based on unobserved components models, explicit information about trend, seasonal and irregular behaviours of the series can be extracted. This information is of great value to the electricity companies' managers in order to improve their strategies, i.e. it provides management innovations. The good forecast performance and the rapid adaptability of the model to changes in the data are illustrated with actual prices taken from the PJM interconnection in the US and for the Spanish market for the year 2002. (author)
Characteristics and Properties of a Simple Linear Regression Model
Kowal Robert
2016-12-01
Full Text Available A simple linear regression model is one of the pillars of classic econometrics. Despite the passage of time, it continues to raise interest both from the theoretical side as well as from the application side. One of the many fundamental questions in the model concerns determining derivative characteristics and studying the properties existing in their scope, referring to the first of these aspects. The literature of the subject provides several classic solutions in that regard. In the paper, a completely new design is proposed, based on the direct application of variance and its properties, resulting from the non-correlation of certain estimators with the mean, within the scope of which some fundamental dependencies of the model characteristics are obtained in a much more compact manner. The apparatus allows for a simple and uniform demonstration of multiple dependencies and fundamental properties in the model, and it does it in an intuitive manner. The results were obtained in a classic, traditional area, where everything, as it might seem, has already been thoroughly studied and discovered.
Bayesian Regression of Thermodynamic Models of Redox Active Materials
Johnston, Katherine [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-09-01
Finding a suitable functional redox material is a critical challenge to achieving scalable, economically viable technologies for storing concentrated solar energy in the form of a defected oxide. Demonstrating e ectiveness for thermal storage or solar fuel is largely accomplished by using a thermodynamic model derived from experimental data. The purpose of this project is to test the accuracy of our regression model on representative data sets. Determining the accuracy of the model includes parameter tting the model to the data, comparing the model using di erent numbers of param- eters, and analyzing the entropy and enthalpy calculated from the model. Three data sets were considered in this project: two demonstrating materials for solar fuels by wa- ter splitting and the other of a material for thermal storage. Using Bayesian Inference and Markov Chain Monte Carlo (MCMC), parameter estimation was preformed on the three data sets. Good results were achieved, except some there was some deviations on the edges of the data input ranges. The evidence values were then calculated in a variety of ways and used to compare models with di erent number of parameters. It was believed that at least one of the parameters was unnecessary and comparing evidence values demonstrated that the parameter was need on one data set and not signi cantly helpful on another. The entropy was calculated by taking the derivative in one variable and integrating over another. and its uncertainty was also calculated by evaluating the entropy over multiple MCMC samples. Afterwards, all the parts were written up as a tutorial for the Uncertainty Quanti cation Toolkit (UQTk).
Development of the interRAI home care frailty scale
John N. Morris
2016-11-01
Full Text Available Abstract Background The concept of frailty, a relative state of weakness reflecting multiple functional and health domains, continues to receive attention within the geriatrics field. It offers a summary of key personal characteristics, providing perspective on an individual’s life course. There have been multiple attempts to measure frailty, some focusing on physiologic losses, others on specific diseases, disabilities or health deficits. Recently, multidimensional approaches to measuring frailty have included cognition, mood and social components. The purpose of this project was to develop and evaluate a Home Care Frailty Scale and provide a grounded basis for assessing a person’s risk for decline that included functional and cognitive health, social deficits and troubling diagnostic and clinical conditions. Methods A secondary analysis design was used to develop the Home Care Frailty Scale. The data set consisted of client level home care data from service agencies around the world. The baseline sample included 967,865 assessments while the 6-month follow-up sample of persons still being served by the home care agencies consisted of 464,788 assessments. A pool of 70 candidate independent variables were screened for possible inclusion and 16 problem outcomes referencing accumulating declines and clinical complications served as the dependent variables. Multiple regression techniques were used to analyze the data. Results The resulting Home Care Frailty Scale consisted of a final set of 29 items. The items fall across 6 categories of function, movement, cognition and communication, social life, nutrition, and clinical symptoms. The prevalence of the items ranged from a high of 87% for persons requiring help with meal preparation to 3.7% for persons who have experienced a recent decline in the amount of food eaten. Conclusions The interRAI Home Care Frailty Scale is based on a strong conceptual foundation and in our analysis, performed as
Convergence diagnostics for Eigenvalue problems with linear regression model
Shi, Bo; Petrovic, Bojan
2011-01-01
Although the Monte Carlo method has been extensively used for criticality/Eigenvalue problems, a reliable, robust, and efficient convergence diagnostics method is still desired. Most methods are based on integral parameters (multiplication factor, entropy) and either condense the local distribution information into a single value (e.g., entropy) or even disregard it. We propose to employ the detailed cycle-by-cycle local flux evolution obtained by using mesh tally mechanism to assess the source and flux convergence. By applying a linear regression model to each individual mesh in a mesh tally for convergence diagnostics, a global convergence criterion can be obtained. We exemplify this method on two problems and obtain promising diagnostics results. (author)
The R Package threg to Implement Threshold Regression Models
Tao Xiao
2015-08-01
This new package includes four functions: threg, and the methods hr, predict and plot for threg objects returned by threg. The threg function is the model-fitting function which is used to calculate regression coefficient estimates, asymptotic standard errors and p values. The hr method for threg objects is the hazard-ratio calculation function which provides the estimates of hazard ratios at selected time points for specified scenarios (based on given categories or value settings of covariates. The predict method for threg objects is used for prediction. And the plot method for threg objects provides plots for curves of estimated hazard functions, survival functions and probability density functions of the first-hitting-time; function curves corresponding to different scenarios can be overlaid in the same plot for comparison to give additional research insights.
Simoneau, Gabrielle; Levis, Brooke; Cuijpers, Pim; Ioannidis, John P A; Patten, Scott B; Shrier, Ian; Bombardier, Charles H; de Lima Osório, Flavia; Fann, Jesse R; Gjerdingen, Dwenda; Lamers, Femke; Lotrakul, Manote; Löwe, Bernd; Shaaban, Juwita; Stafford, Lesley; van Weert, Henk C P M; Whooley, Mary A; Wittkampf, Karin A; Yeung, Albert S; Thombs, Brett D; Benedetti, Andrea
2017-11-01
Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Brain Pathology Contributes to Simultaneous Change in Physical Frailty and Cognition in Old Age
Yu, Lei; Wilson, Robert S.; Boyle, Patricia A.; Schneider, Julie A.; Bennett, David. A.
2014-01-01
Objective. First, we tested the hypothesis that the rate of change of physical frailty and cognitive function in older adults are correlated. Next, we examined if their rates of change are associated with the same brain pathologies. Methods. About 2,167 older adults participating in the Religious Orders Study and the Rush Memory and Aging Project had annual clinical evaluations. Bivariate random coefficient models were used to estimate simultaneously the rates of change in both frailty and cognition, and the correlation of change was characterized by a joint distribution of the random effects. Then, we examined whether postmortem indices from deceased were associated with the rate of change of frailty and cognition. Results. During an average follow-up of 6 years, frailty worsened by 0.09 unit/y and cognition declined by 0.08 unit/y. Most individuals showed worsening frailty and cognition (82.8%); 17% showed progressive frailty alone and cognitive decline. The rates of change of frailty and cognition were strongly correlated (ρ = −0.73, p cognition (all ps cognitive decline. Conclusion. The rates of change in frailty and cognition are strongly correlated and this may be due in part because they share a common pathologic basis. PMID:25136002
Ng, Kar Yong; Awang, Norhashidah
2018-01-06
Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.
Ultracentrifuge separative power modeling with multivariate regression using covariance matrix
Migliavacca, Elder
2004-01-01
In this work, the least-squares methodology with covariance matrix is applied to determine a data curve fitting to obtain a performance function for the separative power δU of a ultracentrifuge as a function of variables that are experimentally controlled. The experimental data refer to 460 experiments on the ultracentrifugation process for uranium isotope separation. The experimental uncertainties related with these independent variables are considered in the calculation of the experimental separative power values, determining an experimental data input covariance matrix. The process variables, which significantly influence the δU values are chosen in order to give information on the ultracentrifuge behaviour when submitted to several levels of feed flow rate F, cut θ and product line pressure P p . After the model goodness-of-fit validation, a residual analysis is carried out to verify the assumed basis concerning its randomness and independence and mainly the existence of residual heteroscedasticity with any explained regression model variable. The surface curves are made relating the separative power with the control variables F, θ and P p to compare the fitted model with the experimental data and finally to calculate their optimized values. (author)
Modeling Pan Evaporation for Kuwait by Multiple Linear Regression
Almedeij, Jaber
2012-01-01
Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values. PMID:23226984
[Factors associated with the frailty of elderly people with chronic kidney disease on hemodialysis].
Gesualdo, Gabriela Dutra; Zazzetta, Marisa Silvana; Say, Karina Gramani; Orlandi, Fabiana de Souza
2016-11-01
The scope of this article is to identify sociodemographic and clinical factors associated with the frailty of elderly people with chronic kidney disease on hemodialysis. This involved a correlational, cross-sectional study conducted in a dialysis center in the state of São Paulo. The sample consisted of 60 participants. The Participant Characterization Instrument was used for extracting sociodemographic and clinical data and the Edmonton Frail Scale was used to evaluate the level of frailty. Multivariate logistic regression was used to identify the factors associated with frailty. The mean age of the 60 patients included was 71.1 (± 6.9) years, predominantly male (70%), of which 36.7% were classified as frail. With respect to the factors associated with frailty among the variables of gender, age, self-reported skin color, schooling, monthly per capita income, hemodialysis time, number of associated diseases, falls in the year, hematocrit level, parathyroid hormone and use of calcitriol, it was found that only the monthly per capita income was significantly associated with frailty (OR = 0.44; 95% CI 0.1-0.9; p = 0.04). There was an association between frailty and income, showing that the elderly most at risk of frailty were those with lower income.
Sarcopenia and frailty in elderly trauma patients.
Fairchild, Berry; Webb, Travis P; Xiang, Qun; Tarima, Sergey; Brasel, Karen J
2015-02-01
Sarcopenia describes a loss of muscle mass and resultant decrease in strength, mobility, and function that can be quantified by CT. We hypothesized that sarcopenia and related frailty characteristics are related to discharge disposition after blunt traumatic injury in the elderly. We reviewed charts of 252 elderly blunt trauma patients who underwent abdominal CT prior to hospital admission. Data for thirteen frailty characteristics were abstracted. Sarcopenia was measured by obtaining skeletal muscle cross-sectional area (CSA) from each patient's psoas major muscle using Slice-O-Matic(®) software. Dispositions were grouped as dependent and independent based on discharge location. χ (2), Fisher's exact, and logistic regression were used to determine factors associated with discharge dependence. Mean age 76 years, 49 % male, median ISS 9.0 (IQR = 8.0-17.0). Discharge destination was independent in 61.5 %, dependent in 29 %, and 9.5 % of patients died. Each 1 cm(2) increase in psoas muscle CSA was associated with a 20 % decrease in dependent living (p elderly trauma patients and can be obtained from the admission CT. Lower psoas muscle CSA is related to loss of independence upon discharge in the elderly. The early availability of this variable during the hospitalization of elderly trauma patients may aid in discharge planning and the transition to dependent living.
Che Jinxing; Wang Jianzhou
2010-01-01
In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.
An Ordered Regression Model to Predict Transit Passengers’ Behavioural Intentions
Oña, J. de; Oña, R. de; Eboli, L.; Forciniti, C.; Mazzulla, G.
2016-07-01
Passengers’ behavioural intentions after experiencing transit services can be viewed as signals that show if a customer continues to utilise a company’s service. Users’ behavioural intentions can depend on a series of aspects that are difficult to measure directly. More recently, transit passengers’ behavioural intentions have been just considered together with the concepts of service quality and customer satisfaction. Due to the characteristics of the ways for evaluating passengers’ behavioural intentions, service quality and customer satisfaction, we retain that this kind of issue could be analysed also by applying ordered regression models. This work aims to propose just an ordered probit model for analysing service quality factors that can influence passengers’ behavioural intentions towards the use of transit services. The case study is the LRT of Seville (Spain), where a survey was conducted in order to collect the opinions of the passengers about the existing transit service, and to have a measure of the aspects that can influence the intentions of the users to continue using the transit service in the future. (Author)
Heterogeneous Breast Phantom Development for Microwave Imaging Using Regression Models
Camerin Hahn
2012-01-01
Full Text Available As new algorithms for microwave imaging emerge, it is important to have standard accurate benchmarking tests. Currently, most researchers use homogeneous phantoms for testing new algorithms. These simple structures lack the heterogeneity of the dielectric properties of human tissue and are inadequate for testing these algorithms for medical imaging. To adequately test breast microwave imaging algorithms, the phantom has to resemble different breast tissues physically and in terms of dielectric properties. We propose a systematic approach in designing phantoms that not only have dielectric properties close to breast tissues but also can be easily shaped to realistic physical models. The approach is based on regression model to match phantom's dielectric properties with the breast tissue dielectric properties found in Lazebnik et al. (2007. However, the methodology proposed here can be used to create phantoms for any tissue type as long as ex vivo, in vitro, or in vivo tissue dielectric properties are measured and available. Therefore, using this method, accurate benchmarking phantoms for testing emerging microwave imaging algorithms can be developed.
Collard, R M; Arts, M H L; Schene, A H; Naarding, P; Oude Voshaar, R C; Comijs, H C
2017-06-01
Physical frailty and depressive symptoms are reciprocally related in community-based studies, but its prognostic impact on depressive disorder remains unknown. A cohort of 378 older persons (≥60 years) suffering from a depressive disorder (DSM-IV criteria) was reassessed at two-year follow-up. Depressive symptom severity was assessed every six months with the Inventory of Depressive Symptomatology, including a mood, motivational, and somatic subscale. Frailty was assessed according to the physical frailty phenotype at the baseline examination. For each additional frailty component, the odds of non-remission was 1.24 [95% CI=1.01-1.52] (P=040). Linear mixed models showed that only improvement of the motivational (Pdepression. Since only improvement of mood symptoms was independent of frailty severity, one may hypothesize that frailty and residual depression are easily mixed-up in psychiatric treatment. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
Frailty syndrome and skeletal muscle: results from the Invecchiare in Chianti study.
Cesari, Matteo; Leeuwenburgh, Christiaan; Lauretani, Fulvio; Onder, Graziano; Bandinelli, Stefania; Maraldi, Cinzia; Guralnik, Jack M; Pahor, Marco; Ferrucci, Luigi
2006-05-01
Frailty is a common condition in elders and identifies a state of vulnerability for adverse health outcomes. Our objective was to provide a biological face validity to the well-established definition of frailty proposed by Fried et al. Data are from the baseline evaluation of 923 participants aged > or =65 y enrolled in the Invecchiare in Chianti study. Frailty was defined by the presence of > or =3 of the following criteria: weight loss, exhaustion, low walking speed, low hand grip strength, and physical inactivity. Muscle density and the ratios of muscle area and fat area to total calf area were measured by using a peripheral quantitative computerized tomography (pQCT) scan. Analyses of covariance and logistic regressions were performed to evaluate the relations between frailty and pQCT measures. The mean age (+/-SD) of the study sample was 74.8 +/- 6.8 y, and 81 participants (8.8%) had > or =3 frailty criteria. Participants with no frailty criteria had significantly higher muscle density (71.1 mg/cm(3), SE = 0.2) and muscle area (71.2%, SE = 0.4) than did frail participants (69.8 mg/cm(3), SE = 0.4; and 68.7%, SE = 1.1, respectively). Fat area was significantly higher in frail participants (22.0%, SE = 0.9) than in participants with no frailty criteria (20.3%, SE = 0.4). Physical inactivity and low walking speed were the frailty criteria that showed the strongest associations with pQCT measures. Frail subjects, identified by an easy and inexpensive frailty score, have lower muscle density and muscle mass and higher fat mass than do nonfrail persons.
application of multilinear regression analysis in modeling of soil
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Accordingly [1, 3] in their work, they applied linear regression ... (MLRA) is a statistical technique that uses several explanatory ... order to check this, they adopted bivariate correlation analysis .... groups, namely A-1 through A-7, based on their relative expected ..... Multivariate Regression in Gorgan Province North of Iran” ...
Are depression and frailty overlapping syndromes in mid- and late-life? A latent variable analysis.
Mezuk, Briana; Lohman, Matthew; Dumenci, Levent; Lapane, Kate L
2013-06-01
Depression and frailty both predict disability and morbidity in later life. However, it is unclear to what extent these common geriatric syndromes represent overlapping constructs. To examine the joint relationship between the constructs of depression and frailty. Data come from 2004-2005 wave of the Baltimore Epidemiologic Catchment Area Study, and the analysis is limited to participants 40 years and older, with complete data on frailty and depression indicators (N = 683). Depression was measured using the Diagnostic Interview Schedule, and frailty was indexed by modified Fried criteria. A series of confirmatory latent class analyses were used to assess the degree to which depression and frailty syndromes identify the same populations. A latent kappa coefficient (κl) was also estimated between the constructs. Confirmatory latent class analyses indicated that depression and frailty represent distinct syndromes rather than a single construct. The joint modeling of the two constructs supported a three-class solution for depression and two-class solution for frailty, with 2.9% categorized as severely depressed, 19.4% as mildly depressed, and 77.7% as not depressed, and 21.1% categorized as frail and 78.9% as not frail. The chance-corrected agreement statistic indicated moderate correspondence between the depression and frailty constructs (κl: 66, 95% confidence interval: 0.58-0.74). Results suggest that depression and frailty are interrelated concepts, yet their operational criteria identify substantively overlapping subpopulations. These findings have implications for understanding factors that contribute to the etiology and prognosis of depression and frailty in later life. Copyright © 2013 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.
Missov, Trifon I.; Schöley, Jonas
to this criterion admissible distributions are, for example, the gamma, the beta, the truncated normal, the log-logistic and the Weibull, while distributions like the log-normal and the inverse Gaussian do not satisfy this condition. In this article we show that models with admissible frailty distributions...... and a Gompertz baseline provide a better fit to adult human mortality data than the corresponding models with non-admissible frailty distributions. We implement estimation procedures for mixture models with a Gompertz baseline and frailty that follows a gamma, truncated normal, log-normal, or inverse Gaussian...
Wheat flour dough Alveograph characteristics predicted by Mixolab regression models.
Codină, Georgiana Gabriela; Mironeasa, Silvia; Mironeasa, Costel; Popa, Ciprian N; Tamba-Berehoiu, Radiana
2012-02-01
In Romania, the Alveograph is the most used device to evaluate the rheological properties of wheat flour dough, but lately the Mixolab device has begun to play an important role in the breadmaking industry. These two instruments are based on different principles but there are some correlations that can be found between the parameters determined by the Mixolab and the rheological properties of wheat dough measured with the Alveograph. Statistical analysis on 80 wheat flour samples using the backward stepwise multiple regression method showed that Mixolab values using the ‘Chopin S’ protocol (40 samples) and ‘Chopin + ’ protocol (40 samples) can be used to elaborate predictive models for estimating the value of the rheological properties of wheat dough: baking strength (W), dough tenacity (P) and extensibility (L). The correlation analysis confirmed significant findings (P 0.70 for P, R²(adjusted) > 0.70 for W and R²(adjusted) > 0.38 for L, at a 95% confidence interval. Copyright © 2011 Society of Chemical Industry.
Application of regression model on stream water quality parameters
Suleman, M.; Maqbool, F.; Malik, A.H.; Bhatti, Z.A.
2012-01-01
Statistical analysis was conducted to evaluate the effect of solid waste leachate from the open solid waste dumping site of Salhad on the stream water quality. Five sites were selected along the stream. Two sites were selected prior to mixing of leachate with the surface water. One was of leachate and other two sites were affected with leachate. Samples were analyzed for pH, water temperature, electrical conductivity (EC), total dissolved solids (TDS), Biological oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO) and total bacterial load (TBL). In this study correlation coefficient r among different water quality parameters of various sites were calculated by using Pearson model and then average of each correlation between two parameters were also calculated, which shows TDS and EC and pH and BOD have significantly increasing r value, while temperature and TDS, temp and EC, DO and BL, DO and COD have decreasing r value. Single factor ANOVA at 5% level of significance was used which shows EC, TDS, TCL and COD were significantly differ among various sites. By the application of these two statistical approaches TDS and EC shows strongly positive correlation because the ions from the dissolved solids in water influence the ability of that water to conduct an electrical current. These two parameters significantly vary among 5 sites which are further confirmed by using linear regression. (author)
The microcomputer scientific software series 2: general linear model--regression.
Harold M. Rauscher
1983-01-01
The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...
