Autochthonous tumors driven by Rb1 loss have an ongoing requirement for the RBP2 histone demethylase.

Science.gov (United States)

McBrayer, Samuel K; Olenchock, Benjamin A; DiNatale, Gabriel J; Shi, Diana D; Khanal, Januka; Jennings, Rebecca B; Novak, Jesse S; Oser, Matthew G; Robbins, Alissa K; Modiste, Rebecca; Bonal, Dennis; Moslehi, Javid; Bronson, Roderick T; Neuberg, Donna; Nguyen, Quang-De; Signoretti, Sabina; Losman, Julie-Aurore; Kaelin, William G

2018-04-17

Inactivation of the retinoblastoma gene ( RB1 ) product, pRB, is common in many human cancers. Targeting downstream effectors of pRB that are central to tumorigenesis is a promising strategy to block the growth of tumors harboring loss-of-function RB1 mutations. One such effector is retinoblastoma-binding protein 2 (RBP2, also called JARID1A or KDM5A), which encodes an H3K4 demethylase. Binding of pRB to RBP2 has been linked to the ability of pRB to promote senescence and differentiation. Importantly, genetic ablation of RBP2 is sufficient to phenocopy pRB's ability to induce these cellular changes in cell culture experiments. Moreover, germline Rbp2 deletion significantly impedes tumorigenesis in Rb1 +/- mice. The value of RBP2 as a therapeutic target in cancer, however, hinges on whether loss of RBP2 could block the growth of established tumors as opposed to simply delaying their onset. Here we show that conditional, systemic ablation of RBP2 in tumor-bearing Rb1 +/- mice is sufficient to slow tumor growth and significantly extend survival without causing obvious toxicity to the host. These findings show that established Rb1 -null tumors require RBP2 for growth and further credential RBP2 as a therapeutic target in human cancers driven by RB1 inactivation.

Associations between retinol-binding protein 4 and cardiometabolic risk factors and subclinical atherosclerosis in recently postmenopausal women: cross-sectional analyses from the KEEPS study

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Huang Gary

2012-07-01

Full Text Available Abstract Background The published literature regarding the relationships between retinol-binding protein 4 (RBP4 and cardiometabolic risk factors and subclinical atherosclerosis is conflicting, likely due, in part, to limitations of frequently used RBP4 assays. Prior large studies have not utilized the gold-standard western blot analysis of RBP4 levels. Methods Full-length serum RBP4 levels were measured by western blot in 709 postmenopausal women screened for the Kronos Early Estrogen Prevention Study. Cross-sectional analyses related RBP4 levels to cardiometabolic risk factors, carotid artery intima-media thickness (CIMT, and coronary artery calcification (CAC. Results The mean age of women was 52.9 (± 2.6 years, and the median RBP4 level was 49.0 (interquartile range 36.9-61.5 μg/mL. Higher RBP4 levels were weakly associated with higher triglycerides (age, race, and smoking-adjusted partial Spearman correlation coefficient = 0.10; P = 0.01, but were unrelated to blood pressure, cholesterol, C-reactive protein, glucose, insulin, and CIMT levels (all partial Spearman correlation coefficients ≤0.06, P > 0.05. Results suggested a curvilinear association between RBP4 levels and CAC, with women in the bottom and upper quartiles of RBP4 having higher odds of CAC (odds ratio [95% confidence interval] 2.10 [1.07-4.09], 2.00 [1.02-3.92], 1.64 [0.82-3.27] for the 1st, 3rd, and 4th RBP4 quartiles vs. the 2nd quartile. However, a squared RBP4 term in regression modeling was non-significant (P = 0.10. Conclusions In these healthy, recently postmenopausal women, higher RBP4 levels were weakly associated with elevations in triglycerides and with CAC, but not with other risk factors or CIMT. These data using the gold standard of RBP4 methodology only weakly support the possibility that perturbations in RBP4 homeostasis may be an additional risk factor for subclinical coronary atherosclerosis. Trial registration ClinicalTrials.gov number NCT

Decreased hepatic RBP4 secretion is correlated with reduced hepatic glucose production but is not associated with insulin resistance in patients with liver cirrhosis

NARCIS (Netherlands)

Bahr, Matthias J.; Boeker, Klaus H. W.; Manns, Michael P.; Tietge, Uwe J. F.

Patients with liver cirrhosis have a high incidence of insulin resistance and diabetes. This study was designed to determine circulating levels and hepatic production of retinol-binding protein 4 (RBP4) in relation to parameters of hepatic and systemic metabolism in patients with liver cirrhosis.

CMPK1 and RBP3 are associated with corneal curvature in Asian populations.

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Chen, Peng; Miyake, Masahiro; Fan, Qiao; Liao, Jiemin; Yamashiro, Kenji; Ikram, Mohammad K; Chew, Merywn; Vithana, Eranga N; Khor, Chiea-Chuen; Aung, Tin; Tai, E-Shyong; Wong, Tien-Yin; Teo, Yik-Ying; Yoshimura, Nagahisa; Saw, Seang-Mei; Cheng, Ching-Yu

2014-11-15

Corneal curvature (CC) measures the steepness of the cornea and is an important parameter for clinically diseases such as astigmatism and myopia. Despite the high heritability of CC, only two associated genes have been discovered to date. We performed a three-stage genome-wide association study meta-analysis in 12 660 Asian individuals. Our Stage 1 was done in multiethnic cohorts comprising 7440 individuals, followed by a Stage 2 replication in 2473 Chinese and Stage 3 in 2747 Japanese. The SNP array genotype data were imputed up to the 1000 Genomes Project Phase 1 cosmopolitan panel. The SNP association with the radii of CC was investigated in the linear regression model with the adjustment of age, gender and principal components. In addition to the known genes, MTOR (also known as FRAP1) and PDGFRA, we discovered two novel genes associated with CC: CMPK1 (rs17103186, P = 3.3 × 10(-12)) and RBP3 (rs11204213 [Val884Met], P = 1.1 × 10(-13)). The missense RBP3 SNP, rs11204213, was also associated with axial length (AL) (P = 4.2 × 10(-6)) and had larger effects on both CC and AL compared with other SNPs. The index SNPs at the four indicated loci explained 1.9% of CC variance across the Stages 1 and 2 cohorts, while 33.8% of CC variance was explained by the genome-wide imputation data. We identified two novel genes influencing CC, which are related to either corneal shape or eye size. This study provides additional insights into genetic architecture of corneal shape. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Alterations of retinol-binding protein 4 species in patients with different stages of chronic kidney disease and their relation to lipid parameters

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Henze, Andrea; Frey, Simone K; Raila, Jens

2010-01-01

) was assessed in serum of 45 healthy controls and 52 patients with stage 2-5 of CKD using ELISA and RBP4 immunoprecipitation with subsequent MALDI-TOF-MS analysis. A reduction of glomerular filtration rate was accompanied by a gradual elevation of RBP4 serum levels and relative amounts of RBP4-LL. Correlation...... analysis revealed a strong association of the RBP4-TTR ratio with parameters of lipid metabolism and with diabetes-related factors. In conclusion, RBP4 serum concentration and the appearance of RBP4-LL seem to be influenced by kidney function. Furthermore, the RBP4-TTR ratio may provide diagnostic...

Discriminated benefits of a Mediterranean dietary pattern within a hypocaloric diet program on plasma RBP4 concentrations and other inflammatory markers in obese subjects.

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Hermsdorff, Helen Hermana Miranda; Zulet, M Ángeles; Abete, Itziar; Martínez, J Alfredo

2009-12-01

Personalized nutritional strategies to treat obesity may specifically influence inflammatory markers, in addition to reduce body weight. In the present study, we evaluated the effect of a hypocaloric diet based on a Mediterranean dietary pattern (MDP) on nutritional status as well as on plasma concentrations of retinol binding protein-4 (RBP4) and other proinflammatory markers. Fourty-one subjects (24F/17M; age: 37 ± 7 years; BMI: 32.2 ± 3.9 kg/m²) were assigned to follow a MDP within a caloric-restricted diet over an 8-week period. Anthropometrical, clinical, and biochemical variables were measured at baseline and endpoint after the nutritional program. Dietary intervention resulted in a mean weight loss of -4.4 ± 2.5 kg (P diet score from baseline was a significant and independent predictor factor for the decrease in plasma RBP4 concentration (P hypocaloric diet accompanying a high adherence to a MDP resulted in specific reductions on proinflammatory markers, in addition to a significant improvement in some metabolic syndrome features induced by weight loss, which could be a good combined strategy to treat obesity as well as related metabolic and inflammatory disorders.

LaRbp38: A Leishmania amazonensis protein that binds nuclear and kinetoplast DNAs

International Nuclear Information System (INIS)

Lira, C.B.B.; Siqueira Neto, J.L.; Giardini, M.A.; Winck, F.V.; Ramos, C.H.I.; Cano, M.I.N.

2007-01-01

Leishmania amazonensis causes a wide spectrum of leishmaniasis. There are no vaccines or adequate treatment for leishmaniasis, therefore there is considerable interest in the identification of new targets for anti-leishmania drugs. The central role of telomere-binding proteins in cell maintenance makes these proteins potential targets for new drugs. In this work, we used a combination of purification chromatographies to screen L. amazonensis proteins for molecules capable of binding double-stranded telomeric DNA. This approach resulted in the purification of a 38 kDa polypeptide that was identified by mass spectrometry as Rbp38, a trypanosomatid protein previously shown to stabilize mitochondrial RNA and to associate with nuclear and kinetoplast DNAs. Western blotting and supershift assays confirmed the identity of the protein as LaRbp38. Competition and chromatin immunoprecipitation assays confirmed that LaRbp38 interacted with kinetoplast and nuclear DNAs in vivo and suggested that LaRbp38 may have dual cellular localization and more than one function

RITA, a novel modulator of Notch signalling, acts via nuclear export of RBP-J.

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Wacker, Stephan Armin; Alvarado, Cristobal; von Wichert, Götz; Knippschild, Uwe; Wiedenmann, Jörg; Clauss, Karen; Nienhaus, Gerd Ulrich; Hameister, Horst; Baumann, Bernd; Borggrefe, Tilman; Knöchel, Walter; Oswald, Franz

2011-01-05

The evolutionarily conserved Notch signal transduction pathway regulates fundamental cellular processes during embryonic development and in the adult. Ligand binding induces presenilin-dependent cleavage of the receptor and a subsequent nuclear translocation of the Notch intracellular domain (NICD). In the nucleus, NICD binds to the recombination signal sequence-binding protein J (RBP-J)/CBF-1 transcription factor to induce expression of Notch target genes. Here, we report the identification and functional characterization of RBP-J interacting and tubulin associated (RITA) (C12ORF52) as a novel RBP-J/CBF-1-interacting protein. RITA is a highly conserved 36 kDa protein that, most interestingly, binds to tubulin in the cytoplasm and shuttles rapidly between cytoplasm and nucleus. This shuttling RITA exports RBP-J/CBF-1 from the nucleus. Functionally, we show that RITA can reverse a Notch-induced loss of primary neurogenesis in Xenopus laevis. Furthermore, RITA is able to downregulate Notch-mediated transcription. Thus, we propose that RITA acts as a negative modulator of the Notch signalling pathway, controlling the level of nuclear RBP-J/CBF-1, where its amounts are limiting.

A Polypyrimidine Tract Binding Protein, Pumpkin RBP50, Forms the Basis of a Phloem-Mobile Ribonucleoprotein Complex[W

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Ham, Byung-Kook; Brandom, Jeri L.; Xoconostle-Cázares, Beatriz; Ringgold, Vanessa; Lough, Tony J.; Lucas, William J.

2009-01-01

RNA binding proteins (RBPs) are integral components of ribonucleoprotein (RNP) complexes and play a central role in RNA processing. In plants, some RBPs function in a non-cell-autonomous manner. The angiosperm phloem translocation stream contains a unique population of RBPs, but little is known regarding the nature of the proteins and mRNA species that constitute phloem-mobile RNP complexes. Here, we identified and characterized a 50-kD pumpkin (Cucurbita maxima cv Big Max) phloem RNA binding protein (RBP50) that is evolutionarily related to animal polypyrimidine tract binding proteins. In situ hybridization studies indicated a high level of RBP50 transcripts in companion cells, while immunolocalization experiments detected RBP50 in both companion cells and sieve elements. A comparison of the levels of RBP50 present in vascular bundles and phloem sap indicated that this protein is highly enriched in the phloem sap. Heterografting experiments confirmed that RBP50 is translocated from source to sink tissues. Collectively, these findings established that RBP50 functions as a non-cell-autonomous RBP. Protein overlay, coimmunoprecipitation, and cross-linking experiments identified the phloem proteins and mRNA species that constitute RBP50-based RNP complexes. Gel mobility-shift assays demonstrated that specificity, with respect to the bound mRNA, is established by the polypyrimidine tract binding motifs within such transcripts. We present a model for RBP50-based RNP complexes within the pumpkin phloem translocation stream. PMID:19122103

CmRBP50 protein phosphorylation is essential for assembly of a stable phloem-mobile high-affinity ribonucleoprotein complex.

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Li, Pingfang; Ham, Byung-Kook; Lucas, William J

2011-07-01

RNA-binding proteins (RBPs) form ribonucleoprotein (RNP) complexes that play crucial roles in RNA processing for gene regulation. The angiosperm sieve tube system contains a unique population of transcripts, some of which function as long-distance signaling agents involved in regulating organ development. These phloem-mobile mRNAs are translocated as RNP complexes. One such complex is based on a phloem RBP named Cucurbita maxima RNA-binding protein 50 (CmRBP50), a member of the polypyrimidine track binding protein family. The core of this RNP complex contains six additional phloem proteins. Here, requirements for assembly of this CmRBP50 RNP complex are reported. Phosphorylation sites on CmRBP50 were mapped, and then coimmunoprecipitation and protein overlay studies established that the phosphoserine residues, located at the C terminus of CmRBP50, are critical for RNP complex assembly. In vitro pull-down experiments revealed that three phloem proteins, C. maxima phloem protein 16, C. maxima GTP-binding protein, and C. maxima phosphoinositide-specific phospholipase-like protein, bind directly with CmRBP50. This interaction required CmRBP50 phosphorylation. Gel mobility-shift assays demonstrated that assembly of the CmRBP50-based protein complex results in a system having enhanced binding affinity for phloem-mobile mRNAs carrying polypyrimidine track binding motifs. This property would be essential for effective long-distance translocation of bound mRNA to the target tissues.

Thermometric sensing of peroxide in organic media. Application to monitor the stability of RBP-retinol-HRP complex.

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Ramanathan, K; Jönsson, B R; Danielsson, B

2000-08-01

The stability of horseradish peroxidase (HRP) in aqueous and organic solvents is applied to develop a simple thermometric procedure to detect the binding of retinoic acid-HRP conjugate to retinol binding protein (RBP). Butanone peroxide (BP) in organic phase and hydrogen peroxide in aqueous phase is detected thermometrically on a HRP column, immobilized by cross-linking with glutaraldehyde on controlled pore glass (CPG). Acetone, acetonitrile, methanol, and 2-butanol are used for detection of BP, in the flow injection analysis (FIA) mode. A linear range between 1 and 50 mM BP is obtained in all the organic solvents with a precision of 5-7% (CV%). The magnitude and nature of the thermometric response is significantly different in each organic solvent. The stability of HRP in the organic phase is used to study the stability of a retinoic acid-HRP conjugate bound to immobilized RBP. The response of HRP (to 20 mM BP) in the retinoic acid-HRP conjugate is used as an indicator of the stability of the RBP-retinoic acid-HRP complex, after challenges with various organic/aqueous solvents. Both immobilized HRP and RBP are stable at least for 6 months. The effect of o-phenylene diamine on the thermometric response of HRP is also investigated. A scheme for the design of a thermometric retinol (vitamin A) biosensor is proposed.

The cyst nematode SPRYSEC protein RBP-1 elicits Gpa2- and RanGAP2-dependent plant cell death.

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Melanie Ann Sacco

2009-08-01

Full Text Available Plant NB-LRR proteins confer robust protection against microbes and metazoan parasites by recognizing pathogen-derived avirulence (Avr proteins that are delivered to the host cytoplasm. Microbial Avr proteins usually function as virulence factors in compatible interactions; however, little is known about the types of metazoan proteins recognized by NB-LRR proteins and their relationship with virulence. In this report, we demonstrate that the secreted protein RBP-1 from the potato cyst nematode Globodera pallida elicits defense responses, including cell death typical of a hypersensitive response (HR, through the NB-LRR protein Gpa2. Gp-Rbp-1 variants from G. pallida populations both virulent and avirulent to Gpa2 demonstrated a high degree of polymorphism, with positive selection detected at numerous sites. All Gp-RBP-1 protein variants from an avirulent population were recognized by Gpa2, whereas virulent populations possessed Gp-RBP-1 protein variants both recognized and non-recognized by Gpa2. Recognition of Gp-RBP-1 by Gpa2 correlated to a single amino acid polymorphism at position 187 in the Gp-RBP-1 SPRY domain. Gp-RBP-1 expressed from Potato virus X elicited Gpa2-mediated defenses that required Ran GTPase-activating protein 2 (RanGAP2, a protein known to interact with the Gpa2 N terminus. Tethering RanGAP2 and Gp-RBP-1 variants via fusion proteins resulted in an enhancement of Gpa2-mediated responses. However, activation of Gpa2 was still dependent on the recognition specificity conferred by amino acid 187 and the Gpa2 LRR domain. These results suggest a two-tiered process wherein RanGAP2 mediates an initial interaction with pathogen-delivered Gp-RBP-1 proteins but where the Gpa2 LRR determines which of these interactions will be productive.

Effect of renal replacement therapy on retinol-binding protein 4 isoforms

DEFF Research Database (Denmark)

Frey, Simone K; Henze, Andrea; Nagl, Britta

2009-01-01

Retinol-binding protein 4 (RBP4) levels are elevated in the serum of patients with kidney dysfunction. We recently showed that RBP4 isoforms including apo-RBP4 (RBP4 not bound to retinol) and RBP4 truncated at the C-terminus (RBP4-L, RBP4-LL) are increased in the serum of patients with kidney dis...... diseases but not in serum of patients with various liver diseases. The aim of this study was to investigate the effect of renal replacement therapy on RBP4 isoforms....