Hogan David B
2012-09-01
Full Text Available Abstract Background Few studies have directly compared the competing approaches to identifying frailty in more vulnerable older populations. We examined the ability of two versions of a frailty index (43 vs. 83 items, the Cardiovascular Health Study (CHS frailty criteria, and the CHESS scale to accurately predict the occurrence of three outcomes among Assisted Living (AL residents followed over one year. Methods The three frailty measures and the CHESS scale were derived from assessment items completed among 1,066 AL residents (aged 65+ participating in the Alberta Continuing Care Epidemiological Studies (ACCES. Adjusted risks of one-year mortality, hospitalization and long-term care placement were estimated for those categorized as frail or pre-frail compared with non-frail (or at high/intermediate vs. low risk on CHESS. The area under the ROC curve (AUC was calculated for select models to assess the predictive accuracy of the different frailty measures and CHESS scale in relation to the three outcomes examined. Results Frail subjects defined by the three approaches and those at high risk for decline on CHESS showed a statistically significant increased risk for death and long-term care placement compared with those categorized as either not frail or at low risk for decline. The risk estimates for hospitalization associated with the frailty measures and CHESS were generally weaker with one of the frailty indices (43 items showing no significant association. For death and long-term care placement, the addition of frailty (however derived or CHESS significantly improved on the AUC obtained with a model including only age, sex and co-morbidity, though the magnitude of improvement was sometimes small. The different frailty/risk models did not differ significantly from each other in predicting mortality or hospitalization; however, one of the frailty indices (83 items showed significantly better performance over the other measures in predicting long
Moffatt H
2018-05-01
Full Text Available Heather Moffatt,1 Paige Moorhouse,1,2 Laurie Mallery,1,2 David Landry,1 Karthik Tennankore2 1Nova Scotia Health Authority, Halifax, NS, Canada; 2Dalhousie University, Halifax, NS, CanadaPurpose: Recent evidence supports the prognostic significance of frailty for functional decline and poor health outcomes in patients with chronic kidney disease. Yet, despite the development of clinical tools to screen for frailty, little is known about the experiential impact of screening for frailty in this setting. The Frailty Assessment for Care Planning Tool (FACT evaluates frailty across 4 domains: mobility, function, social circumstances, and cognition. The purpose of this qualitative study was as follows: 1 explore the nurse experience of screening for frailty using the FACT tool in a specialized outpatient renal clinic; 2 determine how, if at all, provider perceptions of frailty changed after implementation of the frailty screening tool; and 3 determine the perceived factors that influence uptake and administration of the FACT screening tool in a specialized clinical setting.Methods: A semi-structured interview of 5 nurses from the Nova Scotia Health Authority, Central Zone Renal Clinic was conducted. A grounded theory approach was used to generate thematic categories and analysis models.Results: Four primary themes emerged in the data analysis: “we were skeptical”, “we made it work”, “we learned how”, and “we understand”. As the renal nurses gained a sense of confidence in their ability to implement the FACT tool, initial barriers to implementation were attenuated. Implementation factors – such as realistic goals, clear guidelines, and ongoing training – were important factors for successful uptake of the frailty screening initiative.Conclusion: Nurse participants reported an overall positive experience using the FACT method to screen for frailty and indicated that their understanding of the multiple dimensions and subtleties of
MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION
Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...
Methods of Detecting Outliers in A Regression Analysis Model ...
PROF. O. E. OSUAGWU
2013-06-01
Jun 1, 2013 ... especially true in observational studies .... Simple linear regression and multiple ... The simple linear ..... Grubbs,F.E (1950): Sample Criteria for Testing Outlying observations: Annals of ... In experimental design, the Relative.
231 Using Multiple Regression Analysis in Modelling the Role of ...
User
of Internal Revenue, Tourism Bureau and hotel records. The multiple regression .... additional guest facilities such as restaurant, a swimming pool or child care and social function ... and provide good quality service to the public. Conclusion.
Song, Chao; Kwan, Mei-Po; Zhu, Jiping
2017-04-08
An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale.
Azarang, Leyla; Scheike, Thomas; de Uña-Álvarez, Jacobo
2017-01-01
In this work, we present direct regression analysis for the transition probabilities in the possibly non-Markov progressive illness–death model. The method is based on binomial regression, where the response is the indicator of the occupancy for the given state along time. Randomly weighted score...
A logistic regression model for Ghana National Health Insurance claims
Samuel Antwi
2013-07-01
Full Text Available In August 2003, the Ghanaian Government made history by implementing the first National Health Insurance System (NHIS in Sub-Saharan Africa. Within three years, over half of the country’s population had voluntarily enrolled into the National Health Insurance Scheme. This study had three objectives: 1 To estimate the risk factors that influences the Ghana national health insurance claims. 2 To estimate the magnitude of each of the risk factors in relation to the Ghana national health insurance claims. In this work, data was collected from the policyholders of the Ghana National Health Insurance Scheme with the help of the National Health Insurance database and the patients’ attendance register of the Koforidua Regional Hospital, from 1st January to 31st December 2011. Quantitative analysis was done using the generalized linear regression (GLR models. The results indicate that risk factors such as sex, age, marital status, distance and length of stay at the hospital were important predictors of health insurance claims. However, it was found that the risk factors; health status, billed charges and income level are not good predictors of national health insurance claim. The outcome of the study shows that sex, age, marital status, distance and length of stay at the hospital are statistically significant in the determination of the Ghana National health insurance premiums since they considerably influence claims. We recommended, among other things that, the National Health Insurance Authority should facilitate the institutionalization of the collection of appropriate data on a continuous basis to help in the determination of future premiums.
Keränen, Niina Susanna; Kangas, Maarit; Immonen, Milla; Similä, Heidi; Enwald, Heidi; Korpelainen, Raija; Jämsä, Timo
2017-02-14
Use of information and communication technologies (ICT) among seniors is increasing; however, studies on the use of ICT by seniors at the highest risk of health impairment are lacking. Frail and prefrail seniors are a group that would likely benefit from preventive nutrition and exercise interventions, both of which can take advantage of ICT. The objective of the study was to quantify the differences in ICT use, attitudes, and reasons for nonuse among physically frail, prefrail, and nonfrail home-dwelling seniors. This was a population-based questionnaire study on people aged 65-98 years living in Northern Finland. A total of 794 eligible individuals responded out of a contacted random sample of 1500. In this study, 29.8% (237/794) of the respondents were classified as frail or prefrail. The ICT use of frail persons was lower than that of the nonfrail ones. In multivariable logistic regression analysis, age and education level were associated with both the use of Internet and advanced mobile ICT such as smartphones or tablets. Controlling for age and education, frailty or prefrailty was independently related to the nonuse of advanced mobile ICT (odds ratio, OR=0.61, P=.01), and frailty with use of the Internet (OR=0.45, P=.03). The frail or prefrail ICT nonusers also held the most negative opinions on the usefulness or usability of mobile ICT. When opinion variables were included in the model, frailty status remained a significant predictor of ICT use. Physical frailty status is associated with older peoples' ICT use independent of age, education, and opinions on ICT use. This should be taken into consideration when designing preventive and assistive technologies and interventions for older people at risk of health impairment. ©Niina Susanna Keränen, Maarit Kangas, Milla Immonen, Heidi Similä, Heidi Enwald, Raija Korpelainen, Timo Jämsä. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 14.02.2017.
Guerard, Emily J; Deal, Allison M; Chang, YunKyung; Williams, Grant R; Nyrop, Kirsten A; Pergolotti, Mackenzi; Muss, Hyman B; Sanoff, Hanna K; Lund, Jennifer L
2017-07-01
Background: An objective measure is needed to identify frail older adults with cancer who are at increased risk for poor health outcomes. The primary objective of this study was to develop a frailty index from a cancer-specific geriatric assessment (GA) and evaluate its ability to predict all-cause mortality among older adults with cancer. Patients and Methods: Using a unique and novel data set that brings together GA data with cancer-specific and long-term mortality data, we developed the Carolina Frailty Index (CFI) from a cancer-specific GA based on the principles of deficit accumulation. CFI scores (range, 0-1) were categorized as robust (0-0.2), pre-frail (0.2-0.35), and frail (>0.35). The primary outcome for evaluating predictive validity was all-cause mortality. The Kaplan-Meier method and log-rank tests were used to compare survival between frailty groups, and Cox proportional hazards regression models were used to evaluate associations. Results: In our sample of 546 older adults with cancer, the median age was 72 years, 72% were women, 85% were white, and 47% had a breast cancer diagnosis. Overall, 58% of patients were robust, 24% were pre-frail, and 18% were frail. The estimated 5-year survival rate was 72% in robust patients, 58% in pre-frail patients, and 34% in frail patients (log-rank test, P older adults with cancer, a finding that was independent of age, sex, cancer type and stage, and number of medical comorbidities. The CFI has the potential to become a tool that oncologists can use to objectively identify frailty in older adults with cancer. Copyright © 2017 by the National Comprehensive Cancer Network.
A generalized additive regression model for survival times
Scheike, Thomas H.
2001-01-01
Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models......Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models...
Frailty and Risk Classification for Life Annuity Portfolios
Annamaria Olivieri
2016-10-01
Full Text Available Life annuities are attractive mainly for healthy people. In order to expand their business, in recent years, some insurers have started offering higher annuity rates to those whose health conditions are critical. Life annuity portfolios are then supposed to become larger and more heterogeneous. With respect to the insurer’s risk profile, there is a trade-off between portfolio size and heterogeneity that we intend to investigate. In performing this, there is a second and possibly more important issue that we address. In actuarial practice, the different mortality levels of the several risk classes are obtained by applying adjustment coefficients to population mortality rates. Such a choice is not supported by a rigorous model. On the other hand, the heterogeneity of a population with respect to mortality can formally be described with a frailty model. We suggest adopting a frailty model for risk classification. We identify risk groups (or classes within the population by assigning specific ranges of values to the frailty within each group. The different levels of mortality of the various groups are based on the conditional probability distributions of the frailty. Annuity rates for each class then can be easily justified, and a comprehensive investigation of insurer’s liabilities can be performed.
Faraway, Julian J
2005-01-01
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...
A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs
Karabatsos, George; Walker, Stephen G.
2013-01-01
The regression discontinuity (RD) design (Thistlewaite & Campbell, 1960; Cook, 2008) provides a framework to identify and estimate causal effects from a non-randomized design. Each subject of a RD design is assigned to the treatment (versus assignment to a non-treatment) whenever her/his observed value of the assignment variable equals or…
Parametric vs. Nonparametric Regression Modelling within Clinical Decision Support
Kalina, Jan; Zvárová, Jana
2017-01-01
Roč. 5, č. 1 (2017), s. 21-27 ISSN 1805-8698 R&D Projects: GA ČR GA17-01251S Institutional support: RVO:67985807 Keywords : decision support systems * decision rules * statistical analysis * nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Statistics and probability
Nataša Šarlija
2017-01-01
Full Text Available This study sheds light on the most common issues related to applying logistic regression in prediction models for company growth. The purpose of the paper is 1 to provide a detailed demonstration of the steps in developing a growth prediction model based on logistic regression analysis, 2 to discuss common pitfalls and methodological errors in developing a model, and 3 to provide solutions and possible ways of overcoming these issues. Special attention is devoted to the question of satisfying logistic regression assumptions, selecting and defining dependent and independent variables, using classification tables and ROC curves, for reporting model strength, interpreting odds ratios as effect measures and evaluating performance of the prediction model. Development of a logistic regression model in this paper focuses on a prediction model of company growth. The analysis is based on predominantly financial data from a sample of 1471 small and medium-sized Croatian companies active between 2009 and 2014. The financial data is presented in the form of financial ratios divided into nine main groups depicting following areas of business: liquidity, leverage, activity, profitability, research and development, investing and export. The growth prediction model indicates aspects of a business critical for achieving high growth. In that respect, the contribution of this paper is twofold. First, methodological, in terms of pointing out pitfalls and potential solutions in logistic regression modelling, and secondly, theoretical, in terms of identifying factors responsible for high growth of small and medium-sized companies.
Is Delirium the Cognitive Harbinger of Frailty in Older Adults? A Review about the Existing Evidence
Giuseppe Bellelli
2017-11-01
Full Text Available Frailty is a clinical syndrome defined by the age-related depletion of the individual’s homeostatic reserves, determining an increased susceptibility to stressors and disproportionate exposure to negative health changes. The physiological systems that are involved in the determination of frailty are mutually interrelated, so that when decline starts in a given system, implications may also regard the other systems. Indeed, it has been shown that the number of abnormal systems is more predictive of frailty than those of the abnormalities in any particular system. Delirium is a transient neurocognitive disorder, characterized by an acute onset and fluctuating course, inattention, cognitive dysfunction, and behavioral abnormalities, that complicates one out of five hospital admissions. Delirium is independently associated with the same negative outcomes of frailty and, like frailty, its pathogenesis is usually multifactorial, depending on complex inter-relationships between predisposing and precipitating factors. By definition, a somatic cause should be identified, or at least suspected, to diagnose delirium. Delirium and frailty potentially share multiple pathophysiologic mechanisms and pathways, meaning that they could be thought of as the two sides to the same coin. This review aims at summarizing the existing evidence, referring both to human and animal models, to postulate that delirium may represent the cognitive harbinger of a state of frailty in older persons experiencing an acute clinical event.
Shimura, Tetsuro; Yamamoto, Masanori; Kano, Seiji; Kagase, Ai; Kodama, Atsuko; Koyama, Yutaka; Otsuka, Toshiaki; Kohsaka, Shun; Tada, Norio; Yamanaka, Futoshi; Naganuma, Toru; Araki, Motoharu; Shirai, Shinichi; Mizutani, Kazuki; Tabata, Minoru; Ueno, Hiroshi; Takagi, Kensuke; Higashimori, Akihiro; Watanabe, Yusuke; Hayashida, Kentaro
2017-09-01
There are no standardized criteria for measuring patients' frailty. We examined prognosis based on four frailty markers [serum albumin level, grip strength, gait speed, and clinical frailty scale (CFS)] in patients who underwent transcatheter aortic valve replacement (TAVR) between October 2013 and April 2016 and were recorded in the Optimized CathEter vAlvular iNtervention (OCEAN) Japanese multicenter registry. Serum albumin level was assessed by dividing patients into two groups: hypoalbuminemia or non-hypoalbuminemia according to their serum albumin level. Clinical outcomes including all-cause, cardiovascular and non-cardiovascular mortality rates after TAVR were compared. During the follow-up period cumulative all-cause, cardiovascular and non-cardiovascular mortality rates were significantly higher in the hypoalbuminemia group than in the non-hypoalbuminemia group. This result remained unchanged even after a propensity-matched model was used in terms of cumulative all-cause and non-cardiovascular mortality; however, differences in cardiovascular mortality rates were attenuated. To consider the impact of grip strength patients were divided into a low or high peak grip strength group based on classification and regression tree (CART) survival analysis. The clinical outcomes for each sex were compared between the two groups. In both sexes the cumulative 1-year mortality rates were significantly different between the two groups. To investigate gait speed patients were classified into two gait speed groups (low or high gait speed group) based on CART survival analysis. Clinical outcomes were compared between the two groups. The cumulative 1-year mortality rate was significantly different between the two gait speed groups. The effect of CFS on prognosis after TAVR was assessed. Patients were categorized into five groups based on the following CFS scores: CFS1-3, CFS4, CFS5, CFS6, and CFS ≥7. We evaluated the relationship between the CFS score and other indicators
Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William
2016-01-01
Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19
Frailty and Lower Urinary Tract Symptoms.
Suskind, Anne M
2017-09-01
The incidence of both frailty and lower urinary tract symptoms, including urinary incontinence, overactive bladder, underactive bladder, and benign prostatic hyperplasia, increases with age. However, our understanding of the relationship between frailty and lower urinary tract symptoms, both in terms of pathophysiology and in terms of the evaluation and management of such symptoms, is greatly lacking. This brief review will summarize definitions and measurement tools associated with frailty and will also review the existing state of the literature on frailty and lower urinary tract symptoms in older individuals.
Soyoung Park
2017-07-01
Full Text Available This study mapped and analyzed groundwater potential using two different models, logistic regression (LR and multivariate adaptive regression splines (MARS, and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m3/d were extracted, and 859 locations (70% were used for model training, whereas the other 365 locations (30% were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.
Semiparametric Mixtures of Regressions with Single-index for Model Based Clustering
Xiang, Sijia; Yao, Weixin
2017-01-01
In this article, we propose two classes of semiparametric mixture regression models with single-index for model based clustering. Unlike many semiparametric/nonparametric mixture regression models that can only be applied to low dimensional predictors, the new semiparametric models can easily incorporate high dimensional predictors into the nonparametric components. The proposed models are very general, and many of the recently proposed semiparametric/nonparametric mixture regression models a...
Semiparametric nonlinear quantile regression model for financial returns
Avdulaj, Krenar; Baruník, Jozef
2017-01-01
Roč. 21, č. 1 (2017), s. 81-97 ISSN 1081-1826 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : copula quantile regression * realized volatility * value-at-risk Subject RIV: AH - Economic s OBOR OECD: Applied Economic s, Econometrics Impact factor: 0.649, year: 2016 http://library.utia.cas.cz/separaty/2017/E/avdulaj-0472346.pdf
Bonellie, Sandra R
2012-10-01
To illustrate the use of regression and logistic regression models to investigate changes over time in size of babies particularly in relation to social deprivation, age of the mother and smoking. Mean birthweight has been found to be increasing in many countries in recent years, but there are still a group of babies who are born with low birthweights. Population-based retrospective cohort study. Multiple linear regression and logistic regression models are used to analyse data on term 'singleton births' from Scottish hospitals between 1994-2003. Mothers who smoke are shown to give birth to lighter babies on average, a difference of approximately 0.57 Standard deviations lower (95% confidence interval. 0.55-0.58) when adjusted for sex and parity. These mothers are also more likely to have babies that are low birthweight (odds ratio 3.46, 95% confidence interval 3.30-3.63) compared with non-smokers. Low birthweight is 30% more likely where the mother lives in the most deprived areas compared with the least deprived, (odds ratio 1.30, 95% confidence interval 1.21-1.40). Smoking during pregnancy is shown to have a detrimental effect on the size of infants at birth. This effect explains some, though not all, of the observed socioeconomic birthweight. It also explains much of the observed birthweight differences by the age of the mother. Identifying mothers at greater risk of having a low birthweight baby as important implications for the care and advice this group receives. © 2012 Blackwell Publishing Ltd.
Association of lead and cadmium exposure with frailty in US older adults
García-Esquinas, Esther, E-mail: esthergge@gmail.com [Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid/ IdiPAZ, Madrid (Spain); CIBER of Epidemiology and Public Health (CIBERESP), Madrid (Spain); Department of Environmental Health Sciences, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD (United States); Navas-Acien, Ana [Department of Environmental Health Sciences, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD (United States); Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD (United States); Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD (United States); Pérez-Gómez, Beatriz [CIBER of Epidemiology and Public Health (CIBERESP), Madrid (Spain); Environmental Epidemiology and Cancer Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid (Spain); Artalejo, Fernando Rodríguez [Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid/ IdiPAZ, Madrid (Spain); CIBER of Epidemiology and Public Health (CIBERESP), Madrid (Spain)
2015-02-15
Background: Environmental lead and cadmium exposure is associated with higher risk of several age-related chronic diseases, including cardiovascular disease, chronic kidney disease and osteoporosis. These diseases may lead to frailty, a geriatric syndrome characterized by diminished physiologic reserve in multiple systems with decreased ability to cope with acute stressors. However, no previous study has evaluated the association between lead or cadmium exposure and frailty. Methods: Cross-sectional study among individuals aged ≥60 years who participated in the third U.S. National Health and Nutrition Examination Survey and had either blood lead (N=5272) or urine cadmium (N=4887) determinations. Frailty was ascertained with a slight modification of the Fried criteria, so that individuals meeting ≥3 of 5 pre-defined criteria (exhaustion, low body weight, low physical activity, weakness and slow walking speed), were considered as frail. The association between lead and cadmium with frailty was evaluated using logistic regression with adjustment for relevant confounders. Results: Median (intertertile range) concentrations of blood lead and urine cadmium were 3.9 µg/dl (2.9–4.9) and 0.62 µg/l (0.41–0.91), respectively. The prevalence of frailty was 7.1%. The adjusted odds ratios (95% confidence interval) of frailty comparing the second and third to the lowest tertile of blood lead were, respectively, 1.40 (0.96–2.04) and 1.75 (1.33–2.31). Lead concentrations were also associated with the frequency of exhaustion, weakness and slowness. The corresponding odds ratios (95% confidence interval) for cadmium were, respectively, 0.97 (0.68–1.39) and 1.55 (1.03–2.32), but this association did not hold after excluding participants with reduced glomerular filtration rate: 0.70 (0.43–1.14) and 1.09 (0.56–2.11), respectively. Conclusions: In the US older adult population, blood lead but not urine cadmium concentrations showed a direct dose
Association of lead and cadmium exposure with frailty in US older adults
García-Esquinas, Esther; Navas-Acien, Ana; Pérez-Gómez, Beatriz; Artalejo, Fernando Rodríguez
2015-01-01
Background: Environmental lead and cadmium exposure is associated with higher risk of several age-related chronic diseases, including cardiovascular disease, chronic kidney disease and osteoporosis. These diseases may lead to frailty, a geriatric syndrome characterized by diminished physiologic reserve in multiple systems with decreased ability to cope with acute stressors. However, no previous study has evaluated the association between lead or cadmium exposure and frailty. Methods: Cross-sectional study among individuals aged ≥60 years who participated in the third U.S. National Health and Nutrition Examination Survey and had either blood lead (N=5272) or urine cadmium (N=4887) determinations. Frailty was ascertained with a slight modification of the Fried criteria, so that individuals meeting ≥3 of 5 pre-defined criteria (exhaustion, low body weight, low physical activity, weakness and slow walking speed), were considered as frail. The association between lead and cadmium with frailty was evaluated using logistic regression with adjustment for relevant confounders. Results: Median (intertertile range) concentrations of blood lead and urine cadmium were 3.9 µg/dl (2.9–4.9) and 0.62 µg/l (0.41–0.91), respectively. The prevalence of frailty was 7.1%. The adjusted odds ratios (95% confidence interval) of frailty comparing the second and third to the lowest tertile of blood lead were, respectively, 1.40 (0.96–2.04) and 1.75 (1.33–2.31). Lead concentrations were also associated with the frequency of exhaustion, weakness and slowness. The corresponding odds ratios (95% confidence interval) for cadmium were, respectively, 0.97 (0.68–1.39) and 1.55 (1.03–2.32), but this association did not hold after excluding participants with reduced glomerular filtration rate: 0.70 (0.43–1.14) and 1.09 (0.56–2.11), respectively. Conclusions: In the US older adult population, blood lead but not urine cadmium concentrations showed a direct dose
Beta Regression Finite Mixture Models of Polarization and Priming
Smithson, Michael; Merkle, Edgar C.; Verkuilen, Jay
2011-01-01
This paper describes the application of finite-mixture general linear models based on the beta distribution to modeling response styles, polarization, anchoring, and priming effects in probability judgments. These models, in turn, enhance our capacity for explicitly testing models and theories regarding the aforementioned phenomena. The mixture…
A generalized exponential time series regression model for electricity prices
Haldrup, Niels; Knapik, Oskar; Proietti, Tomasso
on the estimated model, the best linear predictor is constructed. Our modeling approach provides good fit within sample and outperforms competing benchmark predictors in terms of forecasting accuracy. We also find that building separate models for each hour of the day and averaging the forecasts is a better...
Forecast Model of Urban Stagnant Water Based on Logistic Regression
Liu Pan
2017-01-01
Full Text Available With the development of information technology, the construction of water resource system has been gradually carried out. In the background of big data, the work of water information needs to carry out the process of quantitative to qualitative change. Analyzing the correlation of data and exploring the deep value of data which are the key of water information’s research. On the basis of the research on the water big data and the traditional data warehouse architecture, we try to find out the connection of different data source. According to the temporal and spatial correlation of stagnant water and rainfall, we use spatial interpolation to integrate data of stagnant water and rainfall which are from different data source and different sensors, then use logistic regression to find out the relationship between them.
Parental Vaccine Acceptance: A Logistic Regression Model Using Previsit Decisions.
Lee, Sara; Riley-Behringer, Maureen; Rose, Jeanmarie C; Meropol, Sharon B; Lazebnik, Rina
2017-07-01
This study explores how parents' intentions regarding vaccination prior to their children's visit were associated with actual vaccine acceptance. A convenience sample of parents accompanying 6-week-old to 17-year-old children completed a written survey at 2 pediatric practices. Using hierarchical logistic regression, for hospital-based participants (n = 216), vaccine refusal history ( P < .01) and vaccine decision made before the visit ( P < .05) explained 87% of vaccine refusals. In community-based participants (n = 100), vaccine refusal history ( P < .01) explained 81% of refusals. Over 1 in 5 parents changed their minds about vaccination during the visit. Thirty parents who were previous vaccine refusers accepted current vaccines, and 37 who had intended not to vaccinate choose vaccination. Twenty-nine parents without a refusal history declined vaccines, and 32 who did not intend to refuse before the visit declined vaccination. Future research should identify key factors to nudge parent decision making in favor of vaccination.
Frailty Testing Pilot Study: Pros and Pitfalls.
Adlam, Taylor; Ulrich, Elizabeth; Kent, Missy; Malinzak, Lauren
2018-02-01
Frailty can be defined as an inflammatory state with a loss of physiologic reserve in multiple systems that manifests as a decreased ability to respond to stressors that ultimately leads to an increased risk of adverse outcomes. The aim of this study was to determine the ease of frailty testing in a pre-kidney transplant clinic and the resources required to do so. A secondary goal was to better understand the utility of frailty testing when evaluating potential kidney transplant recipients. Frailty testing was conducted at a pre-kidney transplant clinic in three phases using Fried's frailty phenotype (shrinking, exhaustion, low physical activity, slowness, and grip strength). A total of 132 frailty tests were completed on 128 patients. Frail patients had significantly higher rates of shrinking (26% vs. 8.5%, P testing was most complete when an examiner dedicated to frailty testing performed the testing. Frailty testing is feasible to complete in a pre-transplant clinic with an appropriate investment in personnel and resources.
Drzewiecki Wojciech
2016-12-01
Full Text Available In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques.
Peters, L L; Boter, H; Burgerhof, J G M; Slaets, J P J; Buskens, E
2015-09-01
The primary objective of the present study was to evaluate the validity of the Groningen Frailty Indicator (GFI) in a sample of Dutch elderly persons participating in LifeLines, a large population-based cohort study. Additional aims were to assess differences between frail and non-frail elderly and examine which individual characteristics were associated with frailty. By December 2012, 5712 elderly persons were enrolled in LifeLines and complied with the inclusion criteria of the present study. Mann-Whitney U or Kruskal-Wallis tests were used to assess the variability of GFI-scores among elderly subgroups that differed in demographic characteristics, morbidity, obesity, and healthcare utilization. Within subgroups Kruskal-Wallis tests were also used to examine differences in GFI-scores across age groups. Multivariate logistic regression analyses were performed to assess associations between individual characteristics and frailty. The GFI discriminated between subgroups: statistically significantly higher GFI-median scores (interquartile range) were found in e.g. males (1 [0-2]), the oldest old (2 [1-3]), in elderly who were single (1 [0-2]), with lower socio economic status (1 [0-3]), with increasing co-morbidity (2 [1-3]), who were obese (2 [1-3]), and used more healthcare (2 [1-4]). Overall age had an independent and statistically significant association with GFI scores. Compared with the non-frail, frail elderly persons experienced statistically significantly more chronic stress and more social/psychological related problems. In the multivariate logistic regression model, psychological morbidity had the strongest association with frailty. The present study supports the construct validity of the GFI and provides an insight in the characteristics of (non)frail community-dwelling elderly persons participating in LifeLines. Copyright © 2015 Elsevier Inc. All rights reserved.
Additive Intensity Regression Models in Corporate Default Analysis
Lando, David; Medhat, Mamdouh; Nielsen, Mads Stenbo
2013-01-01
We consider additive intensity (Aalen) models as an alternative to the multiplicative intensity (Cox) models for analyzing the default risk of a sample of rated, nonfinancial U.S. firms. The setting allows for estimating and testing the significance of time-varying effects. We use a variety of mo...
Misspecified poisson regression models for large-scale registry data
Grøn, Randi; Gerds, Thomas A.; Andersen, Per K.
2016-01-01
working models that are then likely misspecified. To support and improve conclusions drawn from such models, we discuss methods for sensitivity analysis, for estimation of average exposure effects using aggregated data, and a semi-parametric bootstrap method to obtain robust standard errors. The methods...
Logistic regression model for detecting radon prone areas in Ireland.
Elío, J; Crowley, Q; Scanlon, R; Hodgson, J; Long, S
2017-12-01
A new high spatial resolution radon risk map of Ireland has been developed, based on a combination of indoor radon measurements (n=31,910) and relevant geological information (i.e. Bedrock Geology, Quaternary Geology, soil permeability and aquifer type). Logistic regression was used to predict the probability of having an indoor radon concentration above the national reference level of 200Bqm -3 in Ireland. The four geological datasets evaluated were found to be statistically significant, and, based on combinations of these four variables, the predicted probabilities ranged from 0.57% to 75.5%. Results show that the Republic of Ireland may be divided in three main radon risk categories: High (HR), Medium (MR) and Low (LR). The probability of having an indoor radon concentration above 200Bqm -3 in each area was found to be 19%, 8% and 3%; respectively. In the Republic of Ireland, the population affected by radon concentrations above 200Bqm -3 is estimated at ca. 460k (about 10% of the total population). Of these, 57% (265k), 35% (160k) and 8% (35k) are in High, Medium and Low Risk Areas, respectively. Our results provide a high spatial resolution utility which permit customised radon-awareness information to be targeted at specific geographic areas. Copyright © 2017 Elsevier B.V. All rights reserved.
Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.
Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko
2016-03-01
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.
Martina Amanzio
2017-11-01
Full Text Available BackgroundRecent studies have suggested that cognitive functions in patients with neurocognitive disorders have a significant role in the pathogenic mechanisms of frailty. Although pre-frailty is considered an intermediate, preclinical state, epidemiological research has begun to dislodge cognition and frailty into their specific subcomponents to understand the relationship among them. We aim to analyse the possible association between pre-frailty and neuropsychological variables to outline which factors can contribute to minor and major neurocognitive disorders.Methods60 subjects complaining of different cognitive deficits underwent a deep-in-wide frailty and neuropsychological assessment. We conducted three multiple linear regression analyses adjusted for a combination of demographic measures and involving several neuropsychological–behavioural parameters selected by the literature on physical frailty.ResultsWe found a significant association between frailty—as measured by the multidimensional prognostic index (MPI—and action monitoring and monetary gain (cognitive domain, depression and disinhibition (behavioural domain. Moreover, an association between MPI and impaired awareness for instrumental activities disabilities exists.ConclusionWe propose a novel framework for understanding frailty associated with metacognitive–executive dysfunction.
Logistic Regression Modeling of Diminishing Manufacturing Sources for Integrated Circuits
Gravier, Michael
1999-01-01
.... This thesis draws on available data from the electronics integrated circuit industry to attempt to assess whether statistical modeling offers a viable method for predicting the presence of DMSMS...
De Roeck, Ellen Elisa; Dury, Sarah; De Witte, Nico; De Donder, Liesbeth; Bjerke, Maria; De Deyn, Peter Paul; Engelborghs, Sebastiaan; Dierckx, Eva
2018-07-01
Cognitive frailty is characterized by the presence of cognitive impairment in exclusion of dementia. In line with other frailty domains, cognitive frailty is associated with negative outcomes. The Comprehensive Frailty Assessment Instrument (CFAI) measures 4 domains of frailty, namely physical, psychological, social, and environmental frailty. The absence of cognitive frailty is a limitation. An expert panel selected 6 questions from the Informant Questionnaire on Cognitive Decline that were, together with the CFAI and the Montreal cognitive assessment administered to 355 older community dwelling adults (mean age = 77). After multivariate analysis, 2 questions were excluded. All the questions from the original CFAI were implemented in a principal component analysis together with the 4 cognitive questions, showing that the 4 cognitive questions all load on 1 factor, representing the cognitive domain of frailty. By adding the cognitive domain to the CFAI, the reliability of the adapted CFAI (CFAI-Plus), remains good (Cronbach's alpha: .767). This study showed that cognitive frailty can be added to the CFAI without affecting its good psychometric properties. In the future, the CFAI-Plus needs to be validated in an independent cohort, and the interaction with the other frailty domains needs to be studied. Copyright © 2018 John Wiley & Sons, Ltd.
Long-term Associations Between Physical Frailty and Performance in Specific Cognitive Domains.
Bunce, David; Batterham, Philip J; Mackinnon, Andrew J
2018-02-01
No longitudinal epidemiological research has reported associations between physical frailty and performance in specific cognitive domains. Our aim was to investigate whether such associations existed in the absence of accompanying neurodegenerative disorders such as mild cognitive impairment (MCI) and dementia. We addressed this issue in a population-based sample of 896 adults aged 70 years and older over 4 waves of data covering a 12-year period. Physical frailty was assessed and a cognitive battery included measures of processing speed, verbal fluency, face and word recognition, episodic memory and simple and choice reaction time (RT). Latent growth models showed frailty was associated with poorer baseline performance in processing speed, verbal fluency, simple and choice RT, and choice intraindividual RT variability. However, no significant effects of frailty on slopes of cognition were observed, suggesting that frailty was not associated with cognitive decline. Importantly, when the models took possible dementia into account, significant effects were retained suggesting that differences were not associated with dementia-related neurodegenerative disorders. The findings suggest that frailty-related cognitive deficits may exist independently of mechanisms underpinning neurodegenerative disorders such as MCI and dementia. If confirmed, this finding suggests a new avenue for preventative and therapeutic interventions in clinical and public health contexts for older adults. © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
U.S. Environmental Protection Agency — Spreadsheets are included here to support the manuscript "Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition". This...
Martingale Regressions for a Continuous Time Model of Exchange Rates
Guo, Zi-Yi
2017-01-01
One of the daunting problems in international finance is the weak explanatory power of existing theories of the nominal exchange rates, the so-called “foreign exchange rate determination puzzle”. We propose a continuous-time model to study the impact of order flow on foreign exchange rates. The model is estimated by a newly developed econometric tool based on a time-change sampling from calendar to volatility time. The estimation results indicate that the effect of order flow on exchange rate...
Nina T Rogers
Full Text Available Frail older adults are heavy users of health and social care. In order to reduce the costs associated with frailty in older age groups, safe and cost-effective strategies are required that will reduce the incidence and severity of frailty.We investigated whether self-reported intensity of physical activity (sedentary, mild, moderate or vigorous performed at least once a week can significantly reduce trajectories of frailty in older adults who are classified as non-frail at baseline (Rockwood's Frailty Index [FI] ≤ 0.25.Multi-level growth curve modelling was used to assess trajectories of frailty in 8649 non-frail adults aged 50 and over and according to baseline self-reported intensity of physical activity. Frailty was measured in five-year age cohorts based on age at baseline (50-54; 55-59; 60-64; 65-69; 70-74; 75-79; 80+ on up to 6 occasions, providing an average of 10 years of follow-up. All models were adjusted for baseline sex, education, wealth, cohabitation, smoking, and alcohol consumption.Compared with the sedentary reference group, mild physical activity was insufficient to significantly slow the progression of frailty, moderate physical activity reduced the progression of frailty in some age groups (particularly ages 65 and above and vigorous activity significantly reduced the trajectory of frailty progression in all older adults.Healthy non-frail older adults require higher intensities of physical activity for continued improvement in frailty trajectories.
Focused information criterion and model averaging based on weighted composite quantile regression
Xu, Ganggang; Wang, Suojin; Huang, Jianhua Z.
2013-01-01
We study the focused information criterion and frequentist model averaging and their application to post-model-selection inference for weighted composite quantile regression (WCQR) in the context of the additive partial linear models. With the non
Cox's regression model for dynamics of grouped unemployment data
Volf, Petr
2003-01-01
Roč. 10, č. 19 (2003), s. 151-162 ISSN 1212-074X R&D Projects: GA ČR GA402/01/0539 Institutional research plan: CEZ:AV0Z1075907 Keywords : mathematical statistics * survival analysis * Cox's model Subject RIV: BB - Applied Statistics, Operational Research
Multiple Linear Regression Model for Estimating the Price of a ...
Ghana Mining Journal ... In the modeling, the Ordinary Least Squares (OLS) normality assumption which could introduce errors in the statistical analyses was dealt with by log transformation of the data, ensuring the data is normally ... The resultant MLRM is: Ŷi MLRM = (X'X)-1X'Y(xi') where X is the sample data matrix.
Inflation, Forecast Intervals and Long Memory Regression Models
C.S. Bos (Charles); Ph.H.B.F. Franses (Philip Hans); M. Ooms (Marius)
2001-01-01
textabstractWe examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading
Inflation, Forecast Intervals and Long Memory Regression Models
Ooms, M.; Bos, C.S.; Franses, P.H.
2003-01-01
We examine recursive out-of-sample forecasting of monthly postwar US core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators
Data-driven modelling of LTI systems using symbolic regression
Khandelwal, D.; Toth, R.; Van den Hof, P.M.J.
2017-01-01
The aim of this project is to automate the task of data-driven identification of dynamical systems. The underlying goal is to develop an identification tool that models a physical system without distinguishing between classes of systems such as linear, nonlinear or possibly even hybrid systems. Such
Frailty and sarcopenia in Bogotá: results from the SABE Bogotá Study.
Samper-Ternent, Rafael; Reyes-Ortiz, Carlos; Ottenbacher, Kenneth J; Cano, Carlos A
2017-04-01
Latin American countries like Colombia are experiencing a unique aging process due to a mixed epidemiological regime of communicable and non-communicable diseases. To estimate the prevalence of frailty and sarcopenia among older adults in Colombia and identify variables associated with these conditions. Data come from the "Salud Bienestar y Envejecimiento" (SABE) Bogotá Study, a cross-sectional study conducted in 2012 in Bogotá, Colombia. Sociodemographic, health, cognitive and anthropometric measures were collected from 2000 community-dwelling adults aged 60 years and older. Frailty variable was created using the Fried phenotype and sarcopenia following the European Working Group on Sarcopenia in Older People algorithm. Logistic regression analyses were used to identify factors associated with frailty and sarcopenia. A total of 135 older adults are frail (9.4 %), while 166 have sarcopenia (11.5 %). Older age and female gender have a significant association with both conditions (Frailty: Age OR 1.05, 95 % CI 1.03-1.06, Gender OR 1.44, 95 % CI 1.12-1.84; Sarcopenia: Age 1.04, 95 % CI 1.02-1.07, Gender OR 1.51, 95 % CI 1.05-2.17). Depression was also significantly associated with frailty (OR 1.17, 95 % CI 1.12-1.22), while smoking was significantly associated with sarcopenia (OR 2.38, 95 % CI 1.29-4.37). Finally, higher function, measured by independence in IADL (Instrumental Activities of Daily Living) was significantly associated with less frailty (OR 0.74, 95 % CI 0.64-0.86). Education, higher number of comorbidities, better MMSE score, activities of daily living disability and alcohol consumption were not significantly associated with frailty or sarcopenia. Frailty, sarcopenia and multimorbidity are overlapping, yet distinct conditions in this sample. There are potentially reversible factors that are associated with frailty and sarcopenia in this sample. Future studies need to analyze the best way to prevent these conditions, and examine individuals
Nonparametric Estimation of Regression Parameters in Measurement Error Models
Ehsanes Saleh, A.K.M.D.; Picek, J.; Kalina, Jan
2009-01-01
Roč. 67, č. 2 (2009), s. 177-200 ISSN 0026-1424 Grant - others:GA AV ČR(CZ) IAA101120801; GA MŠk(CZ) LC06024 Institutional research plan: CEZ:AV0Z10300504 Keywords : asymptotic relative efficiency(ARE) * asymptotic theory * emaculate mode * Me model * R-estimation * Reliabilty ratio(RR) Subject RIV: BB - Applied Statistics, Operational Research
Gale, C R; Cooper, C; Deary, I J; Aihie Sayer, A
2014-03-01
Observations that older people who enjoy life more tend to live longer suggest that psychological well-being may be a potential resource for healthier ageing. We investigated whether psychological well-being was associated with incidence of physical frailty. We used multinomial logistic regression to examine the prospective relationship between psychological well-being, assessed using the CASP-19, a questionnaire that assesses perceptions of control, autonomy, self-realization and pleasure, and incidence of physical frailty or pre-frailty, defined according to the Fried criteria (unintentional weight loss, weakness, self-reported exhaustion, slow walking speed and low physical activity), in 2557 men and women aged 60 to ≥ 90 years from the English Longitudinal Study of Ageing (ELSA). Men and women with higher levels of psychological well-being were less likely to become frail over the 4-year follow-up period. For a standard deviation higher score in psychological well-being at baseline, the relative risk ratio (RR) for incident frailty, adjusted for age, sex and baseline frailty status, was 0.46 [95% confidence interval (CI) 0.40-0.54]. There was a significant association between psychological well-being and risk of pre-frailty (RR 0.69, 95% CI 0.63-0.77). Examination of scores for hedonic (pleasure) and eudaimonic (control, autonomy and self-realization) well-being showed that higher scores on both were associated with decreased risk. Associations were partially attenuated by further adjustment for other potential confounding factors but persisted. Incidence of pre-frailty or frailty was associated with a decline in well-being, suggesting that the relationship is bidirectional. Maintaining a stronger sense of psychological well-being in later life may protect against the development of physical frailty. Future research needs to establish the mechanisms underlying these findings.
Dual-Task Performance: Influence of Frailty, Level of Physical Activity, and Cognition.
Giusti Rossi, Paulo; Pires de Andrade, Larissa; Hotta Ansai, Juliana; Silva Farche, Ana Claudia; Carnaz, Leticia; Dalpubel, Daniela; Ferriolli, Eduardo; Assis Carvalho Vale, Francisco; de Medeiros Takahashi, Anielle Cristhine
2018-03-08
Cognition and level of physical activity have been associated with frailty syndrome. The development of tools that assess deficits related to physical and cognitive frailties simultaneously are of common interest. However, little is known about how much these aspects influence the performance of dual-task tests. Our aims were (a) to verify the influence of frailty syndrome and objectively measured physical activity and cognition on the Timed Up and Go (TUG) test and Timed Up and Go associated with dual-task (TUG-DT) performances; and (b) to compare TUG and TUG-DT performances between older adults who develop frailty syndrome. Sixty-four community-dwelling older adults were divided into frail, prefrail, and nonfrail groups, according to frailty phenotype. Assessments included anamnesis, screening of frailty syndrome, cognitive assessment (Addenbrooke's cognitive examination), placement of a triaxial accelerometer to assess level of physical activity, and TUG and TUG-DT (TUG associated with a motor-cognitive task of calling a phone number) performances. After 7 days, the accelerometer was removed. A multiple linear regression was applied to identify which independent variables could explain performances in the TUG and TUG-DT. Subsequently, the analysis of covariance test, adjusted for age, cognition, and level of physical activity covariates, was used to compare test performances. There were no differences in cognition between groups. Significant differences in the level of physical activity were found in the frail group. Compared with the frail group, the nonfrail group required less time and fewer steps to complete the TUG. Regarding the TUG-DT, cognition and age influenced the time spent and number of steps, respectively; however, no differences were found between groups. Frail older adults presented worse performance in the TUG when compared with nonfrail older adults. The dual-task test does not differentiate older adults with frailty syndrome, regardless of
Shaofu Zhuyu Decoction Regresses Endometriotic Lesions in a Rat Model
Guanghui Zhu
2018-01-01
Full Text Available The current therapies for endometriosis are restricted by various side effects and treatment outcome has been less than satisfactory. Shaofu Zhuyu Decoction (SZD, a classic traditional Chinese medicinal (TCM prescription for dysmenorrhea, has been widely used in clinical practice by TCM doctors to relieve symptoms of endometriosis. The present study aimed to investigate the effects of SZD on a rat model of endometriosis. Forty-eight female Sprague-Dawley rats with regular estrous cycles went through autotransplantation operation to establish endometriosis model. Then 38 rats with successful ectopic implants were randomized into two groups: vehicle- and SZD-treated groups. The latter were administered SZD through oral gavage for 4 weeks. By the end of the treatment period, the volume of the endometriotic lesions was measured, the histopathological properties of the ectopic endometrium were evaluated, and levels of proliferating cell nuclear antigen (PCNA, CD34, and hypoxia inducible factor- (HIF- 1α in the ectopic endometrium were detected with immunohistochemistry. Furthermore, apoptosis was assessed using the terminal deoxynucleotidyl transferase (TdT deoxyuridine 5′-triphosphate (dUTP nick-end labeling (TUNEL assay. In this study, SZD significantly reduced the size of ectopic lesions in rats with endometriosis, inhibited cell proliferation, increased cell apoptosis, and reduced microvessel density and HIF-1α expression. It suggested that SZD could be an effective therapy for the treatment and prevention of endometriosis recurrence.