RBP-Jκ-dependent Notch signaling enhances retinal pigment epithelial cell proliferation in transgenic mice.

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Schouwey, K; Aydin, I T; Radtke, F; Beermann, F

2011-01-20

The Notch signaling pathway is an ubiquitous cell-cell interaction mechanism, which is essential in controlling processes like cell proliferation, cell fate decision, differentiation or stem cell maintenance. Recent data have shown that Notch signaling is RBP-Jκ-dependent in melanocytes, being required for survival of these pigment cells that are responsible for coloration of the skin and hairs in mammals. In addition, Notch is believed to function as an oncogene in melanoma, whereas it is a tumor suppressor in mouse epidermis. In this study, we addressed the implication of the Notch signaling in the development of another population of pigment cells forming the retinal pigment epithelium (RPE) in mammalian eyes. The constitutive activity of Notch in Tyrp1::NotchIC/° transgenic mice enhanced RPE cell proliferation, and the resulting RPE-derived pigmented tumor severely affected the overall eye structure. This RPE cell proliferation is dependent on the presence of the transcription factor RBP-Jκ, as it is rescued in mice lacking RBP-Jκ in the RPE. In conclusion, Notch signaling in the RPE uses the canonical pathway, which is dependent on the transcription factor RBP-Jκ. In addition, it is of importance for RPE development, and constitutive Notch activity leads to hyperproliferation and benign tumors of these pigment cells.

Blockade of Notch Signaling in Tumor-Bearing Mice May Lead to Tumor Regression, Progression, or Metastasis, Depending on Tumor Cell Types

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Xing-Bin Hu

2009-01-01

Full Text Available It has been reported that blocking Notch signaling in tumor-bearing mice results in abortive angiogenesis and tumor regression. However, given that Notch signaling influences numerous cellular processes in vivo, a comprehensive evaluation of the effect of Notch inactivation on tumor growth would be favorable. In this study, we inoculated four cancer cell lines in mice with the conditional inactivation of recombination signal-binding protein-Jκ (RBP-J, which mediates signaling from all four mammalian Notch receptors. We found that whereas three tumors including hepatocarcinoma, lung cancer, and osteogenic sarcoma grew slower in the RBP-J-deficient mice, at least a melanoma, B16, grew significantly faster in the RBP-J-deficient mice than in the controls, suggesting that the RBP-J-deficient hosts could provide permissive cues for tumor growth. All these tumors showed increased microvessels and up-regulated hypoxia-inducible factor 1α, suggesting that whereas defective angiogenesis resulted in hypoxia, different tumors might grow differentially in the RBP-J-deleted mice. Similarly, increased infiltration of Gr1+/Mac1+ cells were noticed in tumors grown in the RBP-J-inactivated mice. Moreover, we found that when inoculated in the RBP-J knockout hosts, the H22 hepatoma cells had a high frequency of metastasis and lethality, suggesting that at least for H22, deficiency of environmental Notch signaling favored tumor metastasis. Our findings suggested that the general blockade of Notch signaling in tumor-bearing mice could lead to defective angiogenesis in tumors, but depending on tumor cell types, general inhibition of Notch signaling might result in tumor regression, progression, or metastasis.

Gold nanoparticle immunochromatographic assay for quantitative detection of urinary RBP

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XU Kuan

2013-04-01

Full Text Available A rapid quantitative detection of urinary RBP was established by using nano-gold immunochromatography (sandwich method and trisodium citrate reduction method and a rapid immunochromatographic test strip was developed. Theimmunochromatographic test strip can quantitatively detect RBP within 15 minutes. The detection limit was 150ng/mL and detection range was from 150 to 5000 ng/mL. There were no cross-reactions with others kidney disease markers,such as urinary albumin (ALB,transferrin protein (TRF,β2-microglobulin (β2-MG,urinary fiber connecting protein (FN,and lysozyme (LZM. The results indicate that it is a quick and simple method with strong specificity,high sensitivity,and wide detection range. The rapid detection method will have extensive clinical applications in the early diagnosis of proximal tubular damage,kidney disease,diabetic nephropathy,and process monitoring.

Retinol-Binding Protein 4 and Lipids Prospectively Measured During Early to Mid-Pregnancy in Relation to Preeclampsia and Preterm Birth Risk.

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Mendola, Pauline; Ghassabian, Akhgar; Mills, James L; Zhang, Cuilin; Tsai, Michael Y; Liu, Aiyi; Yeung, Edwina H

2017-06-01

Maternal retinol-binding protein 4 (RBP4) and lipids may relate to preeclampsia and preterm birth risk but longitudinal data are lacking. This study examines these biomarkers longitudinally during pregnancy in relation to preeclampsia and preterm birth risk. Maternal serum samples from the Calcium for Preeclampsia Prevention (CPEP) trial were analyzed at baseline: average 15 gestational weeks; mid-pregnancy: average 27 weeks; and at >34 weeks. We measured RBP4, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides and lipoprotein (a) (Lp(a)). Cross-sectional logistic regression analyses estimated the odds ratio (OR) and 95% confidence intervals (CI) for preterm preeclampsia (n = 63), term preeclampsia (n = 104), and preterm delivery (n = 160) associated with RBP4 and lipids at baseline and mid-pregnancy compared with controls (n = 136). Longitudinal trajectories across pregnancy were assessed using mixed linear models with fixed effects. Adjusted models included clinical and demographic factors. RBP4 concentrations at baseline and mid-pregnancy were associated with a 4- to 8-fold increase in preterm preeclampsia risk but were not associated with term preeclampsia. RBP4 measured mid-pregnancy was also associated with preterm birth (OR = 6.67, 95% CI: 1.65, 26.84). Higher triglyceride concentrations in mid-pregnancy were associated with a 2- to 4-fold increased risk for both preeclampsia and preterm birth. Longitudinal models demonstrate that both preterm preeclampsia and preterm birth cases had elevated RBP4 throughout gestation. Elevated RBP4 is detectable early in pregnancy and its strong relation with preterm preeclampsia merits further investigation and confirmation to evaluate its potential use as a predictor, particularly among high-risk women. © Published by Oxford University Press on behalf of American Journal of Hypertension Ltd 2017. This work is written by (a) US Government employees(s) and is in the public domain in the US.

Retinol binding protein 4, obesity, and insulin resistance in adolescents

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Ronaldi Noor

2017-02-01

Full Text Available Background Obesity is a global problem. Even in poor and developing countries, obesity has reached alarming levels. In childhood, obesity may lead to insulin resistance. Retinol binding protein (RBP4, secreted primarily by liver and adipose tissues, was recently proposed as a link between obesity and insulin resistance. The role of RBP4 in pediatric obesity and its relationship with insulin resistance have not been well elucidated. Objective To compare RBP4 levels in obese and lean adolescents and to assess for a relationship between RBP4 levels and insulin resistance. Method This cross-sectional study was conducted in three senior high schools in Padang, West Sumatera, Indonesia. Subjects were adolescents aged 14-18 years, who were obese or normal weight (n=56. We measured subjects’ body mass index (BMI and serum RBP4 concentrations. Insulin resistance was assessed using the homeostasis model assessment of insulin resistance (HOMA-IR index. Results Similar RBP4 levels were found in the obese and normoweight groups (P>0.05. Higher RBP4 levels were found in the insulin resistant compared to the non-insulin resistant group, but the difference was not significant (P > 0.05. Conclusion There is no significant difference in mean RBP4 levels in obese adolescents compared to normoweight adolescents. Nor are mean RBP4 levels significantly different between obese adolescents with and without insulin resistance.

Transcription factor RBP-J-mediated signalling regulates basophil immunoregulatory function in mouse asthma model.

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Qu, Shuo-Yao; He, Ya-Long; Zhang, Jian; Wu, Chang-Gui

2017-09-01

Basophils (BA) play an important role in the promotion of aberrant T helper type 2 (Th2) immune responses in asthma. It is not only the effective cell, but also modulates the initiation of Th2 immune responses. We earlier demonstrated that Notch signalling regulates the biological function of BAin vitro. However, whether this pathway plays the same role in vivo is not clear. The purpose of the present study was to investigate the effect of Notch signalling on BA function in the regulation of allergic airway inflammation in a murine model of asthma. Bone marrow BA were prepared by bone marrow cell culture in the presence of recombinant interleukin-3 (rIL-3; 300 pg/ml) for 7 days, followed by isolation of the CD49b + microbeads. The recombination signal binding protein J (RBP-J -/- ) BA were co-cultured with T cells, and the supernatant and the T-cell subtypes were examined. The results indicated disruption of the capacity of BA for antigen presentation alongside an up-regulation of the immunoregulatory function. This was possibly due to the low expression of OX40L in the RBP-J -/- BA. Basophils were adoptively transferred to ovalbumin-sensitized recipient mice, to establish an asthma model. Lung pathology, cytokine profiles of brobchoalveolar fluid, airway hyperactivity and the absolute number of Th1/Th2 cells in lungs were determined. Overall, our results indicate that the RBP-J-mediated Notch signalling is critical for BA-dependent immunoregulation. Deficiency of RBP-J influences the immunoregulatory functions of BA, which include activation of T cells and their differentiation into T helper cell subtypes. The Notch signalling pathway is a potential therapeutic target for BA-based immunotherapy against asthma. © 2017 John Wiley & Sons Ltd.

Serum retinol binding protein 4 in patients with familial partial lipodystrophy.

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Godoy-Matos, Amélio F; Moreira, Rodrigo O; MacDowell, Renata; Bendet, Izidro; Mory, Patrícia B; Moises, Regina S

2009-07-01

To determine Retinol Binding Protein 4 (RBP4) levels in patients with Familial Partial Lipodystrophy (FPLD). Ten patients with FPLD and a control group (9 patients) were selected to participate in the study. RBP4-log levels were lower in patients with FPLD in comparison to control group (1.52 +/- 0.32 vs 1.84+/-0.25, p=0.029). A statistical trend was observed between Waist-to-Hip Ratio and RBP4-log (r=-0.44, p=0.054). RBP4 levels are decreased in FPLD.

Proteomics Analysis for Finding Serum Markers of Ovarian Cancer

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Yushan Cheng

2014-01-01

Full Text Available A combination of peptide ligand library beads (PLLB and 1D gel liquid chromatography-mass spectrometry/mass spectrometry (1DGel-LC-MS/MS was employed to analyze serum samples from patients with ovarian cancer and from healthy controls. Proteomic analysis identified 1200 serum proteins, among which 57 proteins were upregulated and 10 were downregulated in the sera from cancer patients. Retinol binding protein 4 (RBP4 is highly upregulated in the ovarian cancer serum samples. ELISA was employed to measure plasma concentrations of RBP4 in 80 samples from ovarian cancer patients, healthy individuals, myoma patients, and patients with benign ovarian tumor, respectively. The plasma concentrations of RBP4 ranging from 76.91 to 120.08 ng/mL with the mean value 89.13±1.67 ng/mL in ovarian cancer patients are significantly higher than those in healthy individuals (10.85±2.38 ng/mL. Results were further confirmed with immunohistochemistry, demonstrating that RBP4 expression levels in normal ovarian tissue were lower than those in ovarian cancer tissues. Our results suggested that RBP4 is a potential biomarker for diagnostic of screening ovarian cancer.

Interaction between estrogen receptor and retinol-binding protein-4 polymorphisms as a tool for the selection of prolific pigs

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Iara Denise Vasconcellos Gonçalves

2008-01-01

Full Text Available The aim of the present study was to investigate the association of the estrogen receptor (ER-PvuII and retinol-binding protein 4 (RBP4-MspI gene polymorphisms and their interactions with prolificacy in a commercial synthetic pig line reared in Brazil. A total of 10,374 piglet records from 218 sows and 817 litters were used for litter size analysis. Only females with three or four farrowings were included in the analysis. The mean litter size ranged from 5.0 to 19.5 piglets. DNA was extracted from leukocytes by a standard method, and ER-PvuII and RBP4-MspI polymorphisms were characterized by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP technique. The association between alleles or genotypes and reproductive performance was analyzed using a general linear model including the interaction between the ER-PvuII and RBP4-MspI genotypes. For the ER-PvuII gene, the allele frequencies of allele A and allele B were 0.56 and 0.44, respectively. For the RBP4-MspI gene, the frequencies of alleles A1 and A2 were 0.29 and 0.71, respectively. The total number of piglets born (TNB, born alive (NBA, or number of mummies and stillborn piglets (NMUM and NSB per litter did not differ between the various ER-PvuII and RBP4-MspI genotypes. However, when the ER-PvuII and RBP4-MspI genotypes were considered together in each sow, TNB and NBA were 1.4 (p = 0.0026 and 0.9 (p = 0.019 higher in AA/A1 and AB/A1 animals, respectively, than in AA/A2 and BB/A1 animals. Likewise, TNB and NBA were 0.9 (p = 0.0258 and 0.8 (p = 0.0168 higher in BB/A2 and AB/A2 sows, respectively, than in AA/A2 and BB/A1 animals, but no difference was observed compared to AA/A1 and AB/A1 animals. The results showed larger litter sizes (TNB and NBA for sows carrying the ER-PvuII allele A and the RBP4-MspI genotype A1, and for animals carrying the ER-PvuII allele B and the RBP4-MspI genotype A2. In conclusion, the interaction between genotypes ER-PvuII and RBP4-MspI is

Epstein-Barr virus nuclear protein 3C binds to the N-terminal (NTD) and beta trefoil domains (BTD) of RBP/CSL; Only the NTD interaction is essential for lymphoblastoid cell growth

International Nuclear Information System (INIS)

Calderwood, Michael A.; Lee, Sungwook; Holthaus, Amy M.; Blacklow, Stephen C.; Kieff, Elliott; Johannsen, Eric

2011-01-01

Association of EBV nuclear proteins EBNA2, EBNA3A and EBNA3C with RBP/CSL, is essential for lymphoblastoid cell line (LCL) proliferation. Conserved residues in the EBNA3 homology domain, required for RBP/CSL interaction, lack the WΦP motif that mediates EBNA2 and Notch binding to the RBP/CSL beta-trefoil domain (BTD). We map RBP/CSL interacting residues within EBNA3A(aa128-204) and EBNA3C(aa211-233). The EBNA3A results are consistent with an earlier report (aa125-222), but the EBNA3C domain is unexpectedly small and includes a 'WTP' sequence. This EBNA3C WTP motif confers RBP/CSL binding in vitro, in yeast, and in mammalian cells. Further, an EBNA3C WTP → STP(W227S) mutation impaired BTD binding whereas EBNA3 homology domain mutations disrupted RBP/CSL N-terminal domain (NTD) binding. WTP was not essential for EBNA3C repression of EBNA2 in reporter assays or for maintenance of LCL growth. Our results indicate that EBNA3 proteins interact with multiple RBP/CSL domains, but only NTD interactions are required for LCL growth.

Retinol-Binding Protein 4 and Insulin Resistance in Polycystic Ovary Syndrome

OpenAIRE

Hutchison, Samantha K.; Harrison, Cheryce; Stepto, Nigel; Meyer, Caroline; Teede, Helena J.

2008-01-01

OBJECTIVE?Polycystic ovary syndrome (PCOS) is an insulin-resistant state with insulin resistance being an established therapeutic target; however, measurement of insulin resistance remains challenging. We aimed to 1) determine serum retinol-binding protein 4 (RBP4) levels (purported to reflect insulin resistance) in women with PCOS and control subjects, 2) examine the relationship of RBP4 to conventional markers of insulin resistance, and 3) examine RBP4 changes with interventions modulating ...

Regression analysis by example

CERN Document Server

Chatterjee, Samprit

2012-01-01

Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded

Retinol-binding protein 4 in twins: regulatory mechanisms and impact of circulating and tissue expression levels on insulin secretion and action

DEFF Research Database (Denmark)

Ribel-Madsen, Rasmus; Friedrichsen, Martin; Vaag, Allan

2009-01-01

OBJECTIVE: Retinol-binding protein (RBP) 4 is an adipokine of which plasma levels are elevated in obesity and type 2 diabetes. The aims of the study were to identify determinants of plasma RBP4 and RBP4 mRNA expression in subcutaneous adipose tissue (SAT) and skeletal muscle and to investigate...... expression was not associated with circulatory RBP4. CONCLUSIONS: In conclusion, our data indicate that RBP4 levels in plasma, skeletal muscle, and fat may be linked to insulin resistance and type 2 diabetes in a secondary and noncausal manner....

The Relationship Between Retinol/Retinol Binding Protein 4 ratio, resistin and inflammation in non diabetic obese Indonesian men

Directory of Open Access Journals (Sweden)

Anna Meiliana

2010-02-01

Full Text Available Aim To verify the correlation between Retinol/RBP4 Ratio, and resistin with inflammation (represented by hsCRP in non-diabetic obese Indonesian menMethods This was a cross sectional study using 125 subjects. Measured parameters were retinol, RBP4, resistin, and hsCRP. Correlation between retinol, RBP4, resistin, hsCRP and Retinol/RBP4 Ratio was calculated. Cut off point of hsCRP were classiied as follows: <1 mg/l for low risk of inflammation, 1-3 mg/l for moderate risk, and 3-10 mg/l for high risk (according to CVD risk. The Retinol/RBP4 ratio was dichotomized into high (>0.9 and low ratio (≤0.9. The cross tabulation test was performed to predict the inflammation trends described by Retinol/RBP4 Ratio and resistin.Results Retinol was found strongly correlated with RBP4 and resistin (r=0.53; p<0.01. A positive but not significant correlation was found between resistin and Retinol/RBP4 Ratio with hsCRP. In high ratio group, 17.6% subjects were found with low risk inflammation, 26.4% with moderate risk, and 20.8% with high risk, in low ratio group, 8% subjects were low risk inflammation, 20% moderate risk, and 7.2% high risk. Combination between ratio and resistin showed that in “high ratio and low resistin” group, 12% subjects have low risk of inflammation and 8% have high risk. Meanwhile in “low ratio and high resistin” group, 3.2% subjects were found having low risk and 13.6% high risk of inflammation.Conclusions Combination between Retinol/RBP4 Ratio and resistin showed better description about the inflammation risk in non-diabetic obese subjects compare to the ratio itself. (Med J Indones 2010; 19:57-63Keywords: Retinol, RBP4, resistin, hsCRP, obesity, inflammation

Evidence that kidney function but not type 2 diabetes determines retinol-binding protein 4 serum levels

DEFF Research Database (Denmark)

Henze, Andrea; Frey, Simone K; Raila, Jens

2008-01-01

It has been suggested that retinol-binding protein 4 (RBP4) links adiposity, insulin resistance, and type 2 diabetes. However, circulating RBP4 levels are also affected by kidney function. Therefore, the aim of this study was to test whether RBP4 serum levels are primarily associated with kidney...... function or type 2 diabetes....