[Application of detecting and taking overdispersion into account in Poisson regression model].
Bouche, G; Lepage, B; Migeot, V; Ingrand, P
2009-08-01
Researchers often use the Poisson regression model to analyze count data. Overdispersion can occur when a Poisson regression model is used, resulting in an underestimation of variance of the regression model parameters. Our objective was to take overdispersion into account and assess its impact with an illustration based on the data of a study investigating the relationship between use of the Internet to seek health information and number of primary care consultations. Three methods, overdispersed Poisson, a robust estimator, and negative binomial regression, were performed to take overdispersion into account in explaining variation in the number (Y) of primary care consultations. We tested overdispersion in the Poisson regression model using the ratio of the sum of Pearson residuals over the number of degrees of freedom (chi(2)/df). We then fitted the three models and compared parameter estimation to the estimations given by Poisson regression model. Variance of the number of primary care consultations (Var[Y]=21.03) was greater than the mean (E[Y]=5.93) and the chi(2)/df ratio was 3.26, which confirmed overdispersion. Standard errors of the parameters varied greatly between the Poisson regression model and the three other regression models. Interpretation of estimates from two variables (using the Internet to seek health information and single parent family) would have changed according to the model retained, with significant levels of 0.06 and 0.002 (Poisson), 0.29 and 0.09 (overdispersed Poisson), 0.29 and 0.13 (use of a robust estimator) and 0.45 and 0.13 (negative binomial) respectively. Different methods exist to solve the problem of underestimating variance in the Poisson regression model when overdispersion is present. The negative binomial regression model seems to be particularly accurate because of its theorical distribution ; in addition this regression is easy to perform with ordinary statistical software packages.
Active steps for diabetes: a community-campus partnership addressing frailty and diabetes.
Pariser, Gina; Hager, Kathy; Gillette, Patricia; Golemboski, Karen; Jackson, Kimberly
2014-01-01
The purpose of this study was to examine the effects of Active Steps for Diabetes (ASD), a self-management education (DSME) program for aging adults with diabetes and frailty, on blood glucose control (A1C) and level of frailty of participants. Fifty females (62.2 ± 10.1 years old) with type 2 diabetes and frailty completed the program; 16 used a walking aid. Outcome measures included A1C and the modified Physical Performance Test (mPPT). Repeated measures analysis of variance was used to compare outcomes before and after the program and between participants who did and did not use a walking aid. ASD was effective in reducing A1C and frailty in participants who did and did not use a walking aid. The reduction in A1C was similar for the 2 groups. The reduction in frailty was greater for the group that used a walking aid. Physical activity, a keystone for blood glucose control, is difficult for older adults who are frail. ASD provides a model for DSME that may reduce frailty of participants and increase their capacity for physical activity.
Frail Elders in an Urban District Setting in Malaysia: Multidimensional Frailty and Its Correlates.
Sathasivam, Jeyanthini; Kamaruzzaman, Shahrul Bahyah; Hairi, Farizah; Ng, Chiu Wan; Chinna, Karuthan
2015-11-01
In the past decade, the population in Malaysia has been rapidly ageing. This poses new challenges and issues that threaten the ability of the elderly to independently age in place. A multistage cross-sectional study on 789 community-dwelling elderly individuals aged 60 years and above was conducted in an urban district in Malaysia to assess the geriatric syndrome of frailty. Using a multidimensional frailty index, we detected 67.7% prefrail and 5.7% frail elders. Cognitive status was a significant correlate for frailty status among the respondents as well as those who perceived their health status as very poor or quite poor; but self-rated health was no longer significant when controlled for sociodemographic variables. Lower-body weakness and history of falls were associated with increasing frailty levels, and this association persisted in the multivariate model. This study offers support that physical disability, falls, and cognition are important determinants for frailty. This initial work on frailty among urban elders in Malaysia provides important correlations and identifies potential risk factors that can form the basis of information for targeted preventive measures for this vulnerable group in their prefrail state. © 2015 APJPH.
Lin, Shu-Yu; Lee, Wei-Ju; Chou, Ming-Yueh; Peng, Li-Ning; Chiou, Shu-Ti; Chen, Liang-Kung
2016-01-01
Frailty Index, defined as an individual's accumulated proportion of listed health-related deficits, is a well-established metric used to assess the health status of old adults; however, it has not yet been developed in Taiwan, and its local related structure factors remain unclear. The objectives were to construct a Taiwan Frailty Index to predict mortality risk, and to explore the structure of its factors. Analytic data on 1,284 participants aged 53 and older were excerpted from the Social Environment and Biomarkers of Aging Study (2006), in Taiwan. A consensus workgroup of geriatricians selected 159 items according to the standard procedure for creating a Frailty Index. Cox proportional hazard modeling was used to explore the association between the Taiwan Frailty Index and mortality. Exploratory factor analysis was used to identify structure factors and produce a shorter version-the Taiwan Frailty Index Short-Form. During an average follow-up of 4.3 ± 0.8 years, 140 (11%) subjects died. Compared to those in the lowest Taiwan Frailty Index tertile ( 0.23) had significantly higher risk of death (Hazard ratio: 3.2; 95% CI 1.9-5.4). Thirty-five items of five structure factors identified by exploratory factor analysis, included: physical activities, life satisfaction and financial status, health status, cognitive function, and stresses. Area under the receiver operating characteristic curves (C-statistics) of the Taiwan Frailty Index and its Short-Form were 0.80 and 0.78, respectively, with no statistically significant difference between them. Although both the Taiwan Frailty Index and Short-Form were associated with mortality, the Short-Form, which had similar accuracy in predicting mortality as the full Taiwan Frailty Index, would be more expedient in clinical practice and community settings to target frailty screening and intervention.
Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model
Møller, Niels Framroze
This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its stru....... Further fundamental extensions and advances to more sophisticated theory models, such as those related to dynamics and expectations (in the structural relations) are left for future papers......This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its......, it is demonstrated how other controversial hypotheses such as Rational Expectations can be formulated directly as restrictions on the CVAR-parameters. A simple example of a "Neoclassical synthetic" AS-AD model is also formulated. Finally, the partial- general equilibrium distinction is related to the CVAR as well...
Using the classical linear regression model in analysis of the dependences of conveyor belt life
Miriam Andrejiová
2013-12-01
Full Text Available The paper deals with the classical linear regression model of the dependence of conveyor belt life on some selected parameters: thickness of paint layer, width and length of the belt, conveyor speed and quantity of transported material. The first part of the article is about regression model design, point and interval estimation of parameters, verification of statistical significance of the model, and about the parameters of the proposed regression model. The second part of the article deals with identification of influential and extreme values that can have an impact on estimation of regression model parameters. The third part focuses on assumptions of the classical regression model, i.e. on verification of independence assumptions, normality and homoscedasticity of residuals.
Kusumastuti, Sasmita; Gerds, Thomas Alexander; Lund, Rikke
2017-01-01
OBJECTIVE: To investigate the added value of comorbidity, frailty, and subjective health to mortality predictions in community-dwelling older people and whether it changes with increasing age. PARTICIPANTS: 36,751 community-dwelling subjects aged 50-100 from the longitudinal Survey of Health......, Ageing, and Retirement in Europe. METHODS: Mortality risk associated with Comorbidity Index, Frailty Index, Frailty Phenotype, and subjective health was analysed using Cox regression. The extent to which health indicators modified individual mortality risk predictions was examined and the added ability......, and household income. CONCLUSION: Calendar age encompasses most of the discrimination ability to predict mortality. The added value of comorbidity, frailty, and subjective health to mortality predictions decreases with increasing age....
Frailty in elderly: a brief review.
Tabue-Teguo, Maturin; Simo, Nadine; Gonzalez-Colaço Harmand, Magali; Cesari, Matteo; Avila-Funes, Jose-Alberto; Féart, Catherine; Amiéva, Hélène; Dartigues, Jean-François
2017-06-01
The identification of frail older persons is a public health priority. Frailty is defined as an extreme vulnerability of the organism to endogenous and exogenous stressors, a syndrome that exposes the individual at higher risk of negative health-related outcomes as well as a transition phase between successful aging and disability. The theoretical concept of frailty is largely agreed, its practical translation still presents some limitations due to the existence of multiple tools and operational definition. In this brief review, we would like to clarify the frailty concept regarding scientific literature.
Suhartono, Lee, Muhammad Hisyam; Prastyo, Dedy Dwi
2015-12-01
The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men's jeans and women's trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.
Statistical approach for selection of regression model during validation of bioanalytical method
Natalija Nakov
2014-06-01
Full Text Available The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple non-parametric statistical test provides statistical confirmation of the choice of an adequate regression model.
On a Robust MaxEnt Process Regression Model with Sample-Selection
Hea-Jung Kim
2018-04-01
Full Text Available In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.
Ivanka Jerić
2011-11-01
Full Text Available Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample.
Frailty and geriatric syndromes in elderly assisted in primary health care
Vera Elizabeth Closs
2016-06-01
Full Text Available The aim of this study was to describe the association between frailty and geriatric syndromes (GS [cognitive impairment (CI; postural instability (PI; urinary/fecal incontinence (UFI; polypharmacy (PP; and immobility (IM] and the frequency of these conditions in elderly people assisted in primary health care. Five hundred twenty-one elderly participants of The Multidimensional Study of the Elderly in the Family Health Strategy (EMI-SUS were evaluated. Sociodemographic data, identification of frailty (Fried phenotype and GS were collected. Multinomial logistic regression analysis was performed. The frequency of frailty was 21.5%, prefrailty 51.1% and robustness 27.4%. The frequency of CI was 54.7%, PP 41.2%, PI 36.5%, UFI 14% and IM 5.8%. The odds of frailty when compared to robustness and adjusted for gender, age, depression, self-perception of health, nutritional status, falls, vision and hearing, was significantly higher in elderly with CI, PI and PP. The adjusted odds of prefrail when compared to robustness was significantly higher only in elderly with CI. The most frequently presented number of GS (0-5 was two geriatric syndromes (26.87%. The frequency of frailty was high among elderly in primary health care and was associated with three of five GS (CI - PI - PP.
A generalized right truncated bivariate Poisson regression model with applications to health data.
Islam, M Ataharul; Chowdhury, Rafiqul I
2017-01-01
A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.
Wei, Jiawei; Carroll, Raymond J.; Maity, Arnab
2011-01-01
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work
Koon, Sharon; Petscher, Yaacov
2015-01-01
The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…
Analysis of dental caries using generalized linear and count regression models
Javali M. Phil
2013-11-01
Full Text Available Generalized linear models (GLM are generalization of linear regression models, which allow fitting regression models to response data in all the sciences especially medical and dental sciences that follow a general exponential family. These are flexible and widely used class of such models that can accommodate response variables. Count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. Zero inflated count regression models such as the zero-inflated Poisson (ZIP, zero-inflated negative binomial (ZINB regression models have been used to handle dental caries count data with many zeros. We present an evaluation framework to the suitability of applying the GLM, Poisson, NB, ZIP and ZINB to dental caries data set where the count data may exhibit evidence of many zeros and over-dispersion. Estimation of the model parameters using the method of maximum likelihood is provided. Based on the Vuong test statistic and the goodness of fit measure for dental caries data, the NB and ZINB regression models perform better than other count regression models.
Accounting for measurement error in log regression models with applications to accelerated testing.
Robert Richardson
Full Text Available In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.
Accounting for measurement error in log regression models with applications to accelerated testing.
Richardson, Robert; Tolley, H Dennis; Evenson, William E; Lunt, Barry M
2018-01-01
In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.
Generic global regression models for growth prediction of Salmonella in ground pork and pork cuts
Buschhardt, Tasja; Hansen, Tina Beck; Bahl, Martin Iain
2017-01-01
Introduction and Objectives Models for the prediction of bacterial growth in fresh pork are primarily developed using two-step regression (i.e. primary models followed by secondary models). These models are also generally based on experiments in liquids or ground meat and neglect surface growth....... It has been shown that one-step global regressions can result in more accurate models and that bacterial growth on intact surfaces can substantially differ from growth in liquid culture. Material and Methods We used a global-regression approach to develop predictive models for the growth of Salmonella....... One part of obtained logtransformed cell counts was used for model development and another for model validation. The Ratkowsky square root model and the relative lag time (RLT) model were integrated into the logistic model with delay. Fitted parameter estimates were compared to investigate the effect...
Soldić-Aleksić Jasna
2009-01-01
Full Text Available Market segmentation presents one of the key concepts of the modern marketing. The main goal of market segmentation is focused on creating groups (segments of customers that have similar characteristics, needs, wishes and/or similar behavior regarding the purchase of concrete product/service. Companies can create specific marketing plan for each of these segments and therefore gain short or long term competitive advantage on the market. Depending on the concrete marketing goal, different segmentation schemes and techniques may be applied. This paper presents a predictive market segmentation model based on the application of logistic regression model and CHAID analysis. The logistic regression model was used for the purpose of variables selection (from the initial pool of eleven variables which are statistically significant for explaining the dependent variable. Selected variables were afterwards included in the CHAID procedure that generated the predictive market segmentation model. The model results are presented on the concrete empirical example in the following form: summary model results, CHAID tree, Gain chart, Index chart, risk and classification tables.
MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.
2005-01-01
Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.
Robust geographically weighted regression of modeling the Air Polluter Standard Index (APSI)
Warsito, Budi; Yasin, Hasbi; Ispriyanti, Dwi; Hoyyi, Abdul
2018-05-01
The Geographically Weighted Regression (GWR) model has been widely applied to many practical fields for exploring spatial heterogenity of a regression model. However, this method is inherently not robust to outliers. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression model. One of solution to handle the outliers in the regression model is to use the robust models. So this model was called Robust Geographically Weighted Regression (RGWR). This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the RGWR approach. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. The best model is determined by the smallest AIC value. There are significance differences between Regression and RGWR in this case, but Basic GWR using the Gaussian kernel is the best model to modeling APSI because it has smallest AIC.
Amalia, Junita; Purhadi, Otok, Bambang Widjanarko
2017-11-01
Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.
Dairy Consumption and Risk of Frailty in Older Adults: A Prospective Cohort Study.
Lana, Alberto; Rodriguez-Artalejo, Fernando; Lopez-Garcia, Esther
2015-09-01
To examine the association between consumption of dairy products and risk of frailty in community-dwelling older adults. Prospective cohort study. General population from the older cohort of the Study on Nutrition and Cardiovascular Risk in Spain. Community-dwelling adults aged 60 and older free of frailty at baseline (N = 1,871). From 2008 to 2010, food consumption was assessed using a validated diet history. Participants were examined again in 2012 to assess incident frailty, defined as at least three of the five Fried criteria (exhaustion, weakness, low physical activity, slow walking speed, unintentional weight loss). Adjusted odds ratios (OR) for the main confounders were obtained using logistic regression. During follow-up, 134 new cases of frailty were identified. Participants consuming seven or more servings per week of low-fat milk and yogurt had lower incidence of frailty (OR = 0.52; 95% confidence interval (CI) = 0.29-0.90; P for trend = .03) than those consuming less than one serving per week. Specifically, consumers of seven or more servings per week of low-fat milk and yogurt had less risk of slow walking speed (OR = 0.64, 95% CI = 0.44-0.92, P trend = .01) and of weight loss (OR = 0.54, 95% CI = 0.33-0.87, P trend = .02). Consuming seven or more servings per week of whole milk or yogurt (OR = 1.53, 95% CI = 0.90-2.60, P trend = .10) or of cheese (OR = 0.91, 95% CI = 0.52-1.61; P trend = .61) was not associated with incident frailty. Higher consumption of low-fat milk and yogurt was associated with lower risk of frailty and, specifically, of slow walking speed and weight loss. Current recommendations to prevent frailty include protein supplementation; thus, although experimental research is needed, increasing the consumption of low-fat yogurt and milk might prevent frailty in older adults. © 2015, Copyright the Authors Journal compilation © 2015, The American Geriatrics Society.
impairment, depression, social isolation, ... water and electrolyte balance, lead to a state of ... in hospitalised patients and supplements ... effects/interactions. • screen for physical impairments. • exercise. • adequate diet. .... CVA, arthritis.
Frailty and Fear of Falling: The FISTAC Study.
Esbrí-Víctor, M; Huedo-Rodenas, I; López-Utiel, M; Navarro-López, J L; Martínez-Reig, M; Serra-Rexach, J A; Romero-Rizos, L; Abizanda, P
2017-01-01
To analyze the association between frailty and Fear of Falling (FoF) in a cohort of older adults with previous falls. Cross-sectional study (FISTAC). Falls Unit, Complejo Hospitalario Universitario of Albacete (Spain). 183 adults older than 69 years, from the Falls Unit, with a history of a previous fall in the last year. FoF was assessed at baseline using the Falls Efficacy Scale International (FES-I) and three questions previously validated. Frailty was assessed with the frailty phenotype criteria. Age, gender, comorbidity, nutritional status, cognitive status and risk of depression were determined. Mean age 78.4, 80.3% women. FoF was present in 140 (76.5%) participants with the three questions and 102 (55.7%) presented high concern of falling with the FES-I. 88.8% of frail older adults presented FoF compared to 62.4% of those who were not frail, and only 37.8% of non frail had a high concern of falling, compared to 77.2% of those who were frail measured with the FES-I. Frail participants had an adjusted risk of FoF that was 3.18 (95% CI 1.32 to 7.65) higher compared to those who were not frail assessed with the three questions and 3.93 (95% CI 1.85 to 8.36) higher concern of falling when using the FES-I scale. Only female sex and depression risk were also associated to FoF in the final adjusted models. Frailty is independently associated with the FoF syndrome in older faller subjects.
Can We Use Regression Modeling to Quantify Mean Annual Streamflow at a Global-Scale?
Barbarossa, V.; Huijbregts, M. A. J.; Hendriks, J. A.; Beusen, A.; Clavreul, J.; King, H.; Schipper, A.
2016-12-01
Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for a number of applications, including assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF using observations of discharge and catchment characteristics from 1,885 catchments worldwide, ranging from 2 to 106 km2 in size. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB [van Beek et al., 2011] by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area, mean annual precipitation and air temperature, average slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error values were lower (0.29 - 0.38 compared to 0.49 - 0.57) and the modified index of agreement was higher (0.80 - 0.83 compared to 0.72 - 0.75). Our regression model can be applied globally at any point of the river network, provided that the input parameters are within the range of values employed in the calibration of the model. The performance is reduced for water scarce regions and further research should focus on improving such an aspect for regression-based global hydrological models.
ANALYSIS OF THE FINANCIAL PERFORMANCES OF THE FIRM, BY USING THE MULTIPLE REGRESSION MODEL
Constantin Anghelache
2011-11-01
Full Text Available The information achieved through the use of simple linear regression are not always enough to characterize the evolution of an economic phenomenon and, furthermore, to identify its possible future evolution. To remedy these drawbacks, the special literature includes multiple regression models, in which the evolution of the dependant variable is defined depending on two or more factorial variables.
Strathe, Anders B; Mark, Thomas; Nielsen, Bjarne
2014-01-01
Random regression models were used to estimate covariance functions between cumulated feed intake (CFI) and body weight (BW) in 8424 Danish Duroc pigs. Random regressions on second order Legendre polynomials of age were used to describe genetic and permanent environmental curves in BW and CFI...
Modelling infant mortality rate in Central Java, Indonesia use generalized poisson regression method
Prahutama, Alan; Sudarno
2018-05-01
The infant mortality rate is the number of deaths under one year of age occurring among the live births in a given geographical area during a given year, per 1,000 live births occurring among the population of the given geographical area during the same year. This problem needs to be addressed because it is an important element of a country’s economic development. High infant mortality rate will disrupt the stability of a country as it relates to the sustainability of the population in the country. One of regression model that can be used to analyze the relationship between dependent variable Y in the form of discrete data and independent variable X is Poisson regression model. Recently The regression modeling used for data with dependent variable is discrete, among others, poisson regression, negative binomial regression and generalized poisson regression. In this research, generalized poisson regression modeling gives better AIC value than poisson regression. The most significant variable is the Number of health facilities (X1), while the variable that gives the most influence to infant mortality rate is the average breastfeeding (X9).
Mach Łukasz
2017-06-01
Full Text Available The research process aimed at building regression models, which helps to valuate residential real estate, is presented in the following article. Two widely used computational tools i.e. the classical multiple regression and regression models of artificial neural networks were used in order to build models. An attempt to define the utilitarian usefulness of the above-mentioned tools and comparative analysis of them is the aim of the conducted research. Data used for conducting analyses refers to the secondary transactional residential real estate market.
Linear regression metamodeling as a tool to summarize and present simulation model results.
Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M
2013-10-01
Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.
[Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].
Gao, W L; Lin, H; Liu, X N; Ren, X W; Li, J S; Shen, X P; Zhu, S L
2017-03-10
To evaluate the estimation of prevalence ratio ( PR ) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR 's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95 %CI : 1.005-1.265), 1.128(95 %CI : 1.001-1.264) and 1.132(95 %CI : 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95 % CI : 1.055-1.206) and 1.126(95 % CI : 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR , which was 1.125 (95 %CI : 1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR . Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.
Lee, Sungchul; Lee, Sangyoon; Harada, Kazuhiro; Bae, Seongryu; Makizako, Hyuma; Doi, Takehiko; Tsutsumimoto, Kota; Hotta, Ryo; Nakakubo, Sho; Park, Hyuntae; Suzuki, Takao; Shimada, Hiroyuki
2017-10-01
The aim of the present study was to evaluate the relationship between kidney function with concomitant diabetes or hypertension and frailty in community-dwelling Japanese older adults. The participants were 9606 residents (community-dwelling Japanese older adults) who completed baseline assessments. The estimated glomerular filtration rate (mL/min/1.73 m 2 ) was determined according to the serum creatinine level, and participants were classified into four mutually exclusive categories: ≥60.0 (normal range), 45.0-59.9, 30.0-44.9 and who met three, four or five criteria satisfied the definition of having frailty. Multivariate logistic regression was used to examine the relationships between estimated glomerular filtration rate and frailty. After multivariate adjustment, participants with lower kidney function (estimated glomerular filtration rate hypertension (OR 2.53, 95% CI 1.45-5.12) showed a significantly increased risk of frailty in the lower kidney function group, regardless of multivariate controls. Furthermore, the analyses showed an even greater increase in the risk of frailty in patients with a history of both diabetes and hypertension (OR 3.67, 95% CI 1.13-14.1) CONCLUSIONS: A lower level of kidney function was associated with a higher risk of frailty in community-dwelling Japanese older adults. Geriatr Gerontol Int 2017; 17: 1527-1533. © 2016 Japan Geriatrics Society.
Amaliana, Luthfatul; Sa'adah, Umu; Wayan Surya Wardhani, Ni
2017-12-01
Tetanus Neonatorum is an infectious disease that can be prevented by immunization. The number of Tetanus Neonatorum cases in East Java Province is the highest in Indonesia until 2015. Tetanus Neonatorum data contain over dispersion and big enough proportion of zero-inflation. Negative Binomial (NB) regression is an alternative method when over dispersion happens in Poisson regression. However, the data containing over dispersion and zero-inflation are more appropriately analyzed by using Zero-Inflated Negative Binomial (ZINB) regression. The purpose of this study are: (1) to model Tetanus Neonatorum cases in East Java Province with 71.05 percent proportion of zero-inflation by using NB and ZINB regression, (2) to obtain the best model. The result of this study indicates that ZINB is better than NB regression with smaller AIC.
Poisson regression for modeling count and frequency outcomes in trauma research.
Gagnon, David R; Doron-LaMarca, Susan; Bell, Margret; O'Farrell, Timothy J; Taft, Casey T
2008-10-01
The authors describe how the Poisson regression method for analyzing count or frequency outcome variables can be applied in trauma studies. The outcome of interest in trauma research may represent a count of the number of incidents of behavior occurring in a given time interval, such as acts of physical aggression or substance abuse. Traditional linear regression approaches assume a normally distributed outcome variable with equal variances over the range of predictor variables, and may not be optimal for modeling count outcomes. An application of Poisson regression is presented using data from a study of intimate partner aggression among male patients in an alcohol treatment program and their female partners. Results of Poisson regression and linear regression models are compared.
Watts, Paul; Webb, Elizabeth; Netuveli, Gopalakrishnan
2017-07-14
Frailty is a common syndrome in older adults characterised by increased vulnerability to adverse health outcomes as a result of decline in functional and physiological measures. Frailty predicts a range of poor health and social outcomes and is associated with increased risk of hospital admission. The health benefits of sport and physical activity and the health risks of inactivity are well known. However, less is known about the role of sports clubs and physical activity in preventing and managing frailty in older adults. The objective of this study is to examine the role of membership of sports clubs in promoting physical activity and reducing levels of frailty in older adults. We used data from waves 1 to 7 of the English Longitudinal Study of Ageing (ELSA). Survey items on physical activity were combined to produce a measure of moderate or vigorous physical activity for each wave. Frailty was measured using an index of accumulated deficits. A total of sixty deficits, including symptoms, disabilities and diseases were recorded through self-report and tests. Direct and indirect relationships between sports club membership, levels of physical activity and frailty were examined using a cross-lagged panel model. We found evidence for an indirect relationship between sports club membership and frailty, mediated by physical activity. This finding was observed when examining time-specific indirect pathways and the total of all indirect pathways across seven waves of survey data (Est = -0.097 [95% CI = -0.124,-0.070], p = sports clubs may be useful in preventing and managing frailty in older adults, both directly and indirectly through increased physical activity levels. Sports clubs accessible to older people may improve health in this demographic by increasing activity levels and reducing frailty and associated comorbidities. There is a need for investment in these organisations to provide opportunities for older people to achieve the levels of physical activity
A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield
Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan
2018-04-01
In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.
Drzewiecki, Wojciech
2016-12-01
In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.
Manjusha Yadla
2017-01-01
Full Text Available This is a prospective cohort study to assess the prevalence of frailty in patients undergoing maintenance hemodialysis (HD under the government-funded scheme at our center and to assess the relationship between frailty and falls, hospitalizations, and mortality. This was done at our center which is completely supported by the government, which provides HD to all the patients under poverty line. Epidemiological data, anthropometric measurements, comorbidities assessment, frailty assessment using Fried criteria, subsequent hospitalizations, falls, and mortality were recorded in our prevalent dialysis population at our center between October 2014 and October 2015. Two hundred and twenty-six patients were enrolled during this period. Twenty-one patients were excluded as they did not satisfy the inclusion criteria. Two hundred and five prospective patients were studied for the predictors of frailty. Frailty was present in 82% of the study population. Mean age of our study population was 44.95 ± 13.27 years. On univariate analysis, diabetes mellitus, hypertension (HTN, cerebrovascular accident (CVA, left ventricular dysfunction (LVD, peripheral vascular disease (PVD, smoking, hepatitis C, inadequate dialysis, intradialytic hypotension (IDH, interdialytic weight gain, low serum creatinine <4 mg/dL, and anemia (Hb <10 g/dL were found to be statistically significantly different between frail and nonfrail groups On multivariate regression analysis, only HTN, PVD, CVA, anemia, smoking, and IDH were found to be significant. Frailty is highly prevalent among dialysis population. Factors predicting frailty include HTN, smoking, LVD, PVD, CVA, smoking, anemia, and IDH. Frailty is a significant risk factor for falls and hospitalizations.
As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...
Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models
Pappas, S.S. [Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, 83 200 Samos (Greece); Ekonomou, L.; Chatzarakis, G.E. [Department of Electrical Engineering Educators, ASPETE - School of Pedagogical and Technological Education, N. Heraklion, 141 21 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24100 Kalamata (Greece); Katsikas, S.K. [Department of Technology Education and Digital Systems, University of Piraeus, 150 Androutsou Srt., 18 532 Piraeus (Greece); Liatsis, P. [Division of Electrical Electronic and Information Engineering, School of Engineering and Mathematical Sciences, Information and Biomedical Engineering Centre, City University, Northampton Square, London EC1V 0HB (United Kingdom)
2008-09-15
This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts. (author)
Junius-Walker, Ulrike; Onder, Graziano; Soleymani, Dagmar; Wiese, Birgitt; Albaina, Olatz; Bernabei, Roberto; Marzetti, Emanuele
2018-05-31
One of the major threats looming over the growing older population is frailty. It is a distinctive health state characterised by increased vulnerability to internal and external stressors. Although the presence of frailty is well acknowledged, its concept and operationalisation are hampered by the extraordinary phenotypical and biological complexity. Yet, a widely accepted conception is needed to offer tailored policies and approaches. The ADVANTAGE Group aims to analyse the diverse frailty concepts to uncover the essence of frailty as a basis for a shared understanding. A systematic literature review was performed on frailty concepts and definitions from 2010 onwards. Eligible publications were reviewed using concept analysis that led to the extraction of text data for the themes "definition", "attributes", "antecedents", "consequences", and "related concepts". Qualitative description was used to further analyse the extracted text passages, leading to inductively developed categories on the essence of frailty. 78 publications were included in the review, and 996 relevant text passages were extracted for analysis. Five components constituted a comprehensive definition: vulnerability, genesis, features, characteristics, and adverse outcomes. Each component is described in more detail by a set of defining and explanatory criteria. An underlying functional perspective of health or its impairments is most compatible with the entity of frailty. The recent findings facilitate a focus on the relevant building blocks that define frailty. They point to the commonalities of the diverse frailty concepts and definitions. Based on these components, a widely accepted broad definition of frailty comes into range. Copyright © 2018 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
Fougère, Bertrand; Daumas, Matthieu; Lilamand, Matthieu; Sourdet, Sandrine; Delrieu, Julien; Vellas, Bruno; Abellan van Kan, Gabor
2017-11-01
A consensus panel, based on epidemiologic evidence, argued that physical frailty is often associated with cognitive impairment, possibly because of common underlying pathophysiological mechanisms. The concepts of cognitive frailty and motoric cognitive risk were recently proposed in literature and may represent a prodromal stage for neurodegenerative diseases. The purpose of this study was to analyze the relationship between cognition and the components of the physical phenotype of frailty. Participants admitted to the Toulouse frailty day hospital aged 65 years or older were included in this cross-sectional study. Cognitive impairment was identified using the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR). Frailty was assessed using the physical phenotype as defined by Fried's criteria. We divided the participants into 2 groups: participants with normal cognition (CDR = 0) and participants who had cognitive impairment (CDR = 0.5). Participants with CDR >0.5 were excluded. Data from 1620 participants, mean age 82 years and 63% of women were analyzed. Cognitive impairment was identified in 52.5% of the participants. Frailty was identified in 44.7% of the sample. There were more frail subjects in the impaired group than the normal cognitive group (51% vs 38%, P impairment [adjusted odds ratio (OR) 1.66, 95% confidence interval (CI) 1.12-2.46]. Subsequent analysis showed that the association between cognitive impairment and frailty was only observed considering one of the 5 frailty criteria: gait speed (adjusted OR 1.89, 95% CI 1.55-2.32). Physical frailty and in particular slow gait speed were associated with cognitive impairment. Future research including longitudinal studies should exploit the association between cognitive impairment and frailty. Copyright © 2017 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.
Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A
2014-09-01
Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.
Developing and testing a global-scale regression model to quantify mean annual streamflow
Barbarossa, Valerio; Huijbregts, Mark A. J.; Hendriks, A. Jan; Beusen, Arthur H. W.; Clavreul, Julie; King, Henry; Schipper, Aafke M.
2017-01-01
Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF based on a dataset unprecedented in size, using observations of discharge and catchment characteristics from 1885 catchments worldwide, measuring between 2 and 106 km2. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area and catchment averaged mean annual precipitation and air temperature, slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error (RMSE) values were lower (0.29-0.38 compared to 0.49-0.57) and the modified index of agreement (d) was higher (0.80-0.83 compared to 0.72-0.75). Our regression model can be applied globally to estimate MAF at any point of the river network, thus providing a feasible alternative to spatially explicit process-based global hydrological models.
Assessment of Self-Efficacy and its Relationship with Frailty in the Elderly
Doba, Nobutaka; Tokuda, Yasuharu; Saiki, Keiichirou; Kushiro, Toshio; Hirano, Masumi; Matsubara, Yoshihiro; Hinohara, Shigeaki
2016-01-01
Objective It has been increasingly recognized in various clinical areas that self-efficacy promotes the level of competence in patients. The validity, applicability and potential usefulness of a new, simple model for assessing self-efficacy in the elderly with special reference to frailty were investigated for improving elderly patients' accomplishments. Methods The subjects of the present study comprised 257 elderly people who were members of the New Elder Citizen Movement in Japan and their mean age was 82.3±3.8 years. Interview materials including self-efficacy questionnaires were sent to all participants in advance and all other physical examinations were performed at the Life Planning Center Clinic. Results The internal consistency and close relation among a set of items used as a measure of self-efficacy were evaluated by Cronbach's alpha index, which was 0.79. Although no age-dependent difference was identified in either sex, gender-related differences in some factors were noted. Regarding several parametric parameters, Beck's inventory alone revealed a significant relationship to self-efficacy in both sexes. Additionally, non-parametric items such as stamina, power and memory were strongly correlated with self-efficacy in both sexes. Frailty showed a significant independent relationship with self-efficacy in a multiple linear regression model analysis and using Beck's inventory, stamina, power and memory were identified to be independent factors for self-efficacy. Conclusion The simple assessment of self-efficacy described in this study may be a useful tool for successful aging of elderly people. PMID:27725537
Reflexion on linear regression trip production modelling method for ensuring good model quality
Suprayitno, Hitapriya; Ratnasari, Vita
2017-11-01
Transport Modelling is important. For certain cases, the conventional model still has to be used, in which having a good trip production model is capital. A good model can only be obtained from a good sample. Two of the basic principles of a good sampling is having a sample capable to represent the population characteristics and capable to produce an acceptable error at a certain confidence level. It seems that this principle is not yet quite understood and used in trip production modeling. Therefore, investigating the Trip Production Modelling practice in Indonesia and try to formulate a better modeling method for ensuring the Model Quality is necessary. This research result is presented as follows. Statistics knows a method to calculate span of prediction value at a certain confidence level for linear regression, which is called Confidence Interval of Predicted Value. The common modeling practice uses R2 as the principal quality measure, the sampling practice varies and not always conform to the sampling principles. An experiment indicates that small sample is already capable to give excellent R2 value and sample composition can significantly change the model. Hence, good R2 value, in fact, does not always mean good model quality. These lead to three basic ideas for ensuring good model quality, i.e. reformulating quality measure, calculation procedure, and sampling method. A quality measure is defined as having a good R2 value and a good Confidence Interval of Predicted Value. Calculation procedure must incorporate statistical calculation method and appropriate statistical tests needed. A good sampling method must incorporate random well distributed stratified sampling with a certain minimum number of samples. These three ideas need to be more developed and tested.
Using the Logistic Regression model in supporting decisions of establishing marketing strategies
Cristinel CONSTANTIN
2015-12-01
Full Text Available This paper is about an instrumental research regarding the using of Logistic Regression model for data analysis in marketing research. The decision makers inside different organisation need relevant information to support their decisions regarding the marketing strategies. The data provided by marketing research could be computed in various ways but the multivariate data analysis models can enhance the utility of the information. Among these models we can find the Logistic Regression model, which is used for dichotomous variables. Our research is based on explanation the utility of this model and interpretation of the resulted information in order to help practitioners and researchers to use it in their future investigations
Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi
2012-01-01
The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.
Chen, Baojiang; Qin, Jing
2014-05-10
In statistical analysis, a regression model is needed if one is interested in finding the relationship between a response variable and covariates. When the response depends on the covariate, then it may also depend on the function of this covariate. If one has no knowledge of this functional form but expect for monotonic increasing or decreasing, then the isotonic regression model is preferable. Estimation of parameters for isotonic regression models is based on the pool-adjacent-violators algorithm (PAVA), where the monotonicity constraints are built in. With missing data, people often employ the augmented estimating method to improve estimation efficiency by incorporating auxiliary information through a working regression model. However, under the framework of the isotonic regression model, the PAVA does not work as the monotonicity constraints are violated. In this paper, we develop an empirical likelihood-based method for isotonic regression model to incorporate the auxiliary information. Because the monotonicity constraints still hold, the PAVA can be used for parameter estimation. Simulation studies demonstrate that the proposed method can yield more efficient estimates, and in some situations, the efficiency improvement is substantial. We apply this method to a dementia study. Copyright © 2013 John Wiley & Sons, Ltd.
Fang, Xiande; Xu, Yu
2011-01-01
The empirical model of turbine efficiency is necessary for the control- and/or diagnosis-oriented simulation and useful for the simulation and analysis of dynamic performances of the turbine equipment and systems, such as air cycle refrigeration systems, power plants, turbine engines, and turbochargers. Existing empirical models of turbine efficiency are insufficient because there is no suitable form available for air cycle refrigeration turbines. This work performs a critical review of empirical models (called mean value models in some literature) of turbine efficiency and develops an empirical model in the desired form for air cycle refrigeration, the dominant cooling approach in aircraft environmental control systems. The Taylor series and regression analysis are used to build the model, with the Taylor series being used to expand functions with the polytropic exponent and the regression analysis to finalize the model. The measured data of a turbocharger turbine and two air cycle refrigeration turbines are used for the regression analysis. The proposed model is compact and able to present the turbine efficiency map. Its predictions agree with the measured data very well, with the corrected coefficient of determination R c 2 ≥ 0.96 and the mean absolute percentage deviation = 1.19% for the three turbines. -- Highlights: → Performed a critical review of empirical models of turbine efficiency. → Developed an empirical model in the desired form for air cycle refrigeration, using the Taylor expansion and regression analysis. → Verified the method for developing the empirical model. → Verified the model.
Yan, Jun; Aseltine, Robert H., Jr.; Harel, Ofer
2013-01-01
Comparing regression coefficients between models when one model is nested within another is of great practical interest when two explanations of a given phenomenon are specified as linear models. The statistical problem is whether the coefficients associated with a given set of covariates change significantly when other covariates are added into…
Structured Additive Regression Models: An R Interface to BayesX
Nikolaus Umlauf
2015-02-01
Full Text Available Structured additive regression (STAR models provide a flexible framework for model- ing possible nonlinear effects of covariates: They contain the well established frameworks of generalized linear models and generalized additive models as special cases but also allow a wider class of effects, e.g., for geographical or spatio-temporal data, allowing for specification of complex and realistic models. BayesX is standalone software package providing software for fitting general class of STAR models. Based on a comprehensive open-source regression toolbox written in C++, BayesX uses Bayesian inference for estimating STAR models based on Markov chain Monte Carlo simulation techniques, a mixed model representation of STAR models, or stepwise regression techniques combining penalized least squares estimation with model selection. BayesX not only covers models for responses from univariate exponential families, but also models from less-standard regression situations such as models for multi-categorical responses with either ordered or unordered categories, continuous time survival data, or continuous time multi-state models. This paper presents a new fully interactive R interface to BayesX: the R package R2BayesX. With the new package, STAR models can be conveniently specified using Rs formula language (with some extended terms, fitted using the BayesX binary, represented in R with objects of suitable classes, and finally printed/summarized/plotted. This makes BayesX much more accessible to users familiar with R and adds extensive graphics capabilities for visualizing fitted STAR models. Furthermore, R2BayesX complements the already impressive capabilities for semiparametric regression in R by a comprehensive toolbox comprising in particular more complex response types and alternative inferential procedures such as simulation-based Bayesian inference.
Nagel-Alne, G E; Krontveit, R; Bohlin, J; Valle, P S; Skjerve, E; Sølverød, L S
2014-07-01
In 2001, the Norwegian Goat Health Service initiated the Healthier Goats program (HG), with the aim of eradicating caprine arthritis encephalitis, caseous lymphadenitis, and Johne's disease (caprine paratuberculosis) in Norwegian goat herds. The aim of the present study was to explore how control and eradication of the above-mentioned diseases by enrolling in HG affected milk yield by comparison with herds not enrolled in HG. Lactation curves were modeled using a multilevel cubic spline regression model where farm, goat, and lactation were included as random effect parameters. The data material contained 135,446 registrations of daily milk yield from 28,829 lactations in 43 herds. The multilevel cubic spline regression model was applied to 4 categories of data: enrolled early, control early, enrolled late, and control late. For enrolled herds, the early and late notations refer to the situation before and after enrolling in HG; for nonenrolled herds (controls), they refer to development over time, independent of HG. Total milk yield increased in the enrolled herds after eradication: the total milk yields in the fourth lactation were 634.2 and 873.3 kg in enrolled early and enrolled late herds, respectively, and 613.2 and 701.4 kg in the control early and control late herds, respectively. Day of peak yield differed between enrolled and control herds. The day of peak yield came on d 6 of lactation for the control early category for parities 2, 3, and 4, indicating an inability of the goats to further increase their milk yield from the initial level. For enrolled herds, on the other hand, peak yield came between d 49 and 56, indicating a gradual increase in milk yield after kidding. Our results indicate that enrollment in the HG disease eradication program improved the milk yield of dairy goats considerably, and that the multilevel cubic spline regression was a suitable model for exploring effects of disease control and eradication on milk yield. Copyright © 2014
Profile-driven regression for modeling and runtime optimization of mobile networks
McClary, Dan; Syrotiuk, Violet; Kulahci, Murat
2010-01-01
Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization...... of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike...
Carstensen, Bendix
1996-01-01
This paper shows how to fit excess and relative risk regression models to interval censored survival data, and how to implement the models in standard statistical software. The methods developed are used for the analysis of HIV infection rates in a cohort of Danish homosexual men.......This paper shows how to fit excess and relative risk regression models to interval censored survival data, and how to implement the models in standard statistical software. The methods developed are used for the analysis of HIV infection rates in a cohort of Danish homosexual men....
The Relationship between Economic Growth and Money Laundering – a Linear Regression Model
Daniel Rece
2009-09-01
Full Text Available This study provides an overview of the relationship between economic growth and money laundering modeled by a least squares function. The report analyzes statistically data collected from USA, Russia, Romania and other eleven European countries, rendering a linear regression model. The study illustrates that 23.7% of the total variance in the regressand (level of money laundering is “explained” by the linear regression model. In our opinion, this model will provide critical auxiliary judgment and decision support for anti-money laundering service systems.
Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne
2012-12-01
In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.
Frailty in elderly people with chronic kidney disease
Maria Eugenia Portilla Franco
2016-11-01
Frailty can be reversed, which is why a study of frailty in patients with chronic kidney disease is of particular interest. This article aims to describe the association between ageing, frailty and chronic kidney disease in light of the most recent and relevant scientific publications.
A primer for biomedical scientists on how to execute model II linear regression analysis.
Ludbrook, John
2012-04-01
1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.