Decreased Retinol Binding Protein 4 Concentrations are Associated With Cholesterol Gallstone Disease

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Shen-Nien Wang

2010-06-01

Conclusion: Circulating RBP4 decreases in cholesterol gallstone disease independent of renal function. Further studies are needed to investigate the relationship between liver function and RBP4 levels in these patients.

Independent losses of visual perception genes Gja10 and Rbp3 in echolocating bats (Order: Chiroptera.

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Bin Shen

Full Text Available A trade-off between the sensory modalities of vision and hearing is likely to have occurred in echolocating bats as the sophisticated mechanism of laryngeal echolocation requires considerable neural processing and has reduced the reliance of echolocating bats on vision for perceiving the environment. If such a trade-off exists, it is reasonable to hypothesize that some genes involved in visual function may have undergone relaxed selection or even functional loss in echolocating bats. The Gap junction protein, alpha 10 (Gja10, encoded by Gja10 gene is expressed abundantly in mammal retinal horizontal cells and plays an important role in horizontal cell coupling. The interphotoreceptor retinoid-binding protein (Irbp, encoded by the Rbp3 gene is mainly expressed in interphotoreceptor matrix and is known to be critical for normal functioning of the visual cycle. We sequenced Gja10 and Rbp3 genes in a taxonomically wide range of bats with divergent auditory characteristics (35 and 18 species for Gja10 and Rbp3, respectively. Both genes have became pseudogenes in species from the families Hipposideridae and Rhinolophidae that emit constant frequency echolocation calls with Doppler shift compensation at high-duty-cycles (the most sophisticated form of biosonar known, and in some bat species that emit echolocation calls at low-duty-cycles. Our study thus provides further evidence for the hypothesis that a trade-off occurs at the genetic level between vision and echolocation in bats.

Independent losses of visual perception genes Gja10 and Rbp3 in echolocating bats (Order: Chiroptera).

Science.gov (United States)

Shen, Bin; Fang, Tao; Dai, Mengyao; Jones, Gareth; Zhang, Shuyi

2013-01-01

A trade-off between the sensory modalities of vision and hearing is likely to have occurred in echolocating bats as the sophisticated mechanism of laryngeal echolocation requires considerable neural processing and has reduced the reliance of echolocating bats on vision for perceiving the environment. If such a trade-off exists, it is reasonable to hypothesize that some genes involved in visual function may have undergone relaxed selection or even functional loss in echolocating bats. The Gap junction protein, alpha 10 (Gja10, encoded by Gja10 gene) is expressed abundantly in mammal retinal horizontal cells and plays an important role in horizontal cell coupling. The interphotoreceptor retinoid-binding protein (Irbp, encoded by the Rbp3 gene) is mainly expressed in interphotoreceptor matrix and is known to be critical for normal functioning of the visual cycle. We sequenced Gja10 and Rbp3 genes in a taxonomically wide range of bats with divergent auditory characteristics (35 and 18 species for Gja10 and Rbp3, respectively). Both genes have became pseudogenes in species from the families Hipposideridae and Rhinolophidae that emit constant frequency echolocation calls with Doppler shift compensation at high-duty-cycles (the most sophisticated form of biosonar known), and in some bat species that emit echolocation calls at low-duty-cycles. Our study thus provides further evidence for the hypothesis that a trade-off occurs at the genetic level between vision and echolocation in bats.

Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

Science.gov (United States)

Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

2017-09-01

To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Ghrelin- and GH-induced insulin resistance: no association with retinol-binding protein-4

DEFF Research Database (Denmark)

Vestergaard, Esben Thyssen; Krag, Morten B; Poulsen, Morten M

2013-01-01

Supraphysiological levels of ghrelin and GH induce insulin resistance. Serum levels of retinol-binding protein-4 (RBP4) correlate inversely with insulin sensitivity in patients with type 2 diabetes. We aimed to determine whether ghrelin and GH affect RBP4 levels in human subjects....

Principal component regression analysis with SPSS.

Science.gov (United States)

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Serum proteomic analysis reveals potential serum biomarkers for occupational medicamentosa-like dermatitis caused by trichloroethylene.

Science.gov (United States)

Huang, Peiwu; Ren, Xiaohu; Huang, Zhijun; Yang, Xifei; Hong, Wenxu; Zhang, Yanfang; Zhang, Hang; Liu, Wei; Huang, Haiyan; Huang, Xinfeng; Wu, Desheng; Yang, Linqing; Tang, Haiyan; Zhou, Li; Li, Xuan; Liu, Jianjun

2014-08-17

Trichloroethylene (TCE) is an industrial solvent with widespread occupational exposure and also a major environmental contaminant. Occupational medicamentosa-like dermatitis induced by trichloroethylene (OMLDT) is an autoimmune disease and it has become one major hazard in China. In this study, sera from 3 healthy controls and 3 OMLDT patients at different disease stages were used for a screening study by 2D-DIGE and MALDI-TOF-MS/MS. Eight proteins including transthyretin (TTR), retinol binding protein 4 (RBP4), haptoglobin, clusterin, serum amyloid A protein (SAA), apolipoprotein A-I, apolipoprotein C-III and apolipoprotein C-II were found to be significantly altered among the healthy, acute-stage, healing-stage and healed-stage groups. Specifically, the altered expression of TTR, RBP4 and haptoglobin were further validated by Western blot analysis and ELISA. Our data not only suggested that TTR, RBP4 and haptoglobin could serve as potential serum biomarkers of OMLDT, but also indicated that measurement of TTR, RBP4 and haptoglobin or their combination could help aid in the diagnosis, monitoring the progression and therapy of the disease. Copyright © 2014. Published by Elsevier Ireland Ltd.

INHIBITION OF THE DNA-BINDING ACTIVITY OF DROSOPHILA SUPPRESSOR OF HAIRLESS AND OF ITS HUMAN HOMOLOG, KBF2/RBP-J-KAPPA, BY DIRECT PROTEIN-PROTEIN INTERACTION WITH DROSOPHILA HAIRLESS

NARCIS (Netherlands)

BROU, C; LOGEAT, F; LECOURTOIS, M; VANDEKERCKHOVE, Joël; KOURILSKY, P; SCHWEISGUTH, F; ISRAEL, A

1994-01-01

We have purified the sequence-specific DNA-binding protein KBF2 and cloned the corresponding cDNA, which is derived from the previously described RBP-J kappa gene, the human homolog of the Drosophila Suppressor of Hairless [Su(H)] gene. Deletion studies of the RBP-J kappa and Su(H) proteins allowed

On two flexible methods of 2-dimensional regression analysis

Czech Academy of Sciences Publication Activity Database

Volf, Petr

2012-01-01

Roč. 18, č. 4 (2012), s. 154-164 ISSN 1803-9782 Grant - others:GA ČR(CZ) GAP209/10/2045 Institutional support: RVO:67985556 Keywords : regression analysis * Gordon surface * prediction error * projection pursuit Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2013/SI/volf-on two flexible methods of 2-dimensional regression analysis.pdf

Design, synthesis, and evaluation of nonretinoid retinol binding protein 4 antagonists for the potential treatment of atrophic age-related macular degeneration and Stargardt disease.

Science.gov (United States)

Cioffi, Christopher L; Dobri, Nicoleta; Freeman, Emily E; Conlon, Michael P; Chen, Ping; Stafford, Douglas G; Schwarz, Daniel M C; Golden, Kathy C; Zhu, Lei; Kitchen, Douglas B; Barnes, Keith D; Racz, Boglarka; Qin, Qiong; Michelotti, Enrique; Cywin, Charles L; Martin, William H; Pearson, Paul G; Johnson, Graham; Petrukhin, Konstantin

2014-09-25

Accumulation of lipofuscin in the retina is associated with pathogenesis of atrophic age-related macular degeneration and Stargardt disease. Lipofuscin bisretinoids (exemplified by N-retinylidene-N-retinylethanolamine) seem to mediate lipofuscin toxicity. Synthesis of lipofuscin bisretinoids depends on the influx of retinol from serum to the retina. Compounds antagonizing the retinol-dependent interaction of retinol-binding protein 4 (RBP4) with transthyretin in the serum would reduce serum RBP4 and retinol and inhibit bisretinoid formation. We recently showed that A1120 (3), a potent carboxylic acid based RBP4 antagonist, can significantly reduce lipofuscin bisretinoid formation in the retinas of Abca4(-/-) mice. As part of the NIH Blueprint Neurotherapeutics Network project we undertook the in vitro exploration to identify novel conformationally flexible and constrained RBP4 antagonists with improved potency and metabolic stability. We also demonstrate that upon acute and chronic dosing in rats, 43, a potent cyclopentyl fused pyrrolidine antagonist, reduced circulating plasma RBP4 protein levels by approximately 60%.

Prognostic Importance of Vitamins A, E and Retinol-binding Protein 4 in Renal Cell Carcinoma Patients.

Science.gov (United States)

Sobotka, Roman; Čapoun, Otakar; Kalousová, Marta; Hanuš, Tomáš; Zima, Tomáš; Koštířová, Milada; Soukup, Viktor

2017-07-01

To assess the prognostic importance of serum levels of retinol, retinol-binding protein 4 (RBP4) and vitamin E at the time of diagnosis in patients with renal cell carcinoma (RCC). In this prospective study, in a cohort of 102 renal cell carcinoma patients, relationships between serum levels of the aforementioned markers and recurrence-free survival (RFS), overall survival (OS), as well as cancer-specific survival (CSS), were evaluated. The vitamin A and vitamin E levels were determined by high-performance liquid chromatography (HPLC), while the RBP4 level by enzyme-linked immunosorbent assay (ELISA). The median follow-up period was 39 months. Renal cell carcinoma recurred in 9 patients; 23 patients died with 12 of them from RCC. The preoperative vitamin E level was associated to RFS (p=0.02). We found a significant relationship between OS and the level of RBP4 (p=0.002), retinol (p=0.037) and vitamin E (p=0.007). The CSS period was significantly associated with the level of RBP4 (p=0.0001) and retinol (p=0.0003). Patients with an RBP4 level less than 21.0 mg/l at the time of diagnosis had a 13.5-times higher risk of death due to RCC progression; this risk was up to 7.7-times higher with vitamin A levels under 0.52 mg/l. Low levels of vitamin A, E and RBP4 at the time of RCC diagnosis are associated with a poorer prognosis after surgery. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

CADDIS Volume 4. Data Analysis: PECBO Appendix - R Scripts for Non-Parametric Regressions

Science.gov (United States)

Script for computing nonparametric regression analysis. Overview of using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, statistical scripts.

Polynomial regression analysis and significance test of the regression function

International Nuclear Information System (INIS)

Gao Zhengming; Zhao Juan; He Shengping

2012-01-01

In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)

Applied regression analysis a research tool

CERN Document Server

Pantula, Sastry; Dickey, David

1998-01-01

Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...

A Comparison of the Effects of Aerobic and Intense Exercise on the Type 2 Diabetes Mellitus Risk Marker Adipokines, Adiponectin and Retinol Binding Protein-4

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Amy Phillips

2014-01-01

Full Text Available With a more sedentary population comes growing rates of obesity and increased type 2 diabetes mellitus (T2DM risk. Exercise generally induces positive changes in traditional T2DM risk markers such as lipids, glucose tolerance, and insulin sensitivity; however alterations in concentrations of many circulating cytokines and their respective receptors are also becoming apparent. These cytokines may be early-response health risk factors otherwise overlooked in traditional T2DM risk marker analysis. Plasma levels of two adipocyte-originating cytokines, adiponectin and retinol binding protein 4 (RBP-4, alter following exercise. Adiponectin has anti-inflammatory, anti-atherosclerotic, and anti-insulin resistance roles and its secretion increases with physical activity, whilst elevated RBP-4 leads to increased insulin resistance, and secretion decreases with increasing physical activity; thus these plasma adipokine levels alter favourably following exercise. Although current data are limited, they do suggest that the more intense the exercise, the greater the positive effect on plasma RBP-4 levels, whilst lower intensity aerobic exercise may positively improve adiponectin concentrations. Therefore short-duration, high intensity training may provide a time-efficient alternative to the recommended 150 min moderate aerobic exercise per week in providing positive changes in RBP-4 and other traditional T2DM risk markers and due to increased compliance give greater health benefits over the longer term.

Effect of vitamin A supplementation with BCG vaccine at birth on vitamin A status at 6 wk and 4 mo of age

DEFF Research Database (Denmark)

Fisker, Ane B; Lisse, Ida M; Aaby, Peter

2007-01-01

concentrations indicated vitamin A deficiency in 32% of the children at age 6 wk and in 16% at age 4 mo. VAS was not associated with higher RBP concentrations overall or in either sex. However, the effect of VAS varied with maternal education (P for interaction = 0.004): At age 6 wk, VAS was associated...... with higher (9%; 95% CI: 2, 17%) RBP concentrations in children of noneducated mothers but not in children of educated mothers. Overall, RBP concentrations increased between 6 wk and 4 mo of age. The increase correlated inversely with the number of diphtheria-tetanus-pertussis (DTP) vaccines received...... placebo-controlled trial of VAS, we obtained blood from 614 children at 6 wk of age and from 369 mother-infant pairs at 4 mo of age. We assessed vitamin A status on the basis of serum retinol-binding protein (RBP) and measured serum C-reactive protein to monitor for concurrent infections. RESULTS: RBP...

Regression Analysis by Example. 5th Edition

Science.gov (United States)

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

Regression analysis with categorized regression calibrated exposure: some interesting findings

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Hjartåker Anette

2006-07-01

Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a

Resting-state functional magnetic resonance imaging: the impact of regression analysis.

Science.gov (United States)

Yeh, Chia-Jung; Tseng, Yu-Sheng; Lin, Yi-Ru; Tsai, Shang-Yueh; Huang, Teng-Yi

2015-01-01

To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear. Copyright © 2014 by the American Society of Neuroimaging.

Gaussian process regression analysis for functional data

CERN Document Server

Shi, Jian Qing

2011-01-01

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime

Evolution and variability of Solanum RanGAP2, a cofactor in the incompatible interaction between the resistance protein GPA2 and the Globodera pallida effector Gp-RBP-1.

Science.gov (United States)

Carpentier, Jean; Grenier, Eric; Esquibet, Magalie; Hamel, Louis-Philippe; Moffett, Peter; Manzanares-Dauleux, Maria J; Kerlan, Marie-Claire

2013-04-19

The Ran GTPase Activating Protein 2 (RanGAP2) was first described as a regulator of mitosis and nucleocytoplasmic trafficking. It was then found to interact with the Coiled-Coil domain of the Rx and GPA2 resistance proteins, which confer resistance to Potato Virus X (PVX) and potato cyst nematode Globodera pallida, respectively. RanGAP2 is thought to mediate recognition of the avirulence protein GP-RBP-1 by GPA2. However, the Gpa2-induced hypersensitive response appears to be relatively weak and Gpa2 is limited in terms of spectrum of efficiency as it is effective against only two nematode populations. While functional and evolutionary analyses of Gp-Rbp-1 and Gpa2 identified key residues in both the resistance and avirulence proteins that are involved in recognition determination, whether variation in RanGAP2 also plays a role in pathogen recognition has not been investigated. We amplified a total of 147 RanGAP2 sequences from 55 accessions belonging to 18 different di-and tetraploid Solanum species from the section Petota. Among the newly identified sequences, 133 haplotypes were obtained and 19.1% of the nucleotide sites were found to be polymorphic. The observed intra-specific nucleotide diversity ranges from 0.1 to 1.3%. Analysis of the selection pressures acting on RanGAP2 suggests that this gene evolved mainly under purifying selection. Nonetheless, we identified polymorphic positions in the protein sequence at the intra-specific level, which could modulate the activity of RanGAP2. Two polymorphic sites and a three amino-acid deletion in RanGAP2 were found to affect the timing and intensity of the Gpa2-induced hypersensitive response to avirulent GP-RBP-1 variants even though they did not confer any gain of recognition of virulent GP-RBP-1 variants. Our results highlight how a resistance gene co-factor can manage in terms of evolution both an established role as a cell housekeeping gene and an implication in plant parasite interactions. StRanGAP2 gene

Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

International Nuclear Information System (INIS)

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)

Regression Analysis and the Sociological Imagination

Science.gov (United States)

De Maio, Fernando

2014-01-01

Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.

Polylinear regression analysis in radiochemistry

International Nuclear Information System (INIS)

Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.

1995-01-01

A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis

Retinol Binding Protein-4 Is Associated with TNF-α and Not Insulin Resistance in Subjects with Type 2 Diabetes Mellitus and Coronary Heart Disease

Directory of Open Access Journals (Sweden)

Nasser M. Al-Daghri

2009-01-01

Full Text Available We studied the association between RBP4 and various markers related to insulin resistance and diabetic complications as well as inflammatory markers in Saudi population suffering from type 2 diabetes and coronary heart disease. Patients with type 2 diabetes were divided into 3 groups according to the type of treatment and involvement of coronary artery disease. Serum RBP4, TNF-α, insulin, CRP, resistin, leptin and adiponectin were analysed in all samples. RBP4 levels increased significantly in the group of diabetic subjects treated with oral hypoglycemic agents and diabetic patients with coronary heart disease (30.2 ± 11.8; 33.4 ± 13.6 respectively, while there was no significant change in the other group for diabetic subjects on low-carbohydrate diet (25.1 ± 10.9 compared to control group (22.6 ± 9.5. RPB4 levels were positively correlated with TNF-α in the group of diabetic subjects on oral hypoglycemic agents and diabetic patients with coronary heart disease (r = 0.52, P < 0.05; r = 0.58, P < 0.05 respectively. No correlations were found between RBP4 levels and insulin resistance in all studied groups. Our findings suggest that serum RBP4 levels is associated with pro-inflammatory cytokine (TNF-α and is not associated with insulin resistance among patients with type 2 diabetes and coronary heart disease.