Tan, Qihua; Bathum, L; Christiansen, L
2003-01-01
In this paper, we apply logistic regression models to measure genetic association with human survival for highly polymorphic and pleiotropic genes. By modelling genotype frequency as a function of age, we introduce a logistic regression model with polytomous responses to handle the polymorphic...... situation. Genotype and allele-based parameterization can be used to investigate the modes of gene action and to reduce the number of parameters, so that the power is increased while the amount of multiple testing minimized. A binomial logistic regression model with fractional polynomials is used to capture...... the age-dependent or antagonistic pleiotropic effects. The models are applied to HFE genotype data to assess the effects on human longevity by different alleles and to detect if an age-dependent effect exists. Application has shown that these methods can serve as useful tools in searching for important...
Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression
Khikmah, L.; Wijayanto, H.; Syafitri, U. D.
2017-04-01
The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.
Laur, Celia V; McNicholl, Tara; Valaitis, Renata; Keller, Heather H
2017-05-01
There is increasing awareness of the detrimental health impact of frailty on older adults and of the high prevalence of malnutrition in this segment of the population. Experts in these 2 arenas need to be cognizant of the overlap in constructs, diagnosis, and treatment of frailty and malnutrition. There is a lack of consensus regarding the definition of malnutrition and how it should be assessed. While there is consensus on the definition of frailty, there is no agreement on how it should be measured. Separate assessment tools exist for both malnutrition and frailty; however, there is intersection between concepts and measures. This narrative review highlights some of the intersections within these screening/assessment tools, including weight loss/decreased body mass, functional capacity, and weakness (handgrip strength). The potential for identification of a minimal set of objective measures to identify, or at least consider risk for both conditions, is proposed. Frailty and malnutrition have also been shown to result in similar negative health outcomes and consequently common treatment strategies have been studied, including oral nutritional supplements. While many of the outcomes of treatment relate to both concepts of frailty and malnutrition, research questions are typically focused on the frailty concept, leading to possible gaps or missed opportunities in understanding the effect of complementary interventions on malnutrition. A better understanding of how these conditions overlap may improve treatment strategies for frail, malnourished, older adults.
Regression analysis understanding and building business and economic models using Excel
Wilson, J Holton
2012-01-01
The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book will teach you the essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The authors take a non-theoretical treatment that is accessible even if you have a limited statistical background. It is specifically designed to teach the correct use of regression, while advising you of its limitations and teaching about common pitfalls. This book describe
Estimasi Model Seemingly Unrelated Regression (SUR dengan Metode Generalized Least Square (GLS
Ade Widyaningsih
2015-04-01
Full Text Available Regression analysis is a statistical tool that is used to determine the relationship between two or more quantitative variables so that one variable can be predicted from the other variables. A method that can used to obtain a good estimation in the regression analysis is ordinary least squares method. The least squares method is used to estimate the parameters of one or more regression but relationships among the errors in the response of other estimators are not allowed. One way to overcome this problem is Seemingly Unrelated Regression model (SUR in which parameters are estimated using Generalized Least Square (GLS. In this study, the author applies SUR model using GLS method on world gasoline demand data. The author obtains that SUR using GLS is better than OLS because SUR produce smaller errors than the OLS.
Estimasi Model Seemingly Unrelated Regression (SUR dengan Metode Generalized Least Square (GLS
Ade Widyaningsih
2014-06-01
Full Text Available Regression analysis is a statistical tool that is used to determine the relationship between two or more quantitative variables so that one variable can be predicted from the other variables. A method that can used to obtain a good estimation in the regression analysis is ordinary least squares method. The least squares method is used to estimate the parameters of one or more regression but relationships among the errors in the response of other estimators are not allowed. One way to overcome this problem is Seemingly Unrelated Regression model (SUR in which parameters are estimated using Generalized Least Square (GLS. In this study, the author applies SUR model using GLS method on world gasoline demand data. The author obtains that SUR using GLS is better than OLS because SUR produce smaller errors than the OLS.
Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne
2016-04-01
Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.
Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.
2014-07-01
Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.
Validation of regression models for nitrate concentrations in the upper groundwater in sandy soils
Sonneveld, M.P.W.; Brus, D.J.; Roelsma, J.
2010-01-01
For Dutch sandy regions, linear regression models have been developed that predict nitrate concentrations in the upper groundwater on the basis of residual nitrate contents in the soil in autumn. The objective of our study was to validate these regression models for one particular sandy region dominated by dairy farming. No data from this area were used for calibrating the regression models. The model was validated by additional probability sampling. This sample was used to estimate errors in 1) the predicted areal fractions where the EU standard of 50 mg l -1 is exceeded for farms with low N surpluses (ALT) and farms with higher N surpluses (REF); 2) predicted cumulative frequency distributions of nitrate concentration for both groups of farms. Both the errors in the predicted areal fractions as well as the errors in the predicted cumulative frequency distributions indicate that the regression models are invalid for the sandy soils of this study area. - This study indicates that linear regression models that predict nitrate concentrations in the upper groundwater using residual soil N contents should be applied with care.
A brief introduction to regression designs and mixed-effects modelling by a recent convert
Balling, Laura Winther
2008-01-01
This article discusses the advantages of multiple regression designs over the factorial designs traditionally used in many psycholinguistic experiments. It is shown that regression designs are typically more informative, statistically more powerful and better suited to the analysis of naturalistic tasks. The advantages of including both fixed and random effects are demonstrated with reference to linear mixed-effects models, and problems of collinearity, variable distribution and variable sele...
Macronutrients Intake and Incident Frailty in Older Adults: A Prospective Cohort Study.
Sandoval-Insausti, Helena; Pérez-Tasigchana, Raúl F; López-García, Esther; García-Esquinas, Esther; Rodríguez-Artalejo, Fernando; Guallar-Castillón, Pilar
2016-10-01
Only a few studies have assessed the association between protein intake and frailty incidence and have obtained inconsistent results. This study examined the association of protein and other macronutrient intake with the risk of frailty in older adults. A prospective cohort of 1,822 community-dwelling individuals aged 60 and older was recruited in 2008-2010 and followed-up through 2012. At baseline, food consumption was assessed with a validated, computerized face-to-face diet history. In 2012, individuals were contacted again to ascertain incident frailty, defined as the presence of at least three of the five Fried criteria: low physical activity, slowness, unintentional weight loss, muscle weakness, and exhaustion. Analyses were performed using logistic regression and adjusted for the main confounders, including total energy intake. During a mean follow-up of 3.5 years, 132 persons with incident frailty were identified. The odds ratios (95% confidence interval) of frailty across increasing quartiles of total protein were 1.00, 0.55 (0.32-0.93), 0.45 (0.26-0.78), and 0.41 (0.23-0.72); p trend: .001. The corresponding figures for animal protein intake were 1.00, 0.68 (0.40-1.17), 0.56 (0.32-0.97), and 0.48 (0.26-0.87), p trend: .011. And for intake of monounsaturated fatty acids (MUFAs), the results were 1.00, 0.66 (0.37-1.20), 0.54 (0.28-1.02), and 0.50 (0.26-0.96); p trend: .038. No association was found between intake of vegetable protein, saturated fats, long-chain ω-3 fatty acids, α-linolenic acid, linoleic acid, simple sugars, or polysaccharides and the risk of frailty. Intake of total protein, animal protein, and MUFAs was inversely associated with incident frailty. Promoting the intake of these nutrients might reduce frailty. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
A computational approach to compare regression modelling strategies in prediction research.
Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H
2016-08-25
It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.
Changes in frailty conditions and phenotype components in elderly after hospitalization.
Marchiori, Gianna Fiori; Tavares, Darlene Mara Dos Santos
2017-07-10
describing the changes in frailty conditions over the period of a year after hospital discharge, verifying predictive variables for changes in frailty conditions and frailty phenotype components according to worsening, improving and stable groups. a longitudinal survey carried out with 129 elderly. A structured form for socioeconomic and health data, scales (Geriatric Depression Scale - short form, Katz scale, Lawton and Brody scale) and frailty phenotype according to Fried were used. Descriptive analysis and multinomial logistic regression model (pgrupos de piora, melhora e estabilidade. inquérito longitudinal, realizado com 129 idosos. Utilizou-se formulário estruturado para dados socioeconômicos e saúde, escalas (Depressão Geriátrica Abreviada, Katz, Lawton e Brody) e fenótipo de fragilidade, segundo Fried. Procederam-se às análises descritiva e modelo de regressão logística multinomial (pgrupo de piora, o aumento do número de morbidades foi preditor para exaustão e/ou fadiga, enquanto que, no grupo de melhora, o aumento na dependência das atividades instrumentais de vida diária foi preditor para a perda de peso, e a diminuição dos escores do indicativo de depressão para o baixo nível de atividade física. houve maior percentual de mudança na condição de idosos não frágeis para pré-frágeis e as variáveis de saúde foram preditoras apenas para os componentes do fenótipo de fragilidade. describir los cambios en las condiciones de fragilidad a lo largo de un año después del alta hospitalaria, y verificar las variables predictoras del cambio de las condiciones de fragilidad y de los componentes del fenotipo de fragilidad, según grupos de empeoramiento, mejoría y estabilidad. encuesta longitudinal, realizada con 129 ancianos. Se utilizó formulario estructurado para recoger datos socioeconómicos y salud; se utilizaron las escalas Depresión Geriátrica Abreviada (GDS-15), Actividades Básicas de Vida Diaria de Katz, Actividades
Prahutama, Alan; Suparti; Wahyu Utami, Tiani
2018-03-01
Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.
truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models
Maria Karlsson
2014-05-01
Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.
Modeling and prediction of Turkey's electricity consumption using Support Vector Regression
Kavaklioglu, Kadir
2011-01-01
Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, ε-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best ε-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. (author)
Improved model of the retardance in citric acid coated ferrofluids using stepwise regression
Lin, J. F.; Qiu, X. R.
2017-06-01
Citric acid (CA) coated Fe3O4 ferrofluids (FFs) have been conducted for biomedical application. The magneto-optical retardance of CA coated FFs was measured by a Stokes polarimeter. Optimization and multiple regression of retardance in FFs were executed by Taguchi method and Microsoft Excel previously, and the F value of regression model was large enough. However, the model executed by Excel was not systematic. Instead we adopted the stepwise regression to model the retardance of CA coated FFs. From the results of stepwise regression by MATLAB, the developed model had highly predictable ability owing to F of 2.55897e+7 and correlation coefficient of one. The average absolute error of predicted retardances to measured retardances was just 0.0044%. Using the genetic algorithm (GA) in MATLAB, the optimized parametric combination was determined as [4.709 0.12 39.998 70.006] corresponding to the pH of suspension, molar ratio of CA to Fe3O4, CA volume, and coating temperature. The maximum retardance was found as 31.712°, close to that obtained by evolutionary solver in Excel and a relative error of -0.013%. Above all, the stepwise regression method was successfully used to model the retardance of CA coated FFs, and the maximum global retardance was determined by the use of GA.
On pseudo-values for regression analysis in competing risks models
Graw, F; Gerds, Thomas Alexander; Schumacher, M
2009-01-01
For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15-27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their...
A Predictive Logistic Regression Model of World Conflict Using Open Source Data
2015-03-26
No correlation between the error terms and the independent variables 9. Absence of perfect multicollinearity (Menard, 2001) When assumptions are...some of the variables before initial model building. Multicollinearity , or near-linear dependence among the variables will cause problems in the...model. High multicollinearity tends to produce unreasonably high logistic regression coefficients and can result in coefficients that are not
Sample size calculation to externally validate scoring systems based on logistic regression models.
Antonio Palazón-Bru
Full Text Available A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence. Scoring systems based on binary logistic regression models are a specific type of predictive model.The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study.The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units.In the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature.An algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.
Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.
2006-01-01
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…
de Vries, S O; Fidler, Vaclav; Kuipers, Wietze D; Hunink, Maria G M
1998-01-01
The purpose of this study was to develop a model that predicts the outcome of supervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a
Endogenous glucose production from infancy to adulthood: a non-linear regression model
Huidekoper, Hidde H.; Ackermans, Mariëtte T.; Ruiter, An F. C.; Sauerwein, Hans P.; Wijburg, Frits A.
2014-01-01
To construct a regression model for endogenous glucose production (EGP) as a function of age, and compare this with glucose supplementation using commonly used dextrose-based saline solutions at fluid maintenance rate in children. A model was constructed based on EGP data, as quantified by
Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...
Kleijnen, J.P.C.
1995-01-01
This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for
Genomic prediction based on data from three layer lines using non-linear regression models
Huang, H.; Windig, J.J.; Vereijken, A.; Calus, M.P.L.
2014-01-01
Background - Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. Methods - In an attempt to alleviate
Logistic regression models of factors influencing the location of bioenergy and biofuels plants
T.M. Young; R.L. Zaretzki; J.H. Perdue; F.M. Guess; X. Liu
2011-01-01
Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of "thinnings to a basal area of 31.7m2/ha...
Determining factors influencing survival of breast cancer by fuzzy logistic regression model.
Nikbakht, Roya; Bahrampour, Abbas
2017-01-01
Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.
Photovoltaic Array Condition Monitoring Based on Online Regression of Performance Model
Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas
2013-01-01
regression modeling, from PV array production, plane-of-array irradiance, and module temperature measurements, acquired during an initial learning phase of the system. After the model has been parameterized automatically, the condition monitoring system enters the normal operation phase, where...
The use of logistic regression in modelling the distributions of bird ...
The method of logistic regression was used to model the observed geographical distribution patterns of bird species in Swaziland in relation to a set of environmental variables. Reporting rates derived from bird atlas data are used as an index of population densities. This is justified in part by the success of the modelling ...
Time series modeling by a regression approach based on a latent process.
Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice
2009-01-01
Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.
A LATENT CLASS POISSON REGRESSION-MODEL FOR HETEROGENEOUS COUNT DATA
WEDEL, M; DESARBO, WS; BULT, [No Value; RAMASWAMY, [No Value
1993-01-01
In this paper an approach is developed that accommodates heterogeneity in Poisson regression models for count data. The model developed assumes that heterogeneity arises from a distribution of both the intercept and the coefficients of the explanatory variables. We assume that the mixing
The limiting behavior of the estimated parameters in a misspecified random field regression model
Dahl, Christian Møller; Qin, Yu
This paper examines the limiting properties of the estimated parameters in the random field regression model recently proposed by Hamilton (Econometrica, 2001). Though the model is parametric, it enjoys the flexibility of the nonparametric approach since it can approximate a large collection of n...
Deep ensemble learning of sparse regression models for brain disease diagnosis.
Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang
2017-04-01
Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.
Bias and Uncertainty in Regression-Calibrated Models of Groundwater Flow in Heterogeneous Media
Cooley, R.L.; Christensen, Steen
2006-01-01
small. Model error is accounted for in the weighted nonlinear regression methodology developed to estimate θ* and assess model uncertainties by incorporating the second-moment matrix of the model errors into the weight matrix. Techniques developed by statisticians to analyze classical nonlinear...... are reduced in magnitude. Biases, correction factors, and confidence and prediction intervals were obtained for a test problem for which model error is large to test robustness of the methodology. Numerical results conform with the theoretical analysis....
Longitudinal beta regression models for analyzing health-related quality of life scores over time
Hunger Matthias
2012-09-01
Full Text Available Abstract Background Health-related quality of life (HRQL has become an increasingly important outcome parameter in clinical trials and epidemiological research. HRQL scores are typically bounded at both ends of the scale and often highly skewed. Several regression techniques have been proposed to model such data in cross-sectional studies, however, methods applicable in longitudinal research are less well researched. This study examined the use of beta regression models for analyzing longitudinal HRQL data using two empirical examples with distributional features typically encountered in practice. Methods We used SF-6D utility data from a German older age cohort study and stroke-specific HRQL data from a randomized controlled trial. We described the conceptual differences between mixed and marginal beta regression models and compared both models to the commonly used linear mixed model in terms of overall fit and predictive accuracy. Results At any measurement time, the beta distribution fitted the SF-6D utility data and stroke-specific HRQL data better than the normal distribution. The mixed beta model showed better likelihood-based fit statistics than the linear mixed model and respected the boundedness of the outcome variable. However, it tended to underestimate the true mean at the upper part of the distribution. Adjusted group means from marginal beta model and linear mixed model were nearly identical but differences could be observed with respect to standard errors. Conclusions Understanding the conceptual differences between mixed and marginal beta regression models is important for their proper use in the analysis of longitudinal HRQL data. Beta regression fits the typical distribution of HRQL data better than linear mixed models, however, if focus is on estimating group mean scores rather than making individual predictions, the two methods might not differ substantially.
Madarang, Krish J; Kang, Joo-Hyon
2014-06-01
Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.
Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.
Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H
2016-01-01
Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.
Caimmi, R.
2011-08-01
Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both
Shi, Jinfei; Zhu, Songqing; Chen, Ruwen
2017-12-01
An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.
Construction of risk prediction model of type 2 diabetes mellitus based on logistic regression
Li Jian
2017-01-01
Full Text Available Objective: to construct multi factor prediction model for the individual risk of T2DM, and to explore new ideas for early warning, prevention and personalized health services for T2DM. Methods: using logistic regression techniques to screen the risk factors for T2DM and construct the risk prediction model of T2DM. Results: Male’s risk prediction model logistic regression equation: logit(P=BMI × 0.735+ vegetables × (−0.671 + age × 0.838+ diastolic pressure × 0.296+ physical activity× (−2.287 + sleep ×(−0.009 +smoking ×0.214; Female’s risk prediction model logistic regression equation: logit(P=BMI ×1.979+ vegetables× (−0.292 + age × 1.355+ diastolic pressure× 0.522+ physical activity × (−2.287 + sleep × (−0.010.The area under the ROC curve of male was 0.83, the sensitivity was 0.72, the specificity was 0.86, the area under the ROC curve of female was 0.84, the sensitivity was 0.75, the specificity was 0.90. Conclusion: This study model data is from a compared study of nested case, the risk prediction model has been established by using the more mature logistic regression techniques, and the model is higher predictive sensitivity, specificity and stability.
Buonaccorsi, John P; Romeo, Giovanni; Thoresen, Magne
2018-03-01
When fitting regression models, measurement error in any of the predictors typically leads to biased coefficients and incorrect inferences. A plethora of methods have been proposed to correct for this. Obtaining standard errors and confidence intervals using the corrected estimators can be challenging and, in addition, there is concern about remaining bias in the corrected estimators. The bootstrap, which is one option to address these problems, has received limited attention in this context. It has usually been employed by simply resampling observations, which, while suitable in some situations, is not always formally justified. In addition, the simple bootstrap does not allow for estimating bias in non-linear models, including logistic regression. Model-based bootstrapping, which can potentially estimate bias in addition to being robust to the original sampling or whether the measurement error variance is constant or not, has received limited attention. However, it faces challenges that are not present in handling regression models with no measurement error. This article develops new methods for model-based bootstrapping when correcting for measurement error in logistic regression with replicate measures. The methodology is illustrated using two examples, and a series of simulations are carried out to assess and compare the simple and model-based bootstrap methods, as well as other standard methods. While not always perfect, the model-based approaches offer some distinct improvements over the other methods. © 2017, The International Biometric Society.
Multiple regression models for energy use in air-conditioned office buildings in different climates
Lam, Joseph C.; Wan, Kevin K.W.; Liu Dalong; Tsang, C.L.
2010-01-01
An attempt was made to develop multiple regression models for office buildings in the five major climates in China - severe cold, cold, hot summer and cold winter, mild, and hot summer and warm winter. A total of 12 key building design variables were identified through parametric and sensitivity analysis, and considered as inputs in the regression models. The coefficient of determination R 2 varies from 0.89 in Harbin to 0.97 in Kunming, indicating that 89-97% of the variations in annual building energy use can be explained by the changes in the 12 parameters. A pseudo-random number generator based on three simple multiplicative congruential generators was employed to generate random designs for evaluation of the regression models. The difference between regression-predicted and DOE-simulated annual building energy use are largely within 10%. It is envisaged that the regression models developed can be used to estimate the likely energy savings/penalty during the initial design stage when different building schemes and design concepts are being considered.
SPSS macros to compare any two fitted values from a regression model.
Weaver, Bruce; Dubois, Sacha
2012-12-01
In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.
Testing and Modeling Fuel Regression Rate in a Miniature Hybrid Burner
Luciano Fanton
2012-01-01
Full Text Available Ballistic characterization of an extended group of innovative HTPB-based solid fuel formulations for hybrid rocket propulsion was performed in a lab-scale burner. An optical time-resolved technique was used to assess the quasisteady regression history of single perforation, cylindrical samples. The effects of metalized additives and radiant heat transfer on the regression rate of such formulations were assessed. Under the investigated operating conditions and based on phenomenological models from the literature, analyses of the collected experimental data show an appreciable influence of the radiant heat flux from burnt gases and soot for both unloaded and loaded fuel formulations. Pure HTPB regression rate data are satisfactorily reproduced, while the impressive initial regression rates of metalized formulations require further assessment.
Regression analysis of informative current status data with the additive hazards model.
Zhao, Shishun; Hu, Tao; Ma, Ling; Wang, Peijie; Sun, Jianguo
2015-04-01
This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.
LINEAR REGRESSION MODEL ESTİMATİON FOR RIGHT CENSORED DATA
Ersin Yılmaz
2016-05-01
Full Text Available In this study, firstly we will define a right censored data. If we say shortly right-censored data is censoring values that above the exact line. This may be related with scaling device. And then we will use response variable acquainted from right-censored explanatory variables. Then the linear regression model will be estimated. For censored data’s existence, Kaplan-Meier weights will be used for the estimation of the model. With the weights regression model will be consistent and unbiased with that. And also there is a method for the censored data that is a semi parametric regression and this method also give useful results for censored data too. This study also might be useful for the health studies because of the censored data used in medical issues generally.