Linear Regression Analysis

CERN Document Server

Seber, George A F

2012-01-01

Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.

Preface to Berk's "Regression Analysis: A Constructive Critique"

OpenAIRE

de Leeuw, Jan

2003-01-01

It is pleasure to write a preface for the book ”Regression Analysis” of my fellow series editor Dick Berk. And it is a pleasure in particular because the book is about regression analysis, the most popular and the most fundamental technique in applied statistics. And because it is critical of the way regression analysis is used in the sciences, in particular in the social and behavioral sciences. Although the book can be read as an introduction to regression analysis, it can also be read as a...

Multicollinearity and Regression Analysis

Science.gov (United States)

Daoud, Jamal I.

2017-12-01

In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

Hierarchical regression analysis in structural Equation Modeling

NARCIS (Netherlands)

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

Multivariate Regression Analysis and Slaughter Livestock,

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AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Sitagliptin down-regulates retinol-binding protein 4 and reduces insulin resistance in gestational diabetes mellitus: a randomized and double-blind trial.

Science.gov (United States)

Sun, Xia; Zhang, Zhendong; Ning, Hui; Sun, Hong; Ji, Xianghong

2017-06-01

Gestational diabetes mellitus (GDM) is a condition that affects increasing number of pregnant women worldwide. Sitagliptin was reported to alleviate symptoms of type 2 diabetes mellitus by reducing serum levels of retinol-binding protein 4 (RBP-4). We investigated the effectiveness of sitagliptin on insulin sensitivity parameters in GDM patients. Pregnant GDM women in the 2nd trimester were recruited for this study. Participants were then assigned randomly to sitagliptin treatment group or placebo treatment group, and administered sitagliptin or placebo daily for 16 weeks. Glucose and insulin profiles, as well as serum RBP-4 level, were measured at both baseline and end of the study. After 16 weeks of treatment, participants in the STL group exhibited significantly improved levels of fasting plasma glucose and serum insulin, homeostasis model of assessment of β cell function (HOMA-β) and insulin resistance (HOMA-IR), compared with those in the placebo group. Serum levels of RBP-4 were also markedly decreased in the sitagliptin treatment group, and more importantly it was positively correlated with improved insulin resistance parameters. Our study supports a potentially promising role of sitagliptin in improving insulin resistance by decreasing RBP-4 in GDM-affected women.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

Science.gov (United States)

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Comparison of cranial sex determination by discriminant analysis and logistic regression.

Science.gov (United States)

Amores-Ampuero, Anabel; Alemán, Inmaculada

2016-04-05

Various methods have been proposed for estimating dimorphism. The objective of this study was to compare sex determination results from cranial measurements using discriminant analysis or logistic regression. The study sample comprised 130 individuals (70 males) of known sex, age, and cause of death from San José cemetery in Granada (Spain). Measurements of 19 neurocranial dimensions and 11 splanchnocranial dimensions were subjected to discriminant analysis and logistic regression, and the percentages of correct classification were compared between the sex functions obtained with each method. The discriminant capacity of the selected variables was evaluated with a cross-validation procedure. The percentage accuracy with discriminant analysis was 78.2% for the neurocranium (82.4% in females and 74.6% in males) and 73.7% for the splanchnocranium (79.6% in females and 68.8% in males). These percentages were higher with logistic regression analysis: 85.7% for the neurocranium (in both sexes) and 94.1% for the splanchnocranium (100% in females and 91.7% in males).

Bayesian logistic regression analysis

NARCIS (Netherlands)

Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.

2012-01-01

In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an

Xue-fu-Zhu-Yu decoction protects rats against retinal ischemia by downregulation of HIF-1α and VEGF via inhibition of RBP2 and PKM2.

Science.gov (United States)

Tan, Shu-Qiu; Geng, Xue; Liu, Jorn-Hon; Pan, Wynn Hwai-Tzong; Wang, Li-Xiang; Liu, Hui-Kang; Hu, Lei; Chao, Hsiao-Ming

2017-07-14

Retinal ischemia-related eye diseases result in visual dysfunction. This study investigates the protective effects and mechanisms of Xue-Fu-Zhu-Yu decoction (XFZYD) with respect to retinal ischemia. Retinal ischemia (I) was induced in Wistar rats by a high intraocular pressure (HIOP) of 120 mmHg for 1 h, which was followed by reperfusion of the ischemic eye; the fellow untreated eye acted as a control. Electroretinogram (ERG), biochemistry and histopathology investigations were performed. Significant ischemic changes occurred after ischemia including decreased ERG b-wave ratios, less numerous retinal ganglion cells (RGCs), reduced inner retinal thickness, fewer choline acetyltransferase (ChAT) labeled amacrine cell bodies, increased glial fibrillary acidic protein (GFAP) immunoreactivity and increased vimentin Müller immunolabeling. These were accompanied by significant increases in the mRNA/protein concentrations of vascular endothelium growth factor, hypoxia-inducible factor-1α, pyruvate kinase M2 and retinoblastoma-binding protein 2. The ischemic changes were concentration-dependently and significantly altered when XFZYD was given for seven consecutive days before or after retina ischemia, compared to vehicle. These alterations included enhanced ERG b-wave amplitudes, more numerous RGCs, enhanced inner retinal thickness, a greater number of ChAT immunolabeled amacrine cell bodies and decreased GFAP/vimentin immunoreactivity. Furthermore, decreased mRNA levels of VEGF, HIF-1α, PKM2, and RBP2 were also found. Reduced protein concentrations of VEGF, HIF-1α, PKM2, and RBP2 were also demonstrated. Furthermore, there was an inhibition of the ischemia-associated increased ratios (target protein/β-actin) in the protein levels of VEGF, HIF-1α, PKM2, and RBP2, which were induced by Shikonin, JIB-04 or Avastin. XFZYD would seem to protect against well-known retinal ischemic changes via a synergistic inhibition of RBP2 and PKM2, as well as down-regulation of HIF-1�

Regression analysis using dependent Polya trees.

Science.gov (United States)

Schörgendorfer, Angela; Branscum, Adam J

2013-11-30

Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

Science.gov (United States)

This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

Effect of vitamin A supplementation with BCG vaccine at birth on vitamin A status at 6 wk and 4 mo of age

DEFF Research Database (Denmark)

Fisker, Ane B; Lisse, Ida M; Aaby, Peter

2007-01-01

with higher (9%; 95% CI: 2, 17%) RBP concentrations in children of noneducated mothers but not in children of educated mothers. Overall, RBP concentrations increased between 6 wk and 4 mo of age. The increase correlated inversely with the number of diphtheria-tetanus-pertussis (DTP) vaccines received...

Visfatin and retinol-binding protein 4 concentrations in lean, glucose-tolerant women with PCOS.

Science.gov (United States)

Yildiz, Bulent O; Bozdag, Gurkan; Otegen, Umit; Harmanci, Ayla; Boynukalin, Kubra; Vural, Zehra; Kirazli, Serafettin; Yarali, Hakan

2010-01-01

Since insulin resistance is accepted to be a common feature of polycystic ovary syndrome (PCOS), the exact molecular mechanism(s) involved in glucose and lipid metabolism have been under investigation in the syndrome. Recently, two novel adipokines, namely visfatin and retinol-binding protein 4 (RBP4), have been suggested to play a role in insulin resistance and diabetes. This study sought to determine whether plasma concentrations of visfatin and RBP4 are altered in PCOS by comparing a total of 27 lean, normal glucose-tolerant PCOS patients with 19 age- and body mass index-matched healthy controls. The mean plasma visfatin concentrations were higher in PCOS patients than those in healthy subjects (37.9+/-18.2 versus 19.8+/-17.5, PPCOS (r=0.52, Plean, glucose-tolerant women with PCOS have increased circulating visfatin and unaltered RBP4 concentrations compared with healthy lean women. In order to clarify overlapping effects and their potential contribution to the pathophysiology of PCOS, further studies are needed. Copyright (c) 2009 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Common pitfalls in statistical analysis: Linear regression analysis

Directory of Open Access Journals (Sweden)

Rakesh Aggarwal

2017-01-01

Full Text Available In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis.

Moderation analysis using a two-level regression model.

Science.gov (United States)

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.

A primer for biomedical scientists on how to execute model II linear regression analysis.

Science.gov (United States)

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.

Two Paradoxes in Linear Regression Analysis

Science.gov (United States)

FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong

2016-01-01

Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

Science.gov (United States)

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

Linear regression and sensitivity analysis in nuclear reactor design

International Nuclear Information System (INIS)

Kumar, Akansha; Tsvetkov, Pavel V.; McClarren, Ryan G.

2015-01-01

Highlights: • Presented a benchmark for the applicability of linear regression to complex systems. • Applied linear regression to a nuclear reactor power system. • Performed neutronics, thermal–hydraulics, and energy conversion using Brayton’s cycle for the design of a GCFBR. • Performed detailed sensitivity analysis to a set of parameters in a nuclear reactor power system. • Modeled and developed reactor design using MCNP, regression using R, and thermal–hydraulics in Java. - Abstract: The paper presents a general strategy applicable for sensitivity analysis (SA), and uncertainity quantification analysis (UA) of parameters related to a nuclear reactor design. This work also validates the use of linear regression (LR) for predictive analysis in a nuclear reactor design. The analysis helps to determine the parameters on which a LR model can be fit for predictive analysis. For those parameters, a regression surface is created based on trial data and predictions are made using this surface. A general strategy of SA to determine and identify the influential parameters those affect the operation of the reactor is mentioned. Identification of design parameters and validation of linearity assumption for the application of LR of reactor design based on a set of tests is performed. The testing methods used to determine the behavior of the parameters can be used as a general strategy for UA, and SA of nuclear reactor models, and thermal hydraulics calculations. A design of a gas cooled fast breeder reactor (GCFBR), with thermal–hydraulics, and energy transfer has been used for the demonstration of this method. MCNP6 is used to simulate the GCFBR design, and perform the necessary criticality calculations. Java is used to build and run input samples, and to extract data from the output files of MCNP6, and R is used to perform regression analysis and other multivariate variance, and analysis of the collinearity of data

Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2007-01-01

This note primarily describes the mathematics of least squares regression analysis as it is often used in geodesy including land surveying and satellite positioning applications. In these fields regression is often termed adjustment. The note also contains a couple of typical land surveying...... and satellite positioning application examples. In these application areas we are typically interested in the parameters in the model typically 2- or 3-D positions and not in predictive modelling which is often the main concern in other regression analysis applications. Adjustment is often used to obtain...... the clock error) and to obtain estimates of the uncertainty with which the position is determined. Regression analysis is used in many other fields of application both in the natural, the technical and the social sciences. Examples may be curve fitting, calibration, establishing relationships between...

Design and analysis of experiments classical and regression approaches with SAS

CERN Document Server

Onyiah, Leonard C

2008-01-01

Introductory Statistical Inference and Regression Analysis Elementary Statistical Inference Regression Analysis Experiments, the Completely Randomized Design (CRD)-Classical and Regression Approaches Experiments Experiments to Compare Treatments Some Basic Ideas Requirements of a Good Experiment One-Way Experimental Layout or the CRD: Design and Analysis Analysis of Experimental Data (Fixed Effects Model) Expected Values for the Sums of Squares The Analysis of Variance (ANOVA) Table Follow-Up Analysis to Check fo

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin

2015-04-03

© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis

DEFF Research Database (Denmark)

Czekaj, Tomasz Gerard

and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...... within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis. The results provided in this PhD thesis show that nonparametric......This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...

Simulation Experiments in Practice: Statistical Design and Regression Analysis

OpenAIRE

Kleijnen, J.P.C.

2007-01-01

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. The goal of this article is to change these traditional, naïve methods of design and analysis, because statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic DOE and regression analysis assume a single simulation response that is normally and independen...

Retinol-Binding Protein 4 in Young Men With Low Versus Normal Birth Weight

DEFF Research Database (Denmark)

Ribel-Madsen, Rasmus; Brøns, Charlotte; Friedrichsen, Martin

2011-01-01

= 15.4 µg/ml (9.5; 21.3), P index (D(i)) (ß = -2.4% (-4.5%; -0.2%), P = 0.04) and increased basal hepatic glucose production rate (HGP) (ß = 0.02 mg kg(-1) min(-1) (0.002; 0.04), P = 0.03), but not associated...... with peripheral glucose disposal rate or hepatic insulin resistance index. RBP4 levels were not influenced by overfeeding or related to peripheral and hepatic insulin resistance provoked by the dietary intervention. In conclusion, plasma RBP4 in young men associates with components of the metabolic syndrome...... = 20) or normal (n = 26) birth weight underwent a 5-day high-fat high-calorie (HFHC) dietary intervention. In vivo glucose metabolism was assessed by euglycemic-hyperinsulinemic clamp, glucose tracer and intravenous glucose tolerance test techniques. Body composition was measured by a dual-energy x...

General Nature of Multicollinearity in Multiple Regression Analysis.

Science.gov (United States)

Liu, Richard

1981-01-01

Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

On logistic regression analysis of dichotomized responses.

Science.gov (United States)

Lu, Kaifeng

2017-01-01

We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect. On the other hand, the true adjusted odds ratio implied by the normal distribution of the underlying continuous variable is a function of the baseline value and hence is unlikely to be able to be adequately represented by a single value of adjusted odds ratio from the logistic regression model. In contrast, the risk difference function derived from the logistic regression model provides a reasonable approximation to the true risk difference function implied by the normal distribution of the underlying continuous variable over the range of the baseline distribution. We show that different metrics of treatment effect have similar statistical power when evaluated at the baseline mean. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Retinol analysis in dried blood spots by HPLC.

Science.gov (United States)

Craft, N E; Haitema, T; Brindle, L K; Yamini, S; Humphrey, J H; West, K P

2000-04-01

There are many advantages to measuring vitamin A in dried blood spots (DBS) from a finger prick as compared to plasma collected by venipuncture. The advantages include easier collection, transport and storage; accessibility to younger and more remote populations; and decreased risk of disease transmission. We describe a method for the extraction of retinol from DBS for analysis by HPLC and initial comparison to plasma retinol. The effects of various buffers, detergents, antioxidants and chelators were evaluated to establish the most effective approach to elute the retinol: retinol binding protein (holo-RBP) complex from the blood collection cards. The process involves ultrasonic agitation to elute holo-RBP into a phosphate buffer containing an antioxidant and metal chelator. The holo-RBP complex was denatured by the addition of ethanol containing additional antioxidants permitting the extraction of free retinol into hexane. Following solvent evaporation, the extract was dissolved in methanol for HPLC analysis. The initial measured retinol levels in freshly collected DBS declined for 6-10 d whether stored at 25, 4 or -20 degrees C, but remained consistent thereafter (homeostatic). By incorporating a "recovery/volume adjustment" factor, measured retinol values in homeostatic DBS were adjusted to the equivalent of plasma retinol. For 17 normal adults, the correlation coefficient was 0.90 between plasma retinol and adjusted DBS retinol in samples that had been stored at -70 degrees C for < 9 mo. The use of this new sample matrix for vitamin A assessment will allow access to previously unavailable populations.

Relation of Absolute or Relative Adiposity to Insulin Resistance, Retinol Binding Protein-4, Leptin, and Adiponectin in Type 2 Diabetes

Directory of Open Access Journals (Sweden)

You Lim Kim

2012-12-01

Full Text Available BackgroundCentral fat mass (CFM correlates with insulin resistance and increases the risk of type 2 diabetes and cardiovascular complications; however, peripheral fat mass (PFM is associated with insulin sensitivity. The aim of this study was to investigate the relation of absolute and relative regional adiposity to insulin resistance index and adipokines in type 2 diabetes.MethodsTotal of 83 overweighted-Korean women with type 2 diabetes were enrolled, and rate constants for plasma glucose disappearance (KITT and serum adipokines, such as retinol binding protein-4 (RBP4, leptin, and adiponectin, were measured. Using dual X-ray absorptiometry, trunk fat mass (in kilograms was defined as CFM, sum of fat mass on the lower extremities (in kilograms as PFM, and sum of CFM and PFM as total fat mass (TFM. PFM/TFM ratio, CFM/TFM ratio, and PFM/CFM ratio were defined as relative adiposity.ResultsMedian age was 55.9 years, mean body mass index 27.2 kg/m2, and mean HbA1c level 7.12±0.84%. KITT was positively associated with PMF/TFM ratio, PMF/CFM ratio, and negatively with CFM/TFM ratio, but was not associated with TFM, PFM, or CFM. RBP4 levels also had a significant relationship with PMF/TFM ratio and PMF/CFM ratio. Adiponectin, leptin, and apolipoprotein A levels were related to absolute adiposity, while only adiponectin to relative adiposity. In correlation analysis, KITT in type 2 diabetes was positively related with HbA1c, fasting glucose, RBP4, and free fatty acid.ConclusionThese results suggest that increased relative amount of peripheral fat mass may aggravate insulin resistance in type 2 diabetes.

Development of a User Interface for a Regression Analysis Software Tool

Science.gov (United States)

Ulbrich, Norbert Manfred; Volden, Thomas R.

2010-01-01

An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.

Method for nonlinear exponential regression analysis

Science.gov (United States)

Junkin, B. G.

1972-01-01

Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.

Regression of uveal malignant melanomas following cobalt-60 plaque. Correlates between acoustic spectrum analysis and tumor regression

International Nuclear Information System (INIS)

Coleman, D.J.; Lizzi, F.L.; Silverman, R.H.; Ellsworth, R.M.; Haik, B.G.; Abramson, D.H.; Smith, M.E.; Rondeau, M.J.

1985-01-01

Parameters derived from computer analysis of digital radio-frequency (rf) ultrasound scan data of untreated uveal malignant melanomas were examined for correlations with tumor regression following cobalt-60 plaque. Parameters included tumor height, normalized power spectrum and acoustic tissue type (ATT). Acoustic tissue type was based upon discriminant analysis of tumor power spectra, with spectra of tumors of known pathology serving as a model. Results showed ATT to be correlated with tumor regression during the first 18 months following treatment. Tumors with ATT associated with spindle cell malignant melanoma showed over twice the percentage reduction in height as those with ATT associated with mixed/epithelioid melanomas. Pre-treatment height was only weakly correlated with regression. Additionally, significant spectral changes were observed following treatment. Ultrasonic spectrum analysis thus provides a noninvasive tool for classification, prediction and monitoring of tumor response to cobalt-60 plaque

Detecting overdispersion in count data: A zero-inflated Poisson regression analysis

Science.gov (United States)

Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Nor, Maria Elena; Mohamed, Maryati; Ismail, Norradihah

2017-09-01

This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, this study comprising the used of zero-inflated Poisson (ZIP) regression analysis to gain acute precision on analysing the count data of butterfly communities in Jasin, Melaka. On the other hands, Poisson regression should be abandoned in the favour of count data models, which are capable of taking into account the extra zeros explicitly. By far, one of the most popular models include ZIP regression model. The data of butterfly communities which had been called as the number of subjects in this study had been taken in Jasin, Melaka and consisted of 131 number of subjects visits Jasin, Melaka. Since the researchers are considering the number of subjects, this data set consists of five families of butterfly and represent the five variables involve in the analysis which are the types of subjects. Besides, the analysis of ZIP used the SAS procedure of overdispersion in analysing zeros value and the main purpose of continuing the previous study is to compare which models would be better than when exists zero values for the observation of the count data. The analysis used AIC, BIC and Voung test of 5% level significance in order to achieve the objectives. The finding indicates that there is a presence of over-dispersion in analysing zero value. The ZIP regression model is better than Poisson regression model when zero values exist.

An Analysis of Bank Service Satisfaction Based on Quantile Regression and Grey Relational Analysis

Directory of Open Access Journals (Sweden)

Wen-Tsao Pan

2016-01-01

Full Text Available Bank service satisfaction is vital to the success of a bank. In this paper, we propose to use the grey relational analysis to gauge the levels of service satisfaction of the banks. With the grey relational analysis, we compared the effects of different variables on service satisfaction. We gave ranks to the banks according to their levels of service satisfaction. We further used the quantile regression model to find the variables that affected the satisfaction of a customer at a specific quantile of satisfaction level. The result of the quantile regression analysis provided a bank manager with information to formulate policies to further promote satisfaction of the customers at different quantiles of satisfaction level. We also compared the prediction accuracies of the regression models at different quantiles. The experiment result showed that, among the seven quantile regression models, the median regression model has the best performance in terms of RMSE, RTIC, and CE performance measures.

Research and analyze of physical health using multiple regression analysis

Directory of Open Access Journals (Sweden)

T. S. Kyi

2014-01-01

Full Text Available This paper represents the research which is trying to create a mathematical model of the "healthy people" using the method of regression analysis. The factors are the physical parameters of the person (such as heart rate, lung capacity, blood pressure, breath holding, weight height coefficient, flexibility of the spine, muscles of the shoulder belt, abdominal muscles, squatting, etc.., and the response variable is an indicator of physical working capacity. After performing multiple regression analysis, obtained useful multiple regression models that can predict the physical performance of boys the aged of fourteen to seventeen years. This paper represents the development of regression model for the sixteen year old boys and analyzed results.

The RNA-binding protein xCIRP2 is involved in apoptotic tail regression during metamorphosis in Xenopus laevis tadpoles.

Science.gov (United States)

Eto, Ko; Iwama, Tomoyuki; Tajima, Tatsuya; Abe, Shin-ichi

2012-10-01

Frog metamorphosis induced by thyroid hormone (TH) involves not only cell proliferation and differentiation in reconstituted organs such as limbs, but also apoptotic cell death in degenerated organs such as tails. However, the molecular mechanisms directing the TH-dependent cell fate determination remain unclear. We have previously identified from newts an RNA-binding protein (nRBP) acting as the regulator governing survival and death in germ cells during spermatogenesis. To investigate the molecular events leading the tail resorption during metamorphosis, we analyzed the expression, the functional role in apoptosis, and the regulation of xCIRP2, a frog homolog of nRBP, in tails of Xenopus laevis tadpoles. At the prometamorphic stage, xCIRP2 protein is expressed in fibroblast, epidermal, nerve, and muscular cells and localized in their cytoplasm. When spontaneous metamorphosis progressed, the level of xCIRP2 mRNA remained unchanged but the amount of the protein decreased. In organ cultures of tails at the prometamorphic stage, xCIRP2 protein decreased before their lengths shortened during TH-dependent metamorphosis. The inhibition of calpain or proteasome attenuated the TH-induced decrease of xCIRP2 protein in tails, impairing their regression. These results suggest that xCIRP2 protein is downregulated through calpain- and proteasome-mediated proteolysis in response to TH at the onset of metamorphosis, inducing apoptosis in tails and thereby degenerating them. Copyright © 2012 Elsevier Inc. All rights reserved.

An improved multiple linear regression and data analysis computer program package

Science.gov (United States)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

Functional data analysis of generalized regression quantiles

KAUST Repository

Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl

2013-01-01

Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

Functional data analysis of generalized regression quantiles

KAUST Repository

Guo, Mengmeng

2013-11-05

Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

Analysis of Relationship Between Personality and Favorite Places with Poisson Regression Analysis

Directory of Open Access Journals (Sweden)

Yoon Song Ha

2018-01-01

Full Text Available A relationship between human personality and preferred locations have been a long conjecture for human mobility research. In this paper, we analyzed the relationship between personality and visiting place with Poisson Regression. Poisson Regression can analyze correlation between countable dependent variable and independent variable. For this analysis, 33 volunteers provided their personality data and 49 location categories data are used. Raw location data is preprocessed to be normalized into rates of visit and outlier data is prunned. For the regression analysis, independent variables are personality data and dependent variables are preprocessed location data. Several meaningful results are found. For example, persons with high tendency of frequent visiting to university laboratory has personality with high conscientiousness and low openness. As well, other meaningful location categories are presented in this paper.

application of multilinear regression analysis in modeling of soil

African Journals Online (AJOL)

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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” ...

Clinical evaluation of a novel population-based regression analysis for detecting glaucomatous visual field progression.

Science.gov (United States)

Kovalska, M P; Bürki, E; Schoetzau, A; Orguel, S F; Orguel, S; Grieshaber, M C

2011-04-01

The distinction of real progression from test variability in visual field (VF) series may be based on clinical judgment, on trend analysis based on follow-up of test parameters over time, or on identification of a significant change related to the mean of baseline exams (event analysis). The aim of this study was to compare a new population-based method (Octopus field analysis, OFA) with classic regression analyses and clinical judgment for detecting glaucomatous VF changes. 240 VF series of 240 patients with at least 9 consecutive examinations available were included into this study. They were independently classified by two experienced investigators. The results of such a classification served as a reference for comparison for the following statistical tests: (a) t-test global, (b) r-test global, (c) regression analysis of 10 VF clusters and (d) point-wise linear regression analysis. 32.5 % of the VF series were classified as progressive by the investigators. The sensitivity and specificity were 89.7 % and 92.0 % for r-test, and 73.1 % and 93.8 % for the t-test, respectively. In the point-wise linear regression analysis, the specificity was comparable (89.5 % versus 92 %), but the sensitivity was clearly lower than in the r-test (22.4 % versus 89.7 %) at a significance level of p = 0.01. A regression analysis for the 10 VF clusters showed a markedly higher sensitivity for the r-test (37.7 %) than the t-test (14.1 %) at a similar specificity (88.3 % versus 93.8 %) for a significant trend (p = 0.005). In regard to the cluster distribution, the paracentral clusters and the superior nasal hemifield progressed most frequently. The population-based regression analysis seems to be superior to the trend analysis in detecting VF progression in glaucoma, and may eliminate the drawbacks of the event analysis. Further, it may assist the clinician in the evaluation of VF series and may allow better visualization of the correlation between function and structure owing to VF

Multiple regression analysis of Jominy hardenability data for boron treated steels

International Nuclear Information System (INIS)

Komenda, J.; Sandstroem, R.; Tukiainen, M.

1997-01-01

The relations between chemical composition and their hardenability of boron treated steels have been investigated using a multiple regression analysis method. A linear model of regression was chosen. The free boron content that is effective for the hardenability was calculated using a model proposed by Jansson. The regression analysis for 1261 steel heats provided equations that were statistically significant at the 95% level. All heats met the specification according to the nordic countries producers classification. The variation in chemical composition explained typically 80 to 90% of the variation in the hardenability. In the regression analysis elements which did not significantly contribute to the calculated hardness according to the F test were eliminated. Carbon, silicon, manganese, phosphorus and chromium were of importance at all Jominy distances, nickel, vanadium, boron and nitrogen at distances above 6 mm. After the regression analysis it was demonstrated that very few outliers were present in the data set, i.e. data points outside four times the standard deviation. The model has successfully been used in industrial practice replacing some of the necessary Jominy tests. (orig.)

Analisis Perbandingan Teknik Support Vector Regression (SVR) Dan Decision Tree C4.5 Dalam Data Mining

OpenAIRE

Astuti, Yuniar Andi

2011-01-01

This study examines techniques Support Vector Regression and Decision Tree C4.5 has been used in studies in various fields, in order to know the advantages and disadvantages of both techniques that appear in Data Mining. From the ten studies that use both techniques, the results of the analysis showed that the accuracy of the SVR technique for 59,64% and C4.5 for 76,97% So in this study obtained a statement that C4.5 is better than SVR 097038020

High dietary fat-induced obesity in Wistar rats and type 2 diabetes in nonobese Goto-Kakizaki rats differentially affect retinol binding protein 4 expression and vitamin A metabolism.

Science.gov (United States)

Shirai, Tomomi; Shichi, Yuta; Sato, Miyuki; Tanioka, Yuri; Furusho, Tadasu; Ota, Toru; Tadokoro, Tadahiro; Suzuki, Tsukasa; Kobayashi, Ken-Ichi; Yamamoto, Yuji

2016-03-01

Obesity is a major risk factor for type 2 diabetes, which is caused mainly by insulin resistance. Retinol binding protein 4 (RBP4) is the only specific transport protein for retinol in the serum. RBP4 level is increased in the diabetic state and high-fat condition, indicating that retinol metabolism may be affected under these conditions. However, the precise effect of diabetes and high fat-induced obesity on retinol metabolism is unknown. In this study, we examined differences in retinol metabolite levels in rat models of diet-induced obesity and type 2 diabetes (Goto-Kakizaki [GK] rat). Four-week-old male Wistar and GK rats were given either a control diet (AIN-93G) or a high-fat diet (HFD, 40% fat kJ). After 15 weeks of feeding, the RBP4 levels increased by 2-fold in the serum of GK rats but not HFD-fed rats. The hepatic retinol concentration of HFD-fed rats was approximately 50% that of the controls (P type 2 diabetes mellitus. Copyright © 2016 Elsevier Inc. All rights reserved.

Vitamin A metabolism is changed in donors after living-kidney transplantation: an observational study

Directory of Open Access Journals (Sweden)

Henze Andrea

2011-12-01

Full Text Available Abstract Background The kidneys are essential for the metabolism of vitamin A (retinol and its transport proteins retinol-binding protein 4 (RBP4 and transthyretin. Little is known about changes in serum concentration after living donor kidney transplantation (LDKT as a consequence of unilateral nephrectomy; although an association of these parameters with the risk of cardiovascular diseases and insulin resistance has been suggested. Therefore we analyzed the concentration of retinol, RBP4, apoRBP4 and transthyretin in serum of 20 living-kidney donors and respective recipients at baseline as well as 6 weeks and 6 months after LDKT. Results As a consequence of LDKT, the kidney function of recipients was improved while the kidney function of donors was moderately reduced within 6 weeks after LDKT. With regard to vitamin A metabolism, the recipients revealed higher levels of retinol, RBP4, transthyretin and apoRBP4 before LDKT in comparison to donors. After LDKT, the levels of all four parameters decreased in serum of the recipients, while retinol, RBP4 as well as apoRBP4 serum levels of donors increased and remained increased during the follow-up period of 6 months. Conclusion LDKT is generally regarded as beneficial for allograft recipients and not particularly detrimental for the donors. However, it could be demonstrated in this study that a moderate reduction of kidney function by unilateral nephrectomy, resulted in an imbalance of components of vitamin A metabolism with a significant increase of retinol and RBP4 and apoRBP4 concentration in serum of donors.

Understanding logistic regression analysis

OpenAIRE

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...

Exploring factors associated with traumatic dental injuries in preschool children: a Poisson regression analysis.

Science.gov (United States)

Feldens, Carlos Alberto; Kramer, Paulo Floriani; Ferreira, Simone Helena; Spiguel, Mônica Hermann; Marquezan, Marcela

2010-04-01

This cross-sectional study aimed to investigate the factors associated with dental trauma in preschool children using Poisson regression analysis with robust variance. The study population comprised 888 children aged 3- to 5-year-old attending public nurseries in Canoas, southern Brazil. Questionnaires assessing information related to the independent variables (age, gender, race, mother's educational level and family income) were completed by the parents. Clinical examinations were carried out by five trained examiners in order to assess traumatic dental injuries (TDI) according to Andreasen's classification. One of the five examiners was calibrated to assess orthodontic characteristics (open bite and overjet). Multivariable Poisson regression analysis with robust variance was used to determine the factors associated with dental trauma as well as the strengths of association. Traditional logistic regression was also performed in order to compare the estimates obtained by both methods of statistical analysis. 36.4% (323/888) of the children suffered dental trauma and there was no difference in prevalence rates from 3 to 5 years of age. Poisson regression analysis showed that the probability of the outcome was almost 30% higher for children whose mothers had more than 8 years of education (Prevalence Ratio = 1.28; 95% CI = 1.03-1.60) and 63% higher for children with an overjet greater than 2 mm (Prevalence Ratio = 1.63; 95% CI = 1.31-2.03). Odds ratios clearly overestimated the size of the effect when compared with prevalence ratios. These findings indicate the need for preventive orientation regarding TDI, in order to educate parents and caregivers about supervising infants, particularly those with increased overjet and whose mothers have a higher level of education. Poisson regression with robust variance represents a better alternative than logistic regression to estimate the risk of dental trauma in preschool children.

Background stratified Poisson regression analysis of cohort data.

Science.gov (United States)

Richardson, David B; Langholz, Bryan

2012-03-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

Ghrelin- and GH-induced insulin resistance

DEFF Research Database (Denmark)

Vestergaard, Esben Thyssen; Krag, Morten B; Poulsen, Morten M

2013-01-01

Supraphysiological levels of ghrelin and GH induce insulin resistance. Serum levels of retinol-binding protein-4 (RBP4) correlate inversely with insulin sensitivity in patients with type 2 diabetes. We aimed to determine whether ghrelin and GH affect RBP4 levels in human subjects.......Supraphysiological levels of ghrelin and GH induce insulin resistance. Serum levels of retinol-binding protein-4 (RBP4) correlate inversely with insulin sensitivity in patients with type 2 diabetes. We aimed to determine whether ghrelin and GH affect RBP4 levels in human subjects....

Reference-based pricing: an evidence-based solution for lab services shopping.

Science.gov (United States)

Melton, L Doug; Bradley, Kent; Fu, Patricia Lin; Armata, Raegan; Parr, James B

2014-01-01

To determine the effect of reference-based pricing (RBP) on the percentage of lab services utilized by members that were at or below the reference price. Retrospective, quasi-experimental, matched, case-control pilot evaluation of an RBP benefit for lab services. The study group included employees of a multinational grocery chain covered by a national health insurance carrier and subject to RBP for lab services; it had access to an online lab shopping tool and was informed about the RBP benefit through employer communications. The reference group was covered by the same insurance carrier but not subject to RBP. The primary end point was lab compliance, defined as the percentage of lab claims with total charges at or below the reference price. Difference-in-difference regression estimation evaluated changes in lab compliance between the 2 groups. Higher compliance per lab claim was evident for the study group compared with the reference group (69% vs 57%; Ponline shopping tool was used by 7% of the matched-adjusted study group prior to obtaining lab services. Lab compliance was 76% for study group members using the online tool compared with 68% among nonusers who were subject to RBP (P<.01). RBP can promote cost-conscious selection of lab services. Access to facilities that offer services below the reference price and education about RBP improve compliance. Evaluation of the effect of RBP on higher-cost medical services, including radiology, outpatient specialty, and elective inpatient procedures, is needed.

Poisson Regression Analysis of Illness and Injury Surveillance Data

Energy Technology Data Exchange (ETDEWEB)

Frome E.L., Watkins J.P., Ellis E.D.

2012-12-12

The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences due to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra

A Quality Assessment Tool for Non-Specialist Users of Regression Analysis

Science.gov (United States)

Argyrous, George

2015-01-01

This paper illustrates the use of a quality assessment tool for regression analysis. It is designed for non-specialist "consumers" of evidence, such as policy makers. The tool provides a series of questions such consumers of evidence can ask to interrogate regression analysis, and is illustrated with reference to a recent study published…

Background stratified Poisson regression analysis of cohort data

International Nuclear Information System (INIS)

Richardson, David B.; Langholz, Bryan

2012-01-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. (orig.)

Understanding logistic regression analysis.

Science.gov (United States)

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

Regression and local control rates after radiotherapy for jugulotympanic paragangliomas: Systematic review and meta-analysis

International Nuclear Information System (INIS)

Hulsteijn, Leonie T. van; Corssmit, Eleonora P.M.; Coremans, Ida E.M.; Smit, Johannes W.A.; Jansen, Jeroen C.; Dekkers, Olaf M.