Li, Tao
2018-06-01
The complexity of aluminum electrolysis process leads the temperature for aluminum reduction cells hard to measure directly. However, temperature is the control center of aluminum production. To solve this problem, combining some aluminum plant's practice data, this paper presents a Soft-sensing model of temperature for aluminum electrolysis process on Improved Twin Support Vector Regression (ITSVR). ITSVR eliminates the slow learning speed of Support Vector Regression (SVR) and the over-fit risk of Twin Support Vector Regression (TSVR) by introducing a regularization term into the objective function of TSVR, which ensures the structural risk minimization principle and lower computational complexity. Finally, the model with some other parameters as auxiliary variable, predicts the temperature by ITSVR. The simulation result shows Soft-sensing model based on ITSVR has short time-consuming and better generalization.
Predicting risk and outcomes for frail older adults: an umbrella review of frailty screening tools
Apóstolo, João; Cooke, Richard; Bobrowicz-Campos, Elzbieta; Santana, Silvina; Marcucci, Maura; Cano, Antonio; Vollenbroek-Hutten, Miriam; Germini, Federico; Holland, Carol
2017-01-01
EXECUTIVE SUMMARY Background A scoping search identified systematic reviews on diagnostic accuracy and predictive ability of frailty measures in older adults. In most cases, research was confined to specific assessment measures related to a specific clinical model. Objectives To summarize the best available evidence from systematic reviews in relation to reliability, validity, diagnostic accuracy and predictive ability of frailty measures in older adults. Inclusion criteria Population Older adults aged 60 years or older recruited from community, primary care, long-term residential care and hospitals. Index test Available frailty measures in older adults. Reference test Cardiovascular Health Study phenotype model, the Canadian Study of Health and Aging cumulative deficit model, Comprehensive Geriatric Assessment or other reference tests. Diagnosis of interest Frailty defined as an age-related state of decreased physiological reserves characterized by an increased risk of poor clinical outcomes. Types of studies Quantitative systematic reviews. Search strategy A three-step search strategy was utilized to find systematic reviews, available in English, published between January 2001 and October 2015. Methodological quality Assessed by two independent reviewers using the Joanna Briggs Institute critical appraisal checklist for systematic reviews and research synthesis. Data extraction Two independent reviewers extracted data using the standardized data extraction tool designed for umbrella reviews. Data synthesis Data were only presented in a narrative form due to the heterogeneity of included reviews. Results Five reviews with a total of 227,381 participants were included in this umbrella review. Two reviews focused on reliability, validity and diagnostic accuracy; two examined predictive ability for adverse health outcomes; and one investigated validity, diagnostic accuracy and predictive ability. In total, 26 questionnaires and brief assessments and eight frailty
[Relationship between fall and frailty index in elderly adults of urban community in Beijing].
Zhou, B Y; Yu, D N; Tao, Y K; Shi, J; Yu, P L
2018-03-10
Objective: To evaluate the frailty status and understand the relationship between the incidence of fall and frailty status in the elderly in Beijing. Methods: A cross-sectional study was conducted in old people aged ≥60 years in Longtan community of Dongcheng district in Beijing from November 2015 to January 2016. The information about any fall during the past year and frailty status of the elderly were collected with a standardized structured questionnaire in face-to-face interviews. The frailty status of elderly people was assessed with frailty index (FI) method. Logistic regression analysis was used to explore the relationship between fall and frailty status among the elderly. Results: Among 1 557 old people surveyed, the incidence of fall was 17.8% (277/1 557) during the past year. The incidence of fall in women (21.0%, 192/277) was statistically higher than that in men (13.3%, 85/277) ( χ (2)=15.288, P =0.000). The median (quartile) value of FI of the elderly surveyed was 0.09 (0.08); and women had a higher FI median value than men [0.10 (0.08) versus 0.08 (0.07)]( Z =5.376, P =0.000). The median FI value (quartile range) of 277 old people with history of fall in previous year was 0.12 (0.11), which was higher than the median FI value of 0.08 (0.07) of 1 280 old people without fall history ( Z =7.501, P =0.000). Logistic regression analysis showed that higher FI value was associated with more risks for fall; and FI value showed the greatest impact on the incidence and frequency of fall ( OR =1.093, 2.234) compared with other related factors of fall, such as age and gender. Conclusion: Frailty status has a greater impact on both incidence and frequency of fall compared with other factors in elderly people in Beijing; more attention should be paid to weak and old adults in the prevention of fall.
Mijnarends, Donja M; Schols, Jos M G A; Meijers, Judith M M; Tan, Frans E S; Verlaan, Sjors; Luiking, Yvette C; Morley, John E; Halfens, Ruud J G
2015-04-01
Both sarcopenia and physical frailty are geriatric syndromes causing loss of functionality and independence. This study explored the association between sarcopenia and physical frailty and the overlap of their criteria in older people living in different community (care) settings. Moreover, it investigated the concurrent validity of the FRAIL scale to assess physical frailty, by comparison with the widely used Fried criteria. Data were retrieved from the cross-sectional Maastricht Sarcopenia Study (MaSS). The study was undertaken in different community care settings in an urban area (Maastricht) in the south of the Netherlands. Participants were 65 years or older, gave written informed consent, were able to understand Dutch language, and were not wheelchair bound or bedridden. Not applicable. Sarcopenia was identified using the algorithm of the European Working Group on Sarcopenia in Older People. Physical frailty was assessed by the Fried criteria and by the FRAIL scale. Logistic regression was performed to assess the association between sarcopenia and physical frailty measured by the Fried criteria. Spearman correlation was performed to assess the concurrent validity of the FRAIL scale compared with the Fried criteria. Data from 227 participants, mean age 74.9 years, were analyzed. Sarcopenia was identified in 23.3% of the participants, when using the cutoff levels for moderate sarcopenia. Physical frailty was identified in 8.4% (≥3 Fried criteria) and 9.3% (≥3 FRAIL scale criteria) of the study population. Sarcopenia and physical frailty were significantly associated (P = .022). Frail older people were more likely to be sarcopenic than those who were not frail. In older people who were not frail, the risk of having sarcopenia increased with age. Next to poor grip strength (78.9%) and slow gait speed (89.5%), poor performance in other functional tests was common in frail older people. The 2 physical frailty scales were significantly correlated (r = 0.617, P
Combination of supervised and semi-supervised regression models for improved unbiased estimation
Arenas-Garía, Jeronimo; Moriana-Varo, Carlos; Larsen, Jan
2010-01-01
In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised and semisupervi......In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised...
Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm
Ulbrich, Norbert Manfred
2013-01-01
A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.
Keat, Sim Chong; Chun, Beh Boon; San, Lim Hwee; Jafri, Mohd Zubir Mat
2015-04-01
Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model's accuracy and efficiency. These results are accurate and can be used in early warning of the population to comply with air quality standards.
Feng, Yongjiu; Tong, Xiaohua
2017-09-22
Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also applied to simulate the land use change. Simulations using three CA models with multicollinearity eliminated performed better (with accuracy improvements about 3.6%) than the model incorporating all candidate factors. Our results showed that not all candidate factors are necessary for accurate CA modeling and the simulations were not sensitive to changes in statistically non-significant driving factors. We conclude that exploratory regression is an effective method to search for the optimal combinations of driving factors, leading to better land use change models that are devoid of multicollinearity. We suggest identification of dominant factors and elimination of multicollinearity before building land change models, making it possible to simulate more realistic outcomes.
Bruno, Delia Evelina; Barca, Emanuele; Goncalves, Rodrigo Mikosz; de Araujo Queiroz, Heithor Alexandre; Berardi, Luigi; Passarella, Giuseppe
2018-01-01
In this paper, the Evolutionary Polynomial Regression data modelling strategy has been applied to study small scale, short-term coastal morphodynamics, given its capability for treating a wide database of known information, non-linearly. Simple linear and multilinear regression models were also applied to achieve a balance between the computational load and reliability of estimations of the three models. In fact, even though it is easy to imagine that the more complex the model, the more the prediction improves, sometimes a "slight" worsening of estimations can be accepted in exchange for the time saved in data organization and computational load. The models' outcomes were validated through a detailed statistical, error analysis, which revealed a slightly better estimation of the polynomial model with respect to the multilinear model, as expected. On the other hand, even though the data organization was identical for the two models, the multilinear one required a simpler simulation setting and a faster run time. Finally, the most reliable evolutionary polynomial regression model was used in order to make some conjecture about the uncertainty increase with the extension of extrapolation time of the estimation. The overlapping rate between the confidence band of the mean of the known coast position and the prediction band of the estimated position can be a good index of the weakness in producing reliable estimations when the extrapolation time increases too much. The proposed models and tests have been applied to a coastal sector located nearby Torre Colimena in the Apulia region, south Italy.
Accounting for spatial effects in land use regression for urban air pollution modeling.
Bertazzon, Stefania; Johnson, Markey; Eccles, Kristin; Kaplan, Gilaad G
2015-01-01
In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris
2016-09-01
Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have
Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E.
2018-03-01
Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009-2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10-20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.
Oral Frailty as a Risk Factor for Physical Frailty and Mortality in Community-Dwelling Elderly.
Tanaka, Tomoki; Takahashi, Kyo; Hirano, Hirohiko; Kikutani, Takeshi; Watanabe, Yutaka; Ohara, Yuki; Furuya, Hiroyasu; Tsuji, Tetsuo; Akishita, Masahiro; Iijima, Katsuya
2017-11-17
Oral health is important for maintaining general health among the elderly. However, a longitudinal association between poor oral health and general health has not been reported. We investigated whether poor oral status can predict physical weakening (physical frailty, sarcopenia, subsequent disability) and identified the longitudinal impact of the accumulated poor oral health (i.e., oral frailty) on adverse health outcomes, including mortality. A total of 2,011 elderly individuals (aged ≥65 years) participated in the baseline survey of the Kashiwa study in 2012. At baseline, 16 oral status measures and covariates such as demographic characteristics were assessed. As outcomes, physical frailty and sarcopenia were assessed at baseline and at follow-up in 2013 and 2014. Physical independence and survival were assessed from 2012 to 2016 at the time of long-term care certification and time of death. Poor oral status as determined by the number of natural teeth, chewing ability, articulatory oral motor skill, tongue pressure, and subjective difficulties in eating and swallowing significantly predicted future physical weakening (new-onsets of physical frailty, sarcopenia, and disability). Oral frailty was defined as co-existing poor status in ≥3 of the 6 measures. Sixteen percent of participants had oral frailty at baseline, which was significantly associated with 2.4-, 2.2-, 2.3-, and 2.2-fold increased risk of physical frailty, sarcopenia, disability, and mortality, respectively. Accumulated poor oral status strongly predicted the onset of adverse health outcomes, including mortality among the community-dwelling elderly. Prevention of oral frailty at an earlier stage is essential for healthy aging. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Nutritional determinants of frailty in older adults: A systematic review.
Lorenzo-López, Laura; Maseda, Ana; de Labra, Carmen; Regueiro-Folgueira, Laura; Rodríguez-Villamil, José L; Millán-Calenti, José C
2017-05-15
Frailty is a geriatric syndrome that affects multiple domains of human functioning. A variety of problems contributes to the development of this syndrome; poor nutritional status is an important determinant of this condition. The purpose of this systematic review was to examine recent evidence regarding the association between nutritional status and frailty syndrome in older adults. PubMed, Web of Science, and Scopus electronic databases were searched using specific key words, for observational papers that were published during the period from 2005 to February 2017 and that studied the association or relationship between nutritional status and frailty in older adults. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement was followed to assess the quality of the included articles. Of the 2042 studies found, nineteen met the inclusion criteria. Of these studies, five provided data on micronutrients and frailty, and reported that frailty syndrome is associated with low intakes of specific micronutrients. Five studies provided data on macronutrients and frailty, and among those studies, four revealed that a higher protein intake was associated with a lower risk of frailty. Three studies examined the relationship between diet quality and frailty, and showed that the quality of the diet is inversely associated with the risk of being frail. Two studies provided data on the antioxidant capacity of the diet and frailty, and reported that a high dietary antioxidant capacity is associated with a lower risk of developing frailty. Finally, seven studies evaluated the relationship between scores on both the Mini Nutritional Assessment (MNA) and the MNA-SF (Short Form) and frailty, and revealed an association between malnutrition and/or the risk of malnutrition and frailty. This systematic review confirms the importance of both quantitative (energy intake) and qualitative (nutrient quality) factors of nutrition in the development of frailty
Accounting for Zero Inflation of Mussel Parasite Counts Using Discrete Regression Models
Emel Çankaya
2017-06-01
Full Text Available In many ecological applications, the absences of species are inevitable due to either detection faults in samples or uninhabitable conditions for their existence, resulting in high number of zero counts or abundance. Usual practice for modelling such data is regression modelling of log(abundance+1 and it is well know that resulting model is inadequate for prediction purposes. New discrete models accounting for zero abundances, namely zero-inflated regression (ZIP and ZINB, Hurdle-Poisson (HP and Hurdle-Negative Binomial (HNB amongst others are widely preferred to the classical regression models. Due to the fact that mussels are one of the economically most important aquatic products of Turkey, the purpose of this study is therefore to examine the performances of these four models in determination of the significant biotic and abiotic factors on the occurrences of Nematopsis legeri parasite harming the existence of Mediterranean mussels (Mytilus galloprovincialis L.. The data collected from the three coastal regions of Sinop city in Turkey showed more than 50% of parasite counts on the average are zero-valued and model comparisons were based on information criterion. The results showed that the probability of the occurrence of this parasite is here best formulated by ZINB or HNB models and influential factors of models were found to be correspondent with ecological differences of the regions.
Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen
2014-01-01
It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models. PMID:24574916
Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen
2014-01-01
It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.
Alvarez R, J.T.; Morales P, R.
1992-06-01
The absorbed dose for equivalent soft tissue is determined,it is imparted by ophthalmologic applicators, ( 90 Sr/ 90 Y, 1850 MBq) using an extrapolation chamber of variable electrodes; when estimating the slope of the extrapolation curve using a simple lineal regression model is observed that the dose values are underestimated from 17.7 percent up to a 20.4 percent in relation to the estimate of this dose by means of a regression model polynomial two grade, at the same time are observed an improvement in the standard error for the quadratic model until in 50%. Finally the global uncertainty of the dose is presented, taking into account the reproducibility of the experimental arrangement. As conclusion it can infers that in experimental arrangements where the source is to contact with the extrapolation chamber, it was recommended to substitute the lineal regression model by the quadratic regression model, in the determination of the slope of the extrapolation curve, for more exact and accurate measurements of the absorbed dose. (Author)
Wen-Cheng Wang
2014-01-01
Full Text Available It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.
Replica analysis of overfitting in regression models for time-to-event data
Coolen, A. C. C.; Barrett, J. E.; Paga, P.; Perez-Vicente, C. J.
2017-09-01
Overfitting, which happens when the number of parameters in a model is too large compared to the number of data points available for determining these parameters, is a serious and growing problem in survival analysis. While modern medicine presents us with data of unprecedented dimensionality, these data cannot yet be used effectively for clinical outcome prediction. Standard error measures in maximum likelihood regression, such as p-values and z-scores, are blind to overfitting, and even for Cox’s proportional hazards model (the main tool of medical statisticians), one finds in literature only rules of thumb on the number of samples required to avoid overfitting. In this paper we present a mathematical theory of overfitting in regression models for time-to-event data, which aims to increase our quantitative understanding of the problem and provide practical tools with which to correct regression outcomes for the impact of overfitting. It is based on the replica method, a statistical mechanical technique for the analysis of heterogeneous many-variable systems that has been used successfully for several decades in physics, biology, and computer science, but not yet in medical statistics. We develop the theory initially for arbitrary regression models for time-to-event data, and verify its predictions in detail for the popular Cox model.
Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors
Xibin Zhang
2016-04-01
Full Text Available This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function is estimated using the Nadaraya-Watson estimator admitting continuous and discrete regressors. We derive an approximate likelihood and posterior for bandwidth parameters, followed by a sampling algorithm. Simulation results show that the proposed approach typically leads to better accuracy of the resulting estimates than cross-validation, particularly for smaller sample sizes. This bandwidth estimation approach is applied to nonparametric regression model of the Australian All Ordinaries returns and the kernel density estimation of gross domestic product (GDP growth rates among the organisation for economic co-operation and development (OECD and non-OECD countries.
INVESTIGATION OF E-MAIL TRAFFIC BY USING ZERO-INFLATED REGRESSION MODELS
Yılmaz KAYA
2012-06-01
Full Text Available Based on count data obtained with a value of zero may be greater than anticipated. These types of data sets should be used to analyze by regression methods taking into account zero values. Zero- Inflated Poisson (ZIP, Zero-Inflated negative binomial (ZINB, Poisson Hurdle (PH, negative binomial Hurdle (NBH are more common approaches in modeling more zero value possessing dependent variables than expected. In the present study, the e-mail traffic of Yüzüncü Yıl University in 2009 spring semester was investigated. ZIP and ZINB, PH and NBH regression methods were applied on the data set because more zeros counting (78.9% were found in data set than expected. ZINB and NBH regression considered zero dispersion and overdispersion were found to be more accurate results due to overdispersion and zero dispersion in sending e-mail. ZINB is determined to be best model accordingto Vuong statistics and information criteria.
Anke Hüls
2017-05-01
Full Text Available Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model and (ii to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate
Yao, Longbiao; Heuser-Baker, Janet; Herlea-Pana, Oana; Iida, Ryuji; Wang, Qilong; Zou, Ming-Hui; Barlic-Dicen, Jana
2012-01-01
The major event initiating atherosclerosis is hypercholesterolemia-induced disruption of vascular endothelium integrity. In settings of endothelial damage, endothelial progenitor cells (EPCs) are mobilized from bone marrow into circulation and home to sites of vascular injury where they aid endothelial regeneration. Given the beneficial effects of EPCs in vascular repair, we hypothesized that these cells play a pivotal role in atherosclerosis regression. We tested our hypothesis in the atherosclerosis-prone mouse model in which hypercholesterolemia, one of the main factors affecting EPC homeostasis, is reversible (Reversa mice). In these mice normalization of plasma lipids decreased atherosclerotic burden; however, plaque regression was incomplete. To explore whether endothelial progenitors contribute to atherosclerosis regression, bone marrow EPCs from a transgenic strain expressing green fluorescent protein under the control of endothelial cell-specific Tie2 promoter (Tie2-GFP+) were isolated. These cells were then adoptively transferred into atheroregressing Reversa recipients where they augmented plaque regression induced by reversal of hypercholesterolemia. Advanced plaque regression correlated with engraftment of Tie2-GFP+ EPCs into endothelium and resulted in an increase in atheroprotective nitric oxide and improved vascular relaxation. Similarly augmented plaque regression was also detected in regressing Reversa mice treated with the stem cell mobilizer AMD3100 which also mobilizes EPCs to peripheral blood. We conclude that correction of hypercholesterolemia in Reversa mice leads to partial plaque regression that can be augmented by AMD3100 treatment or by adoptive transfer of EPCs. This suggests that direct cell therapy or indirect progenitor cell mobilization therapy may be used in combination with statins to treat atherosclerosis. PMID:23081735
Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P
2015-01-01
This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of
Application of Soft Computing Techniques and Multiple Regression Models for CBR prediction of Soils
Fatimah Khaleel Ibrahim
2017-08-01
Full Text Available The techniques of soft computing technique such as Artificial Neutral Network (ANN have improved the predicting capability and have actually discovered application in Geotechnical engineering. The aim of this research is to utilize the soft computing technique and Multiple Regression Models (MLR for forecasting the California bearing ratio CBR( of soil from its index properties. The indicator of CBR for soil could be predicted from various soils characterizing parameters with the assist of MLR and ANN methods. The data base that collected from the laboratory by conducting tests on 86 soil samples that gathered from different projects in Basrah districts. Data gained from the experimental result were used in the regression models and soft computing techniques by using artificial neural network. The liquid limit, plastic index , modified compaction test and the CBR test have been determined. In this work, different ANN and MLR models were formulated with the different collection of inputs to be able to recognize their significance in the prediction of CBR. The strengths of the models that were developed been examined in terms of regression coefficient (R2, relative error (RE% and mean square error (MSE values. From the results of this paper, it absolutely was noticed that all the proposed ANN models perform better than that of MLR model. In a specific ANN model with all input parameters reveals better outcomes than other ANN models.
Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data.
Mi, Gu; Di, Yanming; Schafer, Daniel W
2015-01-01
This work is about assessing model adequacy for negative binomial (NB) regression, particularly (1) assessing the adequacy of the NB assumption, and (2) assessing the appropriateness of models for NB dispersion parameters. Tools for the first are appropriate for NB regression generally; those for the second are primarily intended for RNA sequencing (RNA-Seq) data analysis. The typically small number of biological samples and large number of genes in RNA-Seq analysis motivate us to address the trade-offs between robustness and statistical power using NB regression models. One widely-used power-saving strategy, for example, is to assume some commonalities of NB dispersion parameters across genes via simple models relating them to mean expression rates, and many such models have been proposed. As RNA-Seq analysis is becoming ever more popular, it is appropriate to make more thorough investigations into power and robustness of the resulting methods, and into practical tools for model assessment. In this article, we propose simulation-based statistical tests and diagnostic graphics to address model adequacy. We provide simulated and real data examples to illustrate that our proposed methods are effective for detecting the misspecification of the NB mean-variance relationship as well as judging the adequacy of fit of several NB dispersion models.