2013-01-01

The primary treatment goal of radiotherapy for paragangliomas of the head and neck region (HNPGLs) is local control of the tumor, i.e. stabilization of tumor volume. Interestingly, regression of tumor volume has also been reported. Up to the present, no meta-analysis has been performed giving an overview of regression rates after radiotherapy in HNPGLs. The main objective was to perform a systematic review and meta-analysis to assess regression of tumor volume in HNPGL-patients after radiotherapy. A second outcome was local tumor control. Design of the study is systematic review and meta-analysis. PubMed, EMBASE, Web of Science, COCHRANE and Academic Search Premier and references of key articles were searched in March 2012 to identify potentially relevant studies. Considering the indolent course of HNPGLs, only studies with ⩾12 months follow-up were eligible. Main outcomes were the pooled proportions of regression and local control after radiotherapy as initial, combined (i.e. directly post-operatively or post-embolization) or salvage treatment (i.e. after initial treatment has failed) for HNPGLs. A meta-analysis was performed with an exact likelihood approach using a logistic regression with a random effect at the study level. Pooled proportions with 95% confidence intervals (CI) were reported. Fifteen studies were included, concerning a total of 283 jugulotympanic HNPGLs in 276 patients. Pooled regression proportions for initial, combined and salvage treatment were respectively 21%, 33% and 52% in radiosurgery studies and 4%, 0% and 64% in external beam radiotherapy studies. Pooled local control proportions for radiotherapy as initial, combined and salvage treatment ranged from 79% to 100%. Radiotherapy for jugulotympanic paragangliomas results in excellent local tumor control and therefore is a valuable treatment for these types of tumors. The effects of radiotherapy on regression of tumor volume remain ambiguous, although the data suggest that regression can

Retro-regression--another important multivariate regression improvement.

Science.gov (United States)

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Linear regression analysis: part 14 of a series on evaluation of scientific publications.

Science.gov (United States)

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

Management of Industrial Performance Indicators: Regression Analysis and Simulation

Directory of Open Access Journals (Sweden)

Walter Roberto Hernandez Vergara

2017-11-01

Full Text Available Stochastic methods can be used in problem solving and explanation of natural phenomena through the application of statistical procedures. The article aims to associate the regression analysis and systems simulation, in order to facilitate the practical understanding of data analysis. The algorithms were developed in Microsoft Office Excel software, using statistical techniques such as regression theory, ANOVA and Cholesky Factorization, which made it possible to create models of single and multiple systems with up to five independent variables. For the analysis of these models, the Monte Carlo simulation and analysis of industrial performance indicators were used, resulting in numerical indices that aim to improve the goals’ management for compliance indicators, by identifying systems’ instability, correlation and anomalies. The analytical models presented in the survey indicated satisfactory results with numerous possibilities for industrial and academic applications, as well as the potential for deployment in new analytical techniques.

Least-Squares Linear Regression and Schrodinger's Cat: Perspectives on the Analysis of Regression Residuals.

Science.gov (United States)

Hecht, Jeffrey B.

The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…

Predicting Dropouts of University Freshmen: A Logit Regression Analysis.

Science.gov (United States)

Lam, Y. L. Jack

1984-01-01

Stepwise discriminant analysis coupled with logit regression analysis of freshmen data from Brandon University (Manitoba) indicated that six tested variables drawn from research on university dropouts were useful in predicting attrition: student status, residence, financial sources, distance from home town, goal fulfillment, and satisfaction with…

Simulation Experiments in Practice : Statistical Design and Regression Analysis

NARCIS (Netherlands)

Kleijnen, J.P.C.

2007-01-01

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic

Quality of life in breast cancer patients--a quantile regression analysis.

Science.gov (United States)

Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

2008-01-01

Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

RBP-J-interacting and tubulin-associated protein induces apoptosis and cell cycle arrest in human hepatocellular carcinoma by activating the p53–Fbxw7 pathway

International Nuclear Information System (INIS)

Wang, Haihe; Yang, Zhanchun; Liu, Chunbo; Huang, Shishun; Wang, Hongzhi; Chen, Yingli; Chen, Guofu

2014-01-01

Highlights: • RITA overexpression increased protein expression of p53 and Fbxw7 and downregulated the expression of cyclin D1, cyclin E, CDK2, Hes-1 and NF-κB p65. • RITA can significantly inhibit the in vitro growth of SMMC7721 and HepG2 cells. • RITA exerts tumor-suppressive effects in hepatocarcinogenesis through induction of G0/G1 cell cycle arrest and apoptosis and suggest a therapeutic application of RITA in HCC. - Abstract: Aberrant Notch signaling is observed in human hepatocellular carcinoma (HCC) and has been associated with the modulation of cell growth. However, the role of Notch signaling in HCC and its underlying mechanism remain elusive. RBP-J-interacting and tubulin-associated (RITA) mediates the nuclear export of RBP-J to tubulin fibers and downregulates Notch-mediated transcription. In this study, we found that RITA overexpression increased protein expression of p53 and Fbxw7 and downregulated the expression of cyclin D1, cyclin E, CDK2, Hes-1 and NF-κB p65. These changes led to growth inhibition and induced G0/G1 cell cycle arrest and apoptosis in SMMC7721 and HepG2 cells. Our findings indicate that RITA exerts tumor-suppressive effects in hepatocarcinogenesis through induction of G0/G1 cell cycle arrest and apoptosis and suggest a therapeutic application of RITA in HCC

Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

Science.gov (United States)

Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

2013-06-01

This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations.

Science.gov (United States)

Zarb, Francis; McEntee, Mark F; Rainford, Louise

2015-06-01

To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

Regression analysis of radiological parameters in nuclear power plants

International Nuclear Information System (INIS)

Bhargava, Pradeep; Verma, R.K.; Joshi, M.L.

2003-01-01

Indian Pressurized Heavy Water Reactors (PHWRs) have now attained maturity in their operations. Indian PHWR operation started in the year 1972. At present there are 12 operating PHWRs collectively producing nearly 2400 MWe. Sufficient radiological data are available for analysis to draw inferences which may be utilised for better understanding of radiological parameters influencing the collective internal dose. Tritium is the main contributor to the occupational internal dose originating in PHWRs. An attempt has been made to establish the relationship between radiological parameters, which may be useful to draw inferences about the internal dose. Regression analysis have been done to find out the relationship, if it exist, among the following variables: A. Specific tritium activity of heavy water (Moderator and PHT) and tritium concentration in air at various work locations. B. Internal collective occupational dose and tritium release to environment through air route. C. Specific tritium activity of heavy water (Moderator and PHT) and collective internal occupational dose. For this purpose multivariate regression analysis has been carried out. D. Tritium concentration in air at various work location and tritium release to environment through air route. For this purpose multivariate regression analysis has been carried out. This analysis reveals that collective internal dose has got very good correlation with the tritium activity release to the environment through air route. Whereas no correlation has been found between specific tritium activity in the heavy water systems and collective internal occupational dose. The good correlation has been found in case D and F test reveals that it is not by chance. (author)

Regression Analysis: Instructional Resource for Cost/Managerial Accounting

Science.gov (United States)

Stout, David E.

2015-01-01

This paper describes a classroom-tested instructional resource, grounded in principles of active learning and a constructivism, that embraces two primary objectives: "demystify" for accounting students technical material from statistics regarding ordinary least-squares (OLS) regression analysis--material that students may find obscure or…

Exploring emergency department 4-hour target performance and cancelled elective operations: a regression analysis of routinely collected and openly reported NHS trust data.

Science.gov (United States)

Keogh, Brad; Culliford, David; Guerrero-Ludueña, Richard; Monks, Thomas

2018-05-24

To quantify the effect of intrahospital patient flow on emergency department (ED) performance targets and indicate if the expectations set by the National Health Service (NHS) England 5-year forward review are realistic in returning emergency services to previous performance levels. Linear regression analysis of routinely reported trust activity and performance data using a series of cross-sectional studies. NHS trusts in England submitting routine nationally reported measures to NHS England. 142 acute non-specialist trusts operating in England between 2012 and 2016. The primary outcome measures were proportion of 4-hour waiting time breaches and cancelled elective operations. Univariate and multivariate linear regression models were used to show relationships between the outcome measures and various measures of trust activity including empty day beds, empty night beds, day bed to night bed ratio, ED conversion ratio and delayed transfers of care. Univariate regression results using the outcome of 4-hour breaches showed clear relationships with empty night beds and ED conversion ratio between 2012 and 2016. The day bed to night bed ratio showed an increasing ability to explain variation in performance between 2015 and 2016. Delayed transfers of care showed little evidence of an association. Multivariate model results indicated that the ability of patient flow variables to explain 4-hour target performance had reduced between 2012 and 2016 (19% to 12%), and had increased in explaining cancelled elective operations (7% to 17%). The flow of patients through trusts is shown to influence ED performance; however, performance has become less explainable by intratrust patient flow between 2012 and 2016. Some commonly stated explanatory factors such as delayed transfers of care showed limited evidence of being related. The results indicate some of the measures proposed by NHS England to reduce pressure on EDs may not have the desired impact on returning services to previous

Use of generalized regression models for the analysis of stress-rupture data

International Nuclear Information System (INIS)

Booker, M.K.

1978-01-01

The design of components for operation in an elevated-temperature environment often requires a detailed consideration of the creep and creep-rupture properties of the construction materials involved. Techniques for the analysis and extrapolation of creep data have been widely discussed. The paper presents a generalized regression approach to the analysis of such data. This approach has been applied to multiple heat data sets for types 304 and 316 austenitic stainless steel, ferritic 2 1 / 4 Cr-1 Mo steel, and the high-nickel austenitic alloy 800H. Analyses of data for single heats of several materials are also presented. All results appear good. The techniques presented represent a simple yet flexible and powerful means for the analysis and extrapolation of creep and creep-rupture data

Logistic Regression and Path Analysis Method to Analyze Factors influencing Students’ Achievement

Science.gov (United States)

Noeryanti, N.; Suryowati, K.; Setyawan, Y.; Aulia, R. R.

2018-04-01

Students' academic achievement cannot be separated from the influence of two factors namely internal and external factors. The first factors of the student (internal factors) consist of intelligence (X1), health (X2), interest (X3), and motivation of students (X4). The external factors consist of family environment (X5), school environment (X6), and society environment (X7). The objects of this research are eighth grade students of the school year 2016/2017 at SMPN 1 Jiwan Madiun sampled by using simple random sampling. Primary data are obtained by distributing questionnaires. The method used in this study is binary logistic regression analysis that aims to identify internal and external factors that affect student’s achievement and how the trends of them. Path Analysis was used to determine the factors that influence directly, indirectly or totally on student’s achievement. Based on the results of binary logistic regression, variables that affect student’s achievement are interest and motivation. And based on the results obtained by path analysis, factors that have a direct impact on student’s achievement are students’ interest (59%) and students’ motivation (27%). While the factors that have indirect influences on students’ achievement, are family environment (97%) and school environment (37).

Robust Mediation Analysis Based on Median Regression

Science.gov (United States)

Yuan, Ying; MacKinnon, David P.

2014-01-01

Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925

Assessing risk factors for periodontitis using regression

Science.gov (United States)

Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

2013-10-01

Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

Science.gov (United States)

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Linear regression in astronomy. II

Science.gov (United States)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

A comparative study of multiple regression analysis and back ...

Indian Academy of Sciences (India)

Abhijit Sarkar

artificial neural network (ANN) models to predict weld bead geometry and HAZ width in submerged arc welding ... Keywords. Submerged arc welding (SAW); multi-regression analysis (MRA); artificial neural network ..... Degree of freedom.

Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.

Science.gov (United States)

Hu, Yi-Chung

2014-01-01

On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.

Is ovarian hyperstimulation associated with higher blood pressure in 4-year-old IVF offspring? Part I: multivariable regression analysis.

Science.gov (United States)

Seggers, Jorien; Haadsma, Maaike L; La Bastide-Van Gemert, Sacha; Heineman, Maas Jan; Middelburg, Karin J; Roseboom, Tessa J; Schendelaar, Pamela; Van den Heuvel, Edwin R; Hadders-Algra, Mijna

2014-03-01

Does ovarian hyperstimulation, the in vitro procedure, or a combination of these two negatively influence blood pressure (BP) and anthropometrics of 4-year-old children born following IVF? Higher systolic blood pressure (SBP) percentiles were found in 4-year-old children born following conventional IVF with ovarian hyperstimulation compared with children born following IVF without ovarian hyperstimulation. Increasing evidence suggests that IVF, which has an increased incidence of preterm birth and low birthweight, is associated with higher BP and altered body fat distribution in offspring but the underlying mechanisms are largely unknown. We performed a prospective, assessor-blinded follow-up study in which 194 children were assessed. The attrition rate up until the 4-year-old assessment was 10%. We measured BP and anthropometrics of 4-year-old singletons born following conventional IVF with controlled ovarian hyperstimulation (COH-IVF, n = 63), or born following modified natural cycle IV (MNC-IVF, n = 52), or born to subfertile couples who conceived naturally (Sub-NC, n = 79). Both IVF and ICSI were performed. Primary outcome measures were the SBP percentiles and diastolic BP (DBP) percentiles. Anthropometric measures included triceps and subscapular skinfold thickness. Several multivariable regression analyses were applied in order to correct for subsets of confounders. The value 'B' is the unstandardized regression coefficient. SBP percentiles were significantly lower in the MNC-IVF group (mean 59, SD 24) than in the COH-IVF (mean 68, SD 22) and Sub-NC groups (mean 70, SD 16). The difference in SBP between COH-IVF and MNC-IVF remained significant after correction for current, early life and parental characteristics (B: 14.09; 95% confidence interval (CI): 5.39-22.79), whereas the difference between MNC-IVF and Sub-NC did not. DBP percentiles did not differ between groups. After correction for early life factors, subscapular skinfold thickness was thicker in the

Exploratory regression analysis: a tool for selecting models and determining predictor importance.

Science.gov (United States)

Braun, Michael T; Oswald, Frederick L

2011-06-01

Linear regression analysis is one of the most important tools in a researcher's toolbox for creating and testing predictive models. Although linear regression analysis indicates how strongly a set of predictor variables, taken together, will predict a relevant criterion (i.e., the multiple R), the analysis cannot indicate which predictors are the most important. Although there is no definitive or unambiguous method for establishing predictor variable importance, there are several accepted methods. This article reviews those methods for establishing predictor importance and provides a program (in Excel) for implementing them (available for direct download at http://dl.dropbox.com/u/2480715/ERA.xlsm?dl=1) . The program investigates all 2(p) - 1 submodels and produces several indices of predictor importance. This exploratory approach to linear regression, similar to other exploratory data analysis techniques, has the potential to yield both theoretical and practical benefits.

Regression analysis for LED color detection of visual-MIMO system

Science.gov (United States)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

Evaluation of syngas production unit cost of bio-gasification facility using regression analysis techniques

Energy Technology Data Exchange (ETDEWEB)

Deng, Yangyang; Parajuli, Prem B.

2011-08-10

Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.

External Tank Liquid Hydrogen (LH2) Prepress Regression Analysis Independent Review Technical Consultation Report

Science.gov (United States)

Parsons, Vickie s.

2009-01-01

The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.

Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data.

Science.gov (United States)

Ying, Gui-Shuang; Maguire, Maureen G; Glynn, Robert; Rosner, Bernard

2017-04-01

To describe and demonstrate appropriate linear regression methods for analyzing correlated continuous eye data. We describe several approaches to regression analysis involving both eyes, including mixed effects and marginal models under various covariance structures to account for inter-eye correlation. We demonstrate, with SAS statistical software, applications in a study comparing baseline refractive error between one eye with choroidal neovascularization (CNV) and the unaffected fellow eye, and in a study determining factors associated with visual field in the elderly. When refractive error from both eyes were analyzed with standard linear regression without accounting for inter-eye correlation (adjusting for demographic and ocular covariates), the difference between eyes with CNV and fellow eyes was 0.15 diopters (D; 95% confidence interval, CI -0.03 to 0.32D, p = 0.10). Using a mixed effects model or a marginal model, the estimated difference was the same but with narrower 95% CI (0.01 to 0.28D, p = 0.03). Standard regression for visual field data from both eyes provided biased estimates of standard error (generally underestimated) and smaller p-values, while analysis of the worse eye provided larger p-values than mixed effects models and marginal models. In research involving both eyes, ignoring inter-eye correlation can lead to invalid inferences. Analysis using only right or left eyes is valid, but decreases power. Worse-eye analysis can provide less power and biased estimates of effect. Mixed effects or marginal models using the eye as the unit of analysis should be used to appropriately account for inter-eye correlation and maximize power and precision.

Dose-Dependent Effects of Statins for Patients with Aneurysmal Subarachnoid Hemorrhage: Meta-Regression Analysis.

Science.gov (United States)

To, Minh-Son; Prakash, Shivesh; Poonnoose, Santosh I; Bihari, Shailesh

2018-05-01

The study uses meta-regression analysis to quantify the dose-dependent effects of statin pharmacotherapy on vasospasm, delayed ischemic neurologic deficits (DIND), and mortality in aneurysmal subarachnoid hemorrhage. Prospective, retrospective observational studies, and randomized controlled trials (RCTs) were retrieved by a systematic database search. Summary estimates were expressed as absolute risk (AR) for a given statin dose or control (placebo). Meta-regression using inverse variance weighting and robust variance estimation was performed to assess the effect of statin dose on transformed AR in a random effects model. Dose-dependence of predicted AR with 95% confidence interval (CI) was recovered by using Miller's Freeman-Tukey inverse. The database search and study selection criteria yielded 18 studies (2594 patients) for analysis. These included 12 RCTs, 4 retrospective observational studies, and 2 prospective observational studies. Twelve studies investigated simvastatin, whereas the remaining studies investigated atorvastatin, pravastatin, or pitavastatin, with simvastatin-equivalent doses ranging from 20 to 80 mg. Meta-regression revealed dose-dependent reductions in Freeman-Tukey-transformed AR of vasospasm (slope coefficient -0.00404, 95% CI -0.00720 to -0.00087; P = 0.0321), DIND (slope coefficient -0.00316, 95% CI -0.00586 to -0.00047; P = 0.0392), and mortality (slope coefficient -0.00345, 95% CI -0.00623 to -0.00067; P = 0.0352). The present meta-regression provides weak evidence for dose-dependent reductions in vasospasm, DIND and mortality associated with acute statin use after aneurysmal subarachnoid hemorrhage. However, the analysis was limited by substantial heterogeneity among individual studies. Greater dosing strategies are a potential consideration for future RCTs. Copyright © 2018 Elsevier Inc. All rights reserved.

Non-stationary hydrologic frequency analysis using B-spline quantile regression

Science.gov (United States)

Nasri, B.; Bouezmarni, T.; St-Hilaire, A.; Ouarda, T. B. M. J.

2017-11-01

Hydrologic frequency analysis is commonly used by engineers and hydrologists to provide the basic information on planning, design and management of hydraulic and water resources systems under the assumption of stationarity. However, with increasing evidence of climate change, it is possible that the assumption of stationarity, which is prerequisite for traditional frequency analysis and hence, the results of conventional analysis would become questionable. In this study, we consider a framework for frequency analysis of extremes based on B-Spline quantile regression which allows to model data in the presence of non-stationarity and/or dependence on covariates with linear and non-linear dependence. A Markov Chain Monte Carlo (MCMC) algorithm was used to estimate quantiles and their posterior distributions. A coefficient of determination and Bayesian information criterion (BIC) for quantile regression are used in order to select the best model, i.e. for each quantile, we choose the degree and number of knots of the adequate B-spline quantile regression model. The method is applied to annual maximum and minimum streamflow records in Ontario, Canada. Climate indices are considered to describe the non-stationarity in the variable of interest and to estimate the quantiles in this case. The results show large differences between the non-stationary quantiles and their stationary equivalents for an annual maximum and minimum discharge with high annual non-exceedance probabilities.

Alternative Methods of Regression

CERN Document Server

Birkes, David

2011-01-01

Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s

Trend Analysis of Cancer Mortality and Incidence in Panama, Using Joinpoint Regression Analysis.

Science.gov (United States)

Politis, Michael; Higuera, Gladys; Chang, Lissette Raquel; Gomez, Beatriz; Bares, Juan; Motta, Jorge

2015-06-01

Cancer is one of the leading causes of death worldwide and its incidence is expected to increase in the future. In Panama, cancer is also one of the leading causes of death. In 1964, a nationwide cancer registry was started and it was restructured and improved in 2012. The aim of this study is to utilize Joinpoint regression analysis to study the trends of the incidence and mortality of cancer in Panama in the last decade. Cancer mortality was estimated from the Panamanian National Institute of Census and Statistics Registry for the period 2001 to 2011. Cancer incidence was estimated from the Panamanian National Cancer Registry for the period 2000 to 2009. The Joinpoint Regression Analysis program, version 4.0.4, was used to calculate trends by age-adjusted incidence and mortality rates for selected cancers. Overall, the trend of age-adjusted cancer mortality in Panama has declined over the last 10 years (-1.12% per year). The cancers for which there was a significant increase in the trend of mortality were female breast cancer and ovarian cancer; while the highest increases in incidence were shown for breast cancer, liver cancer, and prostate cancer. Significant decrease in the trend of mortality was evidenced for the following: prostate cancer, lung and bronchus cancer, and cervical cancer; with respect to incidence, only oral and pharynx cancer in both sexes had a significant decrease. Some cancers showed no significant trends in incidence or mortality. This study reveals contrasting trends in cancer incidence and mortality in Panama in the last decade. Although Panama is considered an upper middle income nation, this study demonstrates that some cancer mortality trends, like the ones seen in cervical and lung cancer, behave similarly to the ones seen in high income countries. In contrast, other types, like breast cancer, follow a pattern seen in countries undergoing a transition to a developed economy with its associated lifestyle, nutrition, and body weight

HIV and schistosomiasis in rural Zimbabwe

DEFF Research Database (Denmark)

Kotzé, Sebastian Ranzi; Zinyama-Gutsire, Rutendo; Kallestrup, Per

2015-01-01

BACKGROUND: Vitamin A has widespread effects on immune function and is therefore interesting in HIV-infection. Retinol-binding protein (RBP or RBP4) is a negative acute-phase protein and a marker of vitamin A status. Our aim was to investigate the association of RBP with HIV progression, infection...... with schistosomiasis, inflammatory cytokines, and mortality. METHODS: The study included 192 HIV-infected and 177 HIV-uninfected individuals from Mupfure in rural Zimbabwe. Of these, 208 were infected with Schistosoma haematobium, 27 with S. mansoni and 48 with both. Plasma RBP, HIV-RNA, CD4 cell count, haemoglobin......, cytokines, clinical staging (CDC category), self-reported level of function (Karnoffsky Performance Score, KPS) and schistosomiasis status were assessed at baseline. Participants were followed up for survival 3-4 years post-enrolment. RESULTS: RBP levels were lower in HIV-infected individuals(p

On macroeconomic values investigation using fuzzy linear regression analysis

Directory of Open Access Journals (Sweden)

Richard Pospíšil

2017-06-01

Full Text Available The theoretical background for abstract formalization of the vague phenomenon of complex systems is the fuzzy set theory. In the paper, vague data is defined as specialized fuzzy sets - fuzzy numbers and there is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague parameters. To identify the fuzzy coefficients of the model, the genetic algorithm is used. The linear approximation of the vague function together with its possibility area is analytically and graphically expressed. A suitable application is performed in the tasks of the time series fuzzy regression analysis. The time-trend and seasonal cycles including their possibility areas are calculated and expressed. The examples are presented from the economy field, namely the time-development of unemployment, agricultural production and construction respectively between 2009 and 2011 in the Czech Republic. The results are shown in the form of the fuzzy regression models of variables of time series. For the period 2009-2011, the analysis assumptions about seasonal behaviour of variables and the relationship between them were confirmed; in 2010, the system behaved fuzzier and the relationships between the variables were vaguer, that has a lot of causes, from the different elasticity of demand, through state interventions to globalization and transnational impacts.

REGRESSION ANALYSIS OF SEA-SURFACE-TEMPERATURE PATTERNS FOR THE NORTH PACIFIC OCEAN.

Science.gov (United States)

SEA WATER, *SURFACE TEMPERATURE, *OCEANOGRAPHIC DATA, PACIFIC OCEAN, REGRESSION ANALYSIS , STATISTICAL ANALYSIS, UNDERWATER EQUIPMENT, DETECTION, UNDERWATER COMMUNICATIONS, DISTRIBUTION, THERMAL PROPERTIES, COMPUTERS.

Regression analysis understanding and building business and economic models using Excel

CERN Document Server

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

Regression Models for Repairable Systems

Czech Academy of Sciences Publication Activity Database

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

Nonlinear regression analysis for evaluating tracer binding parameters using the programmable K1003 desk computer

International Nuclear Information System (INIS)

Sarrach, D.; Strohner, P.

1986-01-01

The Gauss-Newton algorithm has been used to evaluate tracer binding parameters of RIA by nonlinear regression analysis. The calculations were carried out on the K1003 desk computer. Equations for simple binding models and its derivatives are presented. The advantages of nonlinear regression analysis over linear regression are demonstrated

Regression analysis for the social sciences

CERN Document Server

Gordon, Rachel A

2010-01-01

The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.

Logistic regression analysis of risk factors for postoperative recurrence of spinal tumors and analysis of prognostic factors.

Science.gov (United States)

Zhang, Shanyong; Yang, Lili; Peng, Chuangang; Wu, Minfei

2018-02-01

The aim of the present study was to investigate the risk factors for postoperative recurrence of spinal tumors by logistic regression analysis and analysis of prognostic factors. In total, 77 male and 48 female patients with spinal tumor were selected in our hospital from January, 2010 to December, 2015 and divided into the benign (n=76) and malignant groups (n=49). All the patients underwent microsurgical resection of spinal tumors and were reviewed regularly 3 months after operation. The McCormick grading system was used to evaluate the postoperative spinal cord function. Data were subjected to statistical analysis. Of the 125 cases, 63 cases showed improvement after operation, 50 cases were stable, and deterioration was found in 12 cases. The improvement rate of patients with cervical spine tumor, which reached 56.3%, was the highest. Fifty-two cases of sensory disturbance, 34 cases of pain, 30 cases of inability to exercise, 26 cases of ataxia, and 12 cases of sphincter disorders were found after operation. Seventy-two cases (57.6%) underwent total resection, 18 cases (14.4%) received subtotal resection, 23 cases (18.4%) received partial resection, and 12 cases (9.6%) were only treated with biopsy/decompression. Postoperative recurrence was found in 57 cases (45.6%). The mean recurrence time of patients in the malignant group was 27.49±6.09 months, and the mean recurrence time of patients in the benign group was 40.62±4.34. The results were significantly different (Pregression analysis of total resection-related factors showed that total resection should be the preferred treatment for patients with benign tumors, thoracic and lumbosacral tumors, and lower McCormick grade, as well as patients without syringomyelia and intramedullary tumors. Logistic regression analysis of recurrence-related factors revealed that the recurrence rate was relatively higher in patients with malignant, cervical, thoracic and lumbosacral, intramedullary tumors, and higher Mc

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

Science.gov (United States)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

The non-condition logistic regression analysis of the reason of hypothyroidism after hyperthyroidism with 131I treatment

International Nuclear Information System (INIS)

Dang Yaping; Hu Guoying; Meng Xianwen

1994-01-01

There are many opinions on the reason of hypothyroidism after hyperthyroidism with 131 I treatment. In this respect, there are a few scientific analyses and reports. The non-condition logistic regression solved this problem successfully. It has a higher scientific value and confidence in the risk factor analysis. 748 follow-up patients' data were analysed by the non-condition logistic regression. The results shown that the half-life and 131 I dose were the main causes of the incidence of hypothyroidism. The degree of confidence is 92.4%

Composite marginal quantile regression analysis for longitudinal adolescent body mass index data.

Science.gov (United States)

Yang, Chi-Chuan; Chen, Yi-Hau; Chang, Hsing-Yi

2017-09-20

Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long-term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai (Biometrics 2016; 72:74-84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness-of-fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Treating experimental data of inverse kinetic method by unitary linear regression analysis

International Nuclear Information System (INIS)

Zhao Yusen; Chen Xiaoliang

2009-01-01

The theory of treating experimental data of inverse kinetic method by unitary linear regression analysis was described. Not only the reactivity, but also the effective neutron source intensity could be calculated by this method. Computer code was compiled base on the inverse kinetic method and unitary linear regression analysis. The data of zero power facility BFS-1 in Russia were processed and the results were compared. The results show that the reactivity and the effective neutron source intensity can be obtained correctly by treating experimental data of inverse kinetic method using unitary linear regression analysis and the precision of reactivity measurement is improved. The central element efficiency can be calculated by using the reactivity. The result also shows that the effect to reactivity measurement caused by external neutron source should be considered when the reactor power is low and the intensity of external neutron source is strong. (authors)

Regression analysis of informative current status data with the additive hazards model.

Science.gov (United States)

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.

Credit Scoring Problem Based on Regression Analysis

OpenAIRE

Khassawneh, Bashar Suhil Jad Allah

2014-01-01

ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....

MULGRES: a computer program for stepwise multiple regression analysis

Science.gov (United States)

A. Jeff Martin

1971-01-01

MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.

Real-time regression analysis with deep convolutional neural networks

OpenAIRE

Huerta, E. A.; George, Daniel; Zhao, Zhizhen; Allen, Gabrielle

2018-01-01

We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data. We showcase the application of this new method with a timely case study, and then discuss the applicability of this approach to tackle similar challenges across science domains.

[Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

Science.gov (United States)

Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

2017-05-10

We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P valuelinear regression P value). The statistical power of CAT test decreased, while the result of linear regression analysis remained the same when population size was reduced by 100 times and AMI incidence rate remained unchanged. The two statistical methods have their advantages and disadvantages. It is necessary to choose statistical method according the fitting degree of data, or comprehensively analyze the results of two methods.

The evolution of GDP in USA using cyclic regression analysis

OpenAIRE

Catalin Angelo IOAN; Gina IOAN

2013-01-01

Based on the four major types of economic cycles (Kondratieff, Juglar, Kitchin, Kuznet), the paper aims to determine their actual length (for the U.S. economy) using cyclic regressions based on Fourier analysis.

Quantile regression for the statistical analysis of immunological data with many non-detects.

Science.gov (United States)

Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

2012-07-07

Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

Optimal choice of basis functions in the linear regression analysis

International Nuclear Information System (INIS)

Khotinskij, A.M.

1988-01-01

Problem of optimal choice of basis functions in the linear regression analysis is investigated. Step algorithm with estimation of its efficiency, which holds true at finite number of measurements, is suggested. Conditions, providing the probability of correct choice close to 1 are formulated. Application of the step algorithm to analysis of decay curves is substantiated. 8 refs

AcEST: DK947347 [AcEST

Lifescience Database Archive (English)

Full Text Available Prot sp_hit_id Q4Z8K6 Definition sp|Q4Z8K6|RBP9X_DROME Protein midlife crisis OS=Drosophila melanogaster Ali...g significant alignments: (bits) Value sp|Q4Z8K6|RBP9X_DROME Protein midlife crisis OS=Drosophila melan... 3..... 30 7.6 sp|Q07954|LRP1_HUMAN Prolow-density lipoprotein receptor-related... 30 7.6 >sp|Q4Z8K6|RBP9X_DROME Protein midlife crisis

Regression analysis for the social sciences

CERN Document Server

Gordon, Rachel A

2015-01-01

Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.

Extreme heterogeneity of polyadenylation sites in mRNAs encoding chloroplast RNA-binding proteins in Nicotiana plumbaginifolia.

Science.gov (United States)

Klahre, U; Hemmings-Mieszczak, M; Filipowicz, W

1995-06-01

We have previously characterized nuclear cDNA clones encoding two RNA binding proteins, CP-RBP30 and CP-RBP-31, which are targeted to chloroplasts in Nicotiana plumbaginifolia. In this report we describe the analysis of the 3'-untranslated regions (3'-UTRs) in 22 CP-RBP30 and 8 CP-RBP31 clones which reveals that mRNAs encoding both proteins have a very complex polyadenylation pattern. Fourteen distinct poly(A) sites were identified among CP-RBP30 clones and four sites among the CP-RBP31 clones. The authenticity of the sites was confirmed by RNase A/T1 mapping of N. plumbaginifolia RNA. CP-RBP30 provides an extreme example of the heterogeneity known to be a feature of mRNA polyadenylation in higher plants. Using PCR we have demonstrated that CP-RBP genes in N. plumbaginifolia and N. sylvestris, in addition to the previously described introns interrupting the coding region, contain an intron located in the 3' non-coding part of the gene. In the case of the CP-RBP31, we have identified one polyadenylation event occurring in this intron.

A method for nonlinear exponential regression analysis

Science.gov (United States)

Junkin, B. G.

1971-01-01

A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.

Analysis of Functional Data with Focus on Multinomial Regression and Multilevel Data

DEFF Research Database (Denmark)

Mousavi, Seyed Nourollah

Functional data analysis (FDA) is a fast growing area in statistical research with increasingly diverse range of application from economics, medicine, agriculture, chemometrics, etc. Functional regression is an area of FDA which has received the most attention both in aspects of application...... and methodological development. Our main Functional data analysis (FDA) is a fast growing area in statistical research with increasingly diverse range of application from economics, medicine, agriculture, chemometrics, etc. Functional regression is an area of FDA which has received the most attention both in aspects...

Skeletal height estimation from regression analysis of sternal lengths in a Northwest Indian population of Chandigarh region: a postmortem study.

Science.gov (United States)

Singh, Jagmahender; Pathak, R K; Chavali, Krishnadutt H

2011-03-20

Skeletal height estimation from regression analysis of eight sternal lengths in the subjects of Chandigarh zone of Northwest India is the topic of discussion in this study. Analysis of eight sternal lengths (length of manubrium, length of mesosternum, combined length of manubrium and mesosternum, total sternal length and first four intercostals lengths of mesosternum) measured from 252 male and 91 female sternums obtained at postmortems revealed that mean cadaver stature and sternal lengths were more in North Indians and males than the South Indians and females. Except intercostal lengths, all the sternal lengths were positively correlated with stature of the deceased in both sexes (P regression analysis of sternal lengths was found more useful than the linear regression for stature estimation. Using multivariate regression analysis, the combined length of manubrium and mesosternum in both sexes and the length of manubrium along with 2nd and 3rd intercostal lengths of mesosternum in males were selected as best estimators of stature. Nonetheless, the stature of males can be predicted with SEE of 6.66 (R(2) = 0.16, r = 0.318) from combination of MBL+BL_3+LM+BL_2, and in females from MBL only, it can be estimated with SEE of 6.65 (R(2) = 0.10, r = 0.318), whereas from the multiple regression analysis of pooled data, stature can be known with SEE of 6.97 (R(2) = 0.387, r = 575) from the combination of MBL+LM+BL_2+TSL+BL_3. The R(2) and F-ratio were found to be statistically significant for almost all the variables in both the sexes, except 4th intercostal length in males and 2nd to 4th intercostal lengths in females. The 'major' sternal lengths were more useful than the 'minor' ones for stature estimation The universal regression analysis used by Kanchan et al. [39] when applied to sternal lengths, gave satisfactory estimates of stature for males only but female stature was comparatively better estimated from simple linear regressions. But they are not proposed for the

Regression analysis of a chemical reaction fouling model

International Nuclear Information System (INIS)

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

[A SAS marco program for batch processing of univariate Cox regression analysis for great database].

Science.gov (United States)

Yang, Rendong; Xiong, Jie; Peng, Yangqin; Peng, Xiaoning; Zeng, Xiaomin

2015-02-01

To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.

Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments

NARCIS (Netherlands)

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

Application of multilinear regression analysis in modeling of soil ...

African Journals Online (AJOL)

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, ...

Boosted beta regression.

Directory of Open Access Journals (Sweden)

Matthias Schmid

Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.

Bias due to two-stage residual-outcome regression analysis in genetic association studies.

Science.gov (United States)

Demissie, Serkalem; Cupples, L Adrienne

2011-11-01

Association studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two-stage regression analysis, sometimes referred to as residual- or adjusted-outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual-outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted-outcome and the SNP is evaluated by a simple linear regression of the adjusted-outcome on the SNP. In this article, we examine the performance of this two-stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are correlated, the two-stage approach results in biased genotypic effect and loss of power. Bias is always toward the null and increases with the squared-correlation between the SNP and the covariate (). For example, for , 0.1, and 0.5, two-stage analysis results in, respectively, 0, 10, and 50% attenuation in the SNP effect. As expected, MLR was always unbiased. Since individual SNPs often show little or no correlation with covariates, a two-stage analysis is expected to perform as well as MLR in many genetic studies; however, it produces considerably different results from MLR and may lead to incorrect conclusions when independent variables are highly correlated. While a useful alternative to MLR under , the two -stage approach has serious limitations. Its use as a simple substitute for MLR should be avoided. © 2011 Wiley Periodicals, Inc.