Schmidtmann, I; Elsäßer, A; Weinmann, A; Binder, H
2014-12-30
For determining a manageable set of covariates potentially influential with respect to a time-to-event endpoint, Cox proportional hazards models can be combined with variable selection techniques, such as stepwise forward selection or backward elimination based on p-values, or regularized regression techniques such as component-wise boosting. Cox regression models have also been adapted for dealing with more complex event patterns, for example, for competing risks settings with separate, cause-specific hazard models for each event type, or for determining the prognostic effect pattern of a variable over different landmark times, with one conditional survival model for each landmark. Motivated by a clinical cancer registry application, where complex event patterns have to be dealt with and variable selection is needed at the same time, we propose a general approach for linking variable selection between several Cox models. Specifically, we combine score statistics for each covariate across models by Fisher's method as a basis for variable selection. This principle is implemented for a stepwise forward selection approach as well as for a regularized regression technique. In an application to data from hepatocellular carcinoma patients, the coupled stepwise approach is seen to facilitate joint interpretation of the different cause-specific Cox models. In conditional survival models at landmark times, which address updates of prediction as time progresses and both treatment and other potential explanatory variables may change, the coupled regularized regression approach identifies potentially important, stably selected covariates together with their effect time pattern, despite having only a small number of events. These results highlight the promise of the proposed approach for coupling variable selection between Cox models, which is particularly relevant for modeling for clinical cancer registries with their complex event patterns. Copyright © 2014 John Wiley & Sons
Andreasen, Jane; Aadahl, Mette; Sørensen, Erik Elgaard
2018-01-01
OBJECTIVE: To assess whether frailty in acutely admitted older medical patients, assessed by a self-report questionnaire and evaluation of functional level at discharge, was associated with readmission or death within 6 months after discharge. A second objective was to assess the predictive...... measured. Associations were assessed using Cox regression with first unplanned readmission or death (all-causes) as the outcome. Prediction models including the three exposure variables and known risk factors were modelled using logistic regression and C-statistics. RESULTS: Of 1328 included patients, 50......% were readmitted or died within 6 months. When adjusted for gender and age, there was an 88% higher risk of readmission or death if the TFI scores were 8-13 points compared to 0-1 points (HR 1.88, CI 1.38;2.58). Likewise, higher TUG and lower GS scores were associated with higher risk of readmission...
Significance tests to determine the direction of effects in linear regression models.
Wiedermann, Wolfgang; Hagmann, Michael; von Eye, Alexander
2015-02-01
Previous studies have discussed asymmetric interpretations of the Pearson correlation coefficient and have shown that higher moments can be used to decide on the direction of dependence in the bivariate linear regression setting. The current study extends this approach by illustrating that the third moment of regression residuals may also be used to derive conclusions concerning the direction of effects. Assuming non-normally distributed variables, it is shown that the distribution of residuals of the correctly specified regression model (e.g., Y is regressed on X) is more symmetric than the distribution of residuals of the competing model (i.e., X is regressed on Y). Based on this result, 4 one-sample tests are discussed which can be used to decide which variable is more likely to be the response and which one is more likely to be the explanatory variable. A fifth significance test is proposed based on the differences of skewness estimates, which leads to a more direct test of a hypothesis that is compatible with direction of dependence. A Monte Carlo simulation study was performed to examine the behaviour of the procedures under various degrees of associations, sample sizes, and distributional properties of the underlying population. An empirical example is given which illustrates the application of the tests in practice. © 2014 The British Psychological Society.
Suzuki, Makoto; Sugimura, Yuko; Yamada, Sumio; Omori, Yoshitsugu; Miyamoto, Masaaki; Yamamoto, Jun-ichi
2013-01-01
Cognitive disorders in the acute stage of stroke are common and are important independent predictors of adverse outcome in the long term. Despite the impact of cognitive disorders on both patients and their families, it is still difficult to predict the extent or duration of cognitive impairments. The objective of the present study was, therefore, to provide data on predicting the recovery of cognitive function soon after stroke by differential modeling with logarithmic and linear regression. This study included two rounds of data collection comprising 57 stroke patients enrolled in the first round for the purpose of identifying the time course of cognitive recovery in the early-phase group data, and 43 stroke patients in the second round for the purpose of ensuring that the correlation of the early-phase group data applied to the prediction of each individual's degree of cognitive recovery. In the first round, Mini-Mental State Examination (MMSE) scores were assessed 3 times during hospitalization, and the scores were regressed on the logarithm and linear of time. In the second round, calculations of MMSE scores were made for the first two scoring times after admission to tailor the structures of logarithmic and linear regression formulae to fit an individual's degree of functional recovery. The time course of early-phase recovery for cognitive functions resembled both logarithmic and linear functions. However, MMSE scores sampled at two baseline points based on logarithmic regression modeling could estimate prediction of cognitive recovery more accurately than could linear regression modeling (logarithmic modeling, R(2) = 0.676, PLogarithmic modeling based on MMSE scores could accurately predict the recovery of cognitive function soon after the occurrence of stroke. This logarithmic modeling with mathematical procedures is simple enough to be adopted in daily clinical practice.
Climate Impacts on Chinese Corn Yields: A Fractional Polynomial Regression Model
Kooten, van G.C.; Sun, Baojing
2012-01-01
In this study, we examine the effect of climate on corn yields in northern China using data from ten districts in Inner Mongolia and two in Shaanxi province. A regression model with a flexible functional form is specified, with explanatory variables that include seasonal growing degree days,
Estimation of Panel Data Regression Models with Two-Sided Censoring or Truncation
Alan, Sule; Honore, Bo E.; Hu, Luojia
2014-01-01
This paper constructs estimators for panel data regression models with individual speci…fic heterogeneity and two–sided censoring and truncation. Following Powell (1986) the estimation strategy is based on moment conditions constructed from re–censored or re–truncated residuals. While these moment...
Bianca N.I. Eskelson; Hailemariam Temesgen; Tara M. Barrett
2009-01-01
Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods....
A random regression model in analysis of litter size in pigs | Lukovi& ...
Dispersion parameters for number of piglets born alive (NBA) were estimated using a random regression model (RRM). Two data sets of litter records from the Nemščak farm in Slovenia were used for analyses. The first dataset (DS1) included records from the first to the sixth parity. The second dataset (DS2) was extended ...
Tripathy, G.R.; Das, Anirban.
used methods, the Least Square Regression (LSR) and Inverse Modeling (IM), to determine the contributions of (i) solutes from different sources to global river water, and (ii) various rocks to a glacial till. The purpose of this exercise is to compare...
Ling, Ru; Liu, Jiawang
2011-12-01
To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.
Clinical trials: odds ratios and multiple regression models--why and how to assess them
Sobh, Mohamad; Cleophas, Ton J.; Hadj-Chaib, Amel; Zwinderman, Aeilko H.
2008-01-01
Odds ratios (ORs), unlike chi2 tests, provide direct insight into the strength of the relationship between treatment modalities and treatment effects. Multiple regression models can reduce the data spread due to certain patient characteristics and thus improve the precision of the treatment
Susan L. King
2003-01-01
The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...
Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza
2014-01-01
This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…
Reduction of the number of parameters needed for a polynomial random regression test-day model
Pool, M.H.; Meuwissen, T.H.E.
2000-01-01
Legendre polynomials were used to describe the (co)variance matrix within a random regression test day model. The goodness of fit depended on the polynomial order of fit, i.e., number of parameters to be estimated per animal but is limited by computing capacity. Two aspects: incomplete lactation
Li, Spencer D.
2011-01-01
Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…
Walter, G.M.; Augustin, Th.; Kneib, Thomas; Tutz, Gerhard
2010-01-01
The paper is concerned with Bayesian analysis under prior-data conflict, i.e. the situation when observed data are rather unexpected under the prior (and the sample size is not large enough to eliminate the influence of the prior). Two approaches for Bayesian linear regression modeling based on
Petersen, Jørgen Holm
2016-01-01
This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied...
The Development and Demonstration of Multiple Regression Models for Operant Conditioning Questions.
Fanning, Fred; Newman, Isadore
Based on the assumption that inferential statistics can make the operant conditioner more sensitive to possible significant relationships, regressions models were developed to test the statistical significance between slopes and Y intercepts of the experimental and control group subjects. These results were then compared to the traditional operant…
Pivotal statistics for testing subsets of structural parameters in the IV Regression Model
Kleibergen, F.R.
2000-01-01
We construct a novel statistic to test hypothezes on subsets of the structural parameters in anInstrumental Variables (IV) regression model. We derive the chi squared limiting distribution of thestatistic and show that it has a degrees of freedom parameter that is equal to the number ofstructural
The prediction of intelligence in preschool children using alternative models to regression.
Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E
2011-12-01
Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.
Kleijnen, J.P.C.
2006-01-01
Classic linear regression models and their concomitant statistical designs assume a univariate response and white noise.By definition, white noise is normally, independently, and identically distributed with zero mean.This survey tries to answer the following questions: (i) How realistic are these
de Peinder, P.; Visser, T.; Wagemans, R.W.P.; Blomberg, J.; Chaabani, H.; Soulimani, F.; Weckhuysen, B.M.
2013-01-01
Research has been carried out to determine the feasibility of partial least-squares regression (PLS) modeling of infrared (IR) spectra of crude oils as a tool for fast sulfur speciation. The study is a continuation of a previously developed method to predict long and short residue properties of
Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...
Regression-based model of skin diffuse reflectance for skin color analysis
Tsumura, Norimichi; Kawazoe, Daisuke; Nakaguchi, Toshiya; Ojima, Nobutoshi; Miyake, Yoichi
2008-11-01
A simple regression-based model of skin diffuse reflectance is developed based on reflectance samples calculated by Monte Carlo simulation of light transport in a two-layered skin model. This reflectance model includes the values of spectral reflectance in the visible spectra for Japanese women. The modified Lambert Beer law holds in the proposed model with a modified mean free path length in non-linear density space. The averaged RMS and maximum errors of the proposed model were 1.1 and 3.1%, respectively, in the above range.
Menon Carlo
2011-09-01
Full Text Available Abstract Background Several regression models have been proposed for estimation of isometric joint torque using surface electromyography (SEMG signals. Common issues related to torque estimation models are degradation of model accuracy with passage of time, electrode displacement, and alteration of limb posture. This work compares the performance of the most commonly used regression models under these circumstances, in order to assist researchers with identifying the most appropriate model for a specific biomedical application. Methods Eleven healthy volunteers participated in this study. A custom-built rig, equipped with a torque sensor, was used to measure isometric torque as each volunteer flexed and extended his wrist. SEMG signals from eight forearm muscles, in addition to wrist joint torque data were gathered during the experiment. Additional data were gathered one hour and twenty-four hours following the completion of the first data gathering session, for the purpose of evaluating the effects of passage of time and electrode displacement on accuracy of models. Acquired SEMG signals were filtered, rectified, normalized and then fed to models for training. Results It was shown that mean adjusted coefficient of determination (Ra2 values decrease between 20%-35% for different models after one hour while altering arm posture decreased mean Ra2 values between 64% to 74% for different models. Conclusions Model estimation accuracy drops significantly with passage of time, electrode displacement, and alteration of limb posture. Therefore model retraining is crucial for preserving estimation accuracy. Data resampling can significantly reduce model training time without losing estimation accuracy. Among the models compared, ordinary least squares linear regression model (OLS was shown to have high isometric torque estimation accuracy combined with very short training times.
Modeling of chemical exergy of agricultural biomass using improved general regression neural network
Huang, Y.W.; Chen, M.Q.; Li, Y.; Guo, J.
2016-01-01
A comprehensive evaluation for energy potential contained in agricultural biomass was a vital step for energy utilization of agricultural biomass. The chemical exergy of typical agricultural biomass was evaluated based on the second law of thermodynamics. The chemical exergy was significantly influenced by C and O elements rather than H element. The standard entropy of the samples also was examined based on their element compositions. Two predicted models of the chemical exergy were developed, which referred to a general regression neural network model based upon the element composition, and a linear model based upon the high heat value. An auto-refinement algorithm was firstly developed to improve the performance of regression neural network model. The developed general regression neural network model with K-fold cross-validation had a better ability for predicting the chemical exergy than the linear model, which had lower predicted errors (±1.5%). - Highlights: • Chemical exergies of agricultural biomass were evaluated based upon fifty samples. • Values for the standard entropy of agricultural biomass samples were calculated. • A linear relationship between chemical exergy and HHV of samples was detected. • An improved GRNN prediction model for the chemical exergy of biomass was developed.
Random regression models for daily feed intake in Danish Duroc pigs
Strathe, Anders Bjerring; Mark, Thomas; Jensen, Just
The objective of this study was to develop random regression models and estimate covariance functions for daily feed intake (DFI) in Danish Duroc pigs. A total of 476201 DFI records were available on 6542 Duroc boars between 70 to 160 days of age. The data originated from the National test station......-year-season, permanent, and animal genetic effects. The functional form was based on Legendre polynomials. A total of 64 models for random regressions were initially ranked by BIC to identify the approximate order for the Legendre polynomials using AI-REML. The parsimonious model included Legendre polynomials of 2nd...... order for genetic and permanent environmental curves and a heterogeneous residual variance, allowing the daily residual variance to change along the age trajectory due to scale effects. The parameters of the model were estimated in a Bayesian framework, using the RJMC module of the DMU package, where...
Use of a Regression Model to Study Host-Genomic Determinants of Phage Susceptibility in MRSA
Zschach, Henrike; Larsen, Mette V; Hasman, Henrik
2018-01-01
strains to 12 (nine monovalent) different therapeutic phage preparations and subsequently employed linear regression models to estimate the influence of individual host gene families on resistance to phages. Specifically, we used a two-step regression model setup with a preselection step based on gene...... family enrichment. We show that our models are robust and capture the data's underlying signal by comparing their performance to that of models build on randomized data. In doing so, we have identified 167 gene families that govern phage resistance in our strain set and performed functional analysis...... on them. This revealed genes of possible prophage or mobile genetic element origin, along with genes involved in restriction-modification and transcription regulators, though the majority were genes of unknown function. This study is a step in the direction of understanding the intricate host...
Hong, W.-C.
2009-01-01
Accurate forecasting of electric load has always been the most important issues in the electricity industry, particularly for developing countries. Due to the various influences, electric load forecasting reveals highly nonlinear characteristics. Recently, support vector regression (SVR), with nonlinear mapping capabilities of forecasting, has been successfully employed to solve nonlinear regression and time series problems. However, it is still lack of systematic approaches to determine appropriate parameter combination for a SVR model. This investigation elucidates the feasibility of applying chaotic particle swarm optimization (CPSO) algorithm to choose the suitable parameter combination for a SVR model. The empirical results reveal that the proposed model outperforms the other two models applying other algorithms, genetic algorithm (GA) and simulated annealing algorithm (SA). Finally, it also provides the theoretical exploration of the electric load forecasting support system (ELFSS)
BUDIMAN
2012-01-01
Full Text Available Budiman, Arisoesilaningsih E. 2012. Predictive model of Amorphophallus muelleri growth in some agroforestry in East Java by multiple regression analysis. Biodiversitas 13: 18-22. The aims of this research was to determine the multiple regression models of vegetative and corm growth of Amorphophallus muelleri Blume in some age variations and habitat conditions of agroforestry in East Java. Descriptive exploratory research method was conducted by systematic random sampling at five agroforestries on four plantations in East Java: Saradan, Bojonegoro, Nganjuk and Blitar. In each agroforestry, we observed A. muelleri vegetative and corm growth on four growing age (1, 2, 3 and 4 years old respectively as well as environmental variables such as altitude, vegetation, climate and soil conditions. Data were analyzed using descriptive statistics to compare A. muelleri habitat in five agroforestries. Meanwhile, the influence and contribution of each environmental variable to the growth of A. muelleri vegetative and corm were determined using multiple regression analysis of SPSS 17.0. The multiple regression models of A. muelleri vegetative and corm growth were generated based on some characteristics of agroforestries and age showed high validity with R2 = 88-99%. Regression model showed that age, monthly temperatures, percentage of radiation and soil calcium (Ca content either simultaneously or partially determined the growth of A. muelleri vegetative and corm. Based on these models, the A. muelleri corm reached the optimal growth after four years of cultivation and they will be ready to be harvested. Additionally, the soil Ca content should reach 25.3 me.hg-1 as Sugihwaras agroforestry, with the maximal radiation of 60%.
Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal
2005-09-01
To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.
A brief introduction to regression designs and mixed-effects modelling by a recent convert
Balling, Laura Winther
2008-01-01
This article discusses the advantages of multiple regression designs over the factorial designs traditionally used in many psycholinguistic experiments. It is shown that regression designs are typically more informative, statistically more powerful and better suited to the analysis of naturalistic...... tasks. The advantages of including both fixed and random effects are demonstrated with reference to linear mixed-effects models, and problems of collinearity, variable distribution and variable selection are discussed. The advantages of these techniques are exemplified in an analysis of a word...
Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei
2014-10-01
Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.
James W. Hardin; Henrik Schmeidiche; Raymond J. Carroll
2003-01-01
This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on s...
Estimating transmitted waves of floating breakwater using support vector regression model
Mandal, S.; Hegde, A.V.; Kumar, V.; Patil, S.G.
is first mapped onto an m-dimensional feature space using some fixed (nonlinear) mapping, and then a linear model is constructed in this feature space (Ivanciuc Ovidiu 2007). Using mathematical notation, the linear model in the feature space f(x, w... regressive vector machines, Ocean Engineering Journal, Vol – 36, pp 339 – 347, 2009. 3. Ivanciuc Ovidiu, Applications of support vector machines in chemistry, Review in Computational Chemistry, Eds K. B. Lipkouitz and T. R. Cundari, Vol – 23...
Nobuoki, Eshima; Minoru, Tabata; Geng, Zhi; Department of Medical Information Analysis, Faculty of Medicine, Oita Medical University; Department of Applied Mathematics, Faculty of Engineering, Kobe University; Department of Probability and Statistics, Peking University
2001-01-01
This paper discusses path analysis of categorical variables with logistic regression models. The total, direct and indirect effects in fully recursive causal systems are considered by using model parameters. These effects can be explained in terms of log odds ratios, uncertainty differences, and an inner product of explanatory variables and a response variable. A study on food choice of alligators as a numerical exampleis reanalysed to illustrate the present approach.
Samsuri Abdullah
2016-07-01
Full Text Available Air pollution in Peninsular Malaysia is dominated by particulate matter which is demonstrated by having the highest Air Pollution Index (API value compared to the other pollutants at most part of the country. Particulate Matter (PM10 forecasting models development is crucial because it allows the authority and citizens of a community to take necessary actions to limit their exposure to harmful levels of particulates pollution and implement protection measures to significantly improve air quality on designated locations. This study aims in improving the ability of MLR using PCs inputs for PM10 concentrations forecasting. Daily observations for PM10 in Kuala Terengganu, Malaysia from January 2003 till December 2011 were utilized to forecast PM10 concentration levels. MLR and PCR (using PCs input models were developed and the performance was evaluated using RMSE, NAE and IA. Results revealed that PCR performed better than MLR due to the implementation of PCA which reduce intricacy and eliminate data multi-collinearity.
Romero-Ortuno Roman
2010-08-01
Full Text Available Abstract Background A frailty paradigm would be useful in primary care to identify older people at risk, but appropriate metrics at that level are lacking. We created and validated a simple instrument for frailty screening in Europeans aged ≥50. Our study is based on the first wave of the Survey of Health, Ageing and Retirement in Europe (SHARE, http://www.share-project.org, a large population-based survey conducted in 2004-2005 in twelve European countries. Methods Subjects: SHARE Wave 1 respondents (17,304 females and 13,811 males. Measures: five SHARE variables approximating Fried's frailty definition. Analyses (for each gender: 1 estimation of a discreet factor (DFactor model based on the frailty variables using LatentGOLD®. A single DFactor with three ordered levels or latent classes (i.e. non-frail, pre-frail and frail was modelled; 2 the latent classes were characterised against a biopsychosocial range of Wave 1 variables; 3 the prospective mortality risk (unadjusted and age-adjusted for each frailty class was established on those subjects with known mortality status at Wave 2 (2007-2008 (11,384 females and 9,163 males; 4 two web-based calculators were created for easy retrieval of a subject's frailty class given any five measurements. Results Females: the DFactor model included 15,578 cases (standard R2 = 0.61. All five frailty indicators discriminated well (p N = 10,420; 66.9%, pre-frail (N = 4,025; 25.8%, and frail (N = 1,133; 7.3%. Relative to the non-frail class, the age-adjusted Odds Ratio (with 95% Confidence Interval for mortality at Wave 2 was 2.1 (1.4 - 3.0 in the pre-frail and 4.8 (3.1 - 7.4 in the frail. Males: 12,783 cases (standard R2 = 0.61, all frailty indicators had p N = 10,517; 82.3%, pre-frail (N = 1,871; 14.6%, and frail (N = 395; 3.1%; age-adjusted OR (95% CI for mortality: 3.0 (2.3 - 4.0 in the pre-frail, 6.9 (4.7 - 10.2 in the frail. Conclusions The SHARE Frailty Instrument has sufficient construct and
A hydrologic regression sediment-yield model for two ungaged watershed outlet stations in Africa
Moussa, O.M.; Smith, S.E.; Shrestha, R.L.
1991-01-01
A hydrologic regression sediment-yield model was established to determine the relationship between water discharge and suspended sediment discharge at the Blue Nile and the Atbara River outlet stations during the flood season. The model consisted of two main submodels: (1) a suspended sediment discharge model, which was used to determine suspended sediment discharge for each basin outlet; and (2) a sediment rating model, which related water discharge and suspended sediment discharge for each outlet station. Due to the absence of suspended sediment concentration measurements at or near the outlet stations, a minimum norm solution, which is based on the minimization of the unknowns rather than the residuals, was used to determine the suspended sediment discharges at the stations. In addition, the sediment rating submodel was regressed by using an observation equations procedure. Verification analyses on the model were carried out and the mean percentage errors were found to be +12.59 and -12.39, respectively, for the Blue Nile and Atbara. The hydrologic regression model was found to be most sensitive to the relative weight matrix, moderately sensitive to the mean water discharge ratio, and slightly sensitive to the concentration variation along the River Nile's course