Histone demethylase retinoblastoma binding protein 2 regulates the expression of α-smooth muscle actin and vimentin in cirrhotic livers

Energy Technology Data Exchange (ETDEWEB)

Wang, Q. [Department of Microbiology, Key Laboratory for Experimental Teratology of the Chinese Ministry of Education, School of Medicine, Shandong University, Jinan (China); Wang, L.X. [Department of Pharmacology, School of Medicine, Shandong University, Jinan (China); Zeng, J.P. [Department of Biochemistry, School of Medicine, Shandong University, Jinan (China); Liu, X.J.; Liang, X.M.; Zhou, Y.B. [Department of Microbiology, Key Laboratory for Experimental Teratology of the Chinese Ministry of Education, School of Medicine, Shandong University, Jinan (China)

2013-09-06

Liver cirrhosis is one of the most common diseases of Chinese patients. Herein, we report the high expression of a newly identified histone 3 lysine 4 demethylase, retinoblastoma binding protein 2 (RBP2), and its role in liver cirrhosis in humans. The siRNA knockdown of RBP2 expression in hepatic stellate cells (HSCs) reduced levels of α-smooth muscle actin (α-SMA) and vimentin and decreased the proliferation of HSCs; and overexpression of RBP2 increased α-SMA and vimentin levels. Treatment with transforming growth factor β (TGF-β) upregulated the expression of RBP2, α-SMA, and vimentin, and the siRNA knockdown of RBP2 expression attenuated TGF-β-mediated upregulation of α-SMA and vimentin expression and HSC proliferation. Furthermore, RBP2 was highly expressed in cirrhotic rat livers. Therefore, RBP2 may participate in the pathogenesis of liver cirrhosis by regulating the expression of α-SMA and vimentin. RBP2 may be a useful marker for the diagnosis and treatment of liver cirrhosis.

Histone demethylase retinoblastoma binding protein 2 regulates the expression of α-smooth muscle actin and vimentin in cirrhotic livers

International Nuclear Information System (INIS)

Wang, Q.; Wang, L.X.; Zeng, J.P.; Liu, X.J.; Liang, X.M.; Zhou, Y.B.

2013-01-01

Liver cirrhosis is one of the most common diseases of Chinese patients. Herein, we report the high expression of a newly identified histone 3 lysine 4 demethylase, retinoblastoma binding protein 2 (RBP2), and its role in liver cirrhosis in humans. The siRNA knockdown of RBP2 expression in hepatic stellate cells (HSCs) reduced levels of α-smooth muscle actin (α-SMA) and vimentin and decreased the proliferation of HSCs; and overexpression of RBP2 increased α-SMA and vimentin levels. Treatment with transforming growth factor β (TGF-β) upregulated the expression of RBP2, α-SMA, and vimentin, and the siRNA knockdown of RBP2 expression attenuated TGF-β-mediated upregulation of α-SMA and vimentin expression and HSC proliferation. Furthermore, RBP2 was highly expressed in cirrhotic rat livers. Therefore, RBP2 may participate in the pathogenesis of liver cirrhosis by regulating the expression of α-SMA and vimentin. RBP2 may be a useful marker for the diagnosis and treatment of liver cirrhosis

Antioxidant Enzyme Activities and Lipid Oxidation in Rape (Brassica campestris L. Bee Pollen Added to Salami during Processing

Directory of Open Access Journals (Sweden)

Yawei Zhang

2016-10-01

Full Text Available The present research investigated the antioxidant effect of rape (Brassica campestris L. bee pollen (RBP on salami during processing. Eight flavonoids in RBP ethanol extract were quantified by high-performance liquid chromatography-mass spectrometry (HPLC-MS analysis, and quercetin, rutin, and kaempferol were the major bioactive compounds. The RBP ethanol extract exhibited higher total antioxidant capacity than 6-hydroxy-2,5,7,8-tertramethylchromancarboxylic acid (trolox at the same concentration. The salami with 0.05% RBP extract had higher catalase (CAT, superoxide dismutase (SOD, and glutathione peroxidase (GSH-Px activities than that of the control throughout the processing time (p < 0.05. Significant decreases in peroxide value (POV and thiobarbituric acid-reactive substances (TBARS were obtained in the final salami product with 0.05% RBP ethanol extract or 1% RBP (p < 0.05. These results suggested that RBP could improve oxidative stability and had a good potential as a natural antioxidant for retarding lipid oxidation.

An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy

DEFF Research Database (Denmark)

Merlo, Juan; Wagner, Philippe; Ghith, Nermin

2016-01-01

BACKGROUND AND AIM: Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that disting...

Advanced statistics: linear regression, part I: simple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science

International Nuclear Information System (INIS)

Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei

2007-01-01

Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age

Characteristics of retinol accumulation from serum retinol-binding protein by cultured sertoli cells

International Nuclear Information System (INIS)

Shingleton, J.L.; Skinner, M.K.; Ong, D.E.

1989-01-01

The uptake of retinol was examined in cultured Sertoli cells when retinol was provided as a complex with the transport protein retinol-binding protein (RBP). Sertoil cells accumulated [ 3 H]retinol in a time- and temperature-dependent manner. The change in rate of retinol accumulation occurred when the cells had accumulated approximately 0.53 pmol of retinol/μg of cellular DNA. Extraction and HPLC analysis of the cell-associated radioactivity yielded retinol and retinyl esters, indicating that a significant proportion of the accumulated retinol was esterified. Excess unlabeled retinol-RBP competed with [ 3 H]retinol-RBP for [ 3 H]retinol delivery to the cells, indicating that RBP delivery of retinol was a saturable and competable process. However, free [ 3 H]retinol associated with Sertoli cells in a noncompetable manner. The transport constant for specific retinol accumulation from RBP was 3.0 μM. Neither iodinated nor reductively methylated RBP was accumulated by or tightly bound to Sertoli cells. Competition studies indicated, however, that protein recognition is important in the retinol uptake process. RBP, CRBP, and CRBP(II) competed with [ 3 H]retinol-RBP for [ 3 H]retinol accumulation, but free retinol, retinol-bovine serum albumin, and retinol-β-lactoglobulin did not. These studies indicated that Sertoli cell uptake of retinol involved recognition of the retinol-RBP complex at the cell surface with subsequent internalization of retinol, but not RBP

Weibull and lognormal Taguchi analysis using multiple linear regression

International Nuclear Information System (INIS)

Piña-Monarrez, Manuel R.; Ortiz-Yañez, Jesús F.

2015-01-01

The paper provides to reliability practitioners with a method (1) to estimate the robust Weibull family when the Taguchi method (TM) is applied, (2) to estimate the normal operational Weibull family in an accelerated life testing (ALT) analysis to give confidence to the extrapolation and (3) to perform the ANOVA analysis to both the robust and the normal operational Weibull family. On the other hand, because the Weibull distribution neither has the normal additive property nor has a direct relationship with the normal parameters (µ, σ), in this paper, the issues of estimating a Weibull family by using a design of experiment (DOE) are first addressed by using an L_9 (3"4) orthogonal array (OA) in both the TM and in the Weibull proportional hazard model approach (WPHM). Then, by using the Weibull/Gumbel and the lognormal/normal relationships and multiple linear regression, the direct relationships between the Weibull and the lifetime parameters are derived and used to formulate the proposed method. Moreover, since the derived direct relationships always hold, the method is generalized to the lognormal and ALT analysis. Finally, the method’s efficiency is shown through its application to the used OA and to a set of ALT data. - Highlights: • It gives the statistical relations and steps to use the Taguchi Method (TM) to analyze Weibull data. • It gives the steps to determine the unknown Weibull family to both the robust TM setting and the normal ALT level. • It gives a method to determine the expected lifetimes and to perform its ANOVA analysis in TM and ALT analysis. • It gives a method to give confidence to the extrapolation in an ALT analysis by using the Weibull family of the normal level.

Forecasting urban water demand: A meta-regression analysis.

Science.gov (United States)

Sebri, Maamar

2016-12-01

Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

Science.gov (United States)

Anderson, Carl A; McRae, Allan F; Visscher, Peter M

2006-07-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

The use of cognitive ability measures as explanatory variables in regression analysis.

Science.gov (United States)

Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J

2012-12-01

Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual's wage, or a decision such as an individual's education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score , constructed via standard psychometric practice from individuals' responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a "mixed effects structural equations" (MESE) model, may be more appropriate in many circumstances.

Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

Science.gov (United States)

Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

2016-01-01

Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

Temporal trends in sperm count: a systematic review and meta-regression analysis.

Science.gov (United States)

Levine, Hagai; Jørgensen, Niels; Martino-Andrade, Anderson; Mendiola, Jaime; Weksler-Derri, Dan; Mindlis, Irina; Pinotti, Rachel; Swan, Shanna H

2017-11-01

Reported declines in sperm counts remain controversial today and recent trends are unknown. A definitive meta-analysis is critical given the predictive value of sperm count for fertility, morbidity and mortality. To provide a systematic review and meta-regression analysis of recent trends in sperm counts as measured by sperm concentration (SC) and total sperm count (TSC), and their modification by fertility and geographic group. PubMed/MEDLINE and EMBASE were searched for English language studies of human SC published in 1981-2013. Following a predefined protocol 7518 abstracts were screened and 2510 full articles reporting primary data on SC were reviewed. A total of 244 estimates of SC and TSC from 185 studies of 42 935 men who provided semen samples in 1973-2011 were extracted for meta-regression analysis, as well as information on years of sample collection and covariates [fertility group ('Unselected by fertility' versus 'Fertile'), geographic group ('Western', including North America, Europe Australia and New Zealand versus 'Other', including South America, Asia and Africa), age, ejaculation abstinence time, semen collection method, method of measuring SC and semen volume, exclusion criteria and indicators of completeness of covariate data]. The slopes of SC and TSC were estimated as functions of sample collection year using both simple linear regression and weighted meta-regression models and the latter were adjusted for pre-determined covariates and modification by fertility and geographic group. Assumptions were examined using multiple sensitivity analyses and nonlinear models. SC declined significantly between 1973 and 2011 (slope in unadjusted simple regression models -0.70 million/ml/year; 95% CI: -0.72 to -0.69; P regression analysis reports a significant decline in sperm counts (as measured by SC and TSC) between 1973 and 2011, driven by a 50-60% decline among men unselected by fertility from North America, Europe, Australia and New Zealand. Because

Neighborhood social capital and crime victimization: comparison of spatial regression analysis and hierarchical regression analysis.

Science.gov (United States)

Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro

2012-11-01

Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. Copyright

Introduction to regression graphics

CERN Document Server

Cook, R Dennis

2009-01-01

Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava

Characterization of sonographically indeterminate ovarian tumors with MR imaging. A logistic regression analysis

International Nuclear Information System (INIS)

Yamashita, Y.; Hatanaka, Y.; Torashima, M.; Takahashi, M.; Miyazaki, K.; Okamura, H.

1997-01-01

Purpose: The goal of this study was to maximize the discrimination between benign and malignant masses in patients with sonographically indeterminate ovarian lesions by means of unenhanced and contrast-enhanced MR imaging, and to develop a computer-assisted diagnosis system. Material and Methods: Findings in precontrast and Gd-DTPA contrast-enhanced MR images of 104 patients with 115 sonographically indeterminate ovarian masses were analyzed, and the results were correlated with histopathological findings. Of 115 lesions, 65 were benign (23 cystadenomas, 13 complex cysts, 11 teratomas, 6 fibrothecomas, 12 others) and 50 were malignant (32 ovarian carcinomas, 7 metastatic tumors of the ovary, 4 carcinomas of the fallopian tubes, 7 others). A logistic regression analysis was performed to discriminate between benign and malignant lesions, and a model of a computer-assisted diagnosis was developed. This model was prospectively tested in 75 cases of ovarian tumors found at other institutions. Results: From the univariate analysis, the following parameters were selected as significant for predicting malignancy (p≤0.05): A solid or cystic mass with a large solid component or wall thickness greater than 3 mm; complex internal architecture; ascites; and bilaterality. Based on these parameters, a model of a computer-assisted diagnosis system was developed with the logistic regression analysis. To distinguish benign from malignant lesions, the maximum cut-off point was obtained between 0.47 and 0.51. In a prospective application of this model, 87% of the lesions were accurately identified as benign or malignant. (orig.)

Analysis of γ spectra in airborne radioactivity measurements using multiple linear regressions

International Nuclear Information System (INIS)

Bao Min; Shi Quanlin; Zhang Jiamei

2004-01-01

This paper describes the net peak counts calculating of nuclide 137 Cs at 662 keV of γ spectra in airborne radioactivity measurements using multiple linear regressions. Mathematic model is founded by analyzing every factor that has contribution to Cs peak counts in spectra, and multiple linear regression function is established. Calculating process adopts stepwise regression, and the indistinctive factors are eliminated by F check. The regression results and its uncertainty are calculated using Least Square Estimation, then the Cs peak net counts and its uncertainty can be gotten. The analysis results for experimental spectrum are displayed. The influence of energy shift and energy resolution on the analyzing result is discussed. In comparison with the stripping spectra method, multiple linear regression method needn't stripping radios, and the calculating result has relation with the counts in Cs peak only, and the calculating uncertainty is reduced. (authors)

Potential renal toxicity bio-markers indicating radiation injury after 177Lu-octreotate treatment

International Nuclear Information System (INIS)

Dalmo, J.; Forssell-Aronsson, E.; Westberg, E.; Toernqvist, M.; Svedborn, L.; Barregaerd, L.

2015-01-01

Full text of publication follows. The kidneys are one of the most exposed non-tumor tissues and regarded as one of the main dose-limiting organs in peptide receptor radionuclide therapy (PRRT). [ 177 Lu-DOTA0, Tyr3]-octreotate ( 177 Lu-octreotate) has shown promising results in the treatment of somatostatin receptor over-expressing neuroendocrine tumors, but optimization is still needed. The ability to give each patient as much 177 Lu-octreotate as possible without inducing nephrotoxicity is necessary for an efficient treatment. However, due to large inter-individual differences in uptake and retention in the kidneys, there is a need for efficient methods that can indicate renal injury early. A possible way is to identify bio-markers for high risk of radiation nephrotoxicity. The aim of this study was to investigate the potential of using urinary retinol binding protein (RBP), and blood valinhydantoin (VH) as bio-markers of nephrotoxicity on adult mice after 177 Lu-octreotate treatment. BALB/c nude mice (n=6/group) were i.v. injected with 60 MBq or 120 MBq of 177 Lu-octreotate. The control group was mock treated with saline. Spot urine samples were collected before injection, and 14, 30, 60 and 90 days after injection. Analysis of RBP4 and creatinine was performed using Mouse RBP4 ELISA kit and Creatinine kit from R/D Systems, respectively. Erythrocytes were separated from whole blood samples collected 90 days after injection, and analysed for VH by LC-MS/MS. The ratio between VH and a volumetric standard was calculated. The RBP/creatinine level increased with time in both groups given 177 Lu-octreotate, with earlier and higher response for the 120 MBq group. No clear change in VH level between the different groups was observed. The results show that RBP may be a promising new bio-marker for radiation induced kidney toxicity. The presently used method based on VH was not sensitive enough to be used as kidney toxicity marker. Further studies on mice are ongoing to

Regression Analysis and Calibration Recommendations for the Characterization of Balance Temperature Effects

Science.gov (United States)

Ulbrich, N.; Volden, T.

2018-01-01

Analysis and use of temperature-dependent wind tunnel strain-gage balance calibration data are discussed in the paper. First, three different methods are presented and compared that may be used to process temperature-dependent strain-gage balance data. The first method uses an extended set of independent variables in order to process the data and predict balance loads. The second method applies an extended load iteration equation during the analysis of balance calibration data. The third method uses temperature-dependent sensitivities for the data analysis. Physical interpretations of the most important temperature-dependent regression model terms are provided that relate temperature compensation imperfections and the temperature-dependent nature of the gage factor to sets of regression model terms. Finally, balance calibration recommendations are listed so that temperature-dependent calibration data can be obtained and successfully processed using the reviewed analysis methods.

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

Science.gov (United States)

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

Determining Balıkesir’s Energy Potential Using a Regression Analysis Computer Program

Directory of Open Access Journals (Sweden)

Bedri Yüksel

2014-01-01

Full Text Available Solar power and wind energy are used concurrently during specific periods, while at other times only the more efficient is used, and hybrid systems make this possible. When establishing a hybrid system, the extent to which these two energy sources support each other needs to be taken into account. This paper is a study of the effects of wind speed, insolation levels, and the meteorological parameters of temperature and humidity on the energy potential in Balıkesir, in the Marmara region of Turkey. The relationship between the parameters was studied using a multiple linear regression method. Using a designed-for-purpose computer program, two different regression equations were derived, with wind speed being the dependent variable in the first and insolation levels in the second. The regression equations yielded accurate results. The computer program allowed for the rapid calculation of different acceptance rates. The results of the statistical analysis proved the reliability of the equations. An estimate of identified meteorological parameters and unknown parameters could be produced with a specified precision by using the regression analysis method. The regression equations also worked for the evaluation of energy potential.

Covariate Imbalance and Adjustment for Logistic Regression Analysis of Clinical Trial Data

Science.gov (United States)

Ciolino, Jody D.; Martin, Reneé H.; Zhao, Wenle; Jauch, Edward C.; Hill, Michael D.; Palesch, Yuko Y.

2014-01-01

In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This paper uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be pre-specified. Unplanned adjusted analyses should be considered secondary. Results suggest that that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored. PMID:24138438

Predictive model of Amorphophallus muelleri growth in some agroforestry in East Java by multiple regression analysis

Directory of Open Access Journals (Sweden)

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%.

Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis

Science.gov (United States)

Jarrell, Stephen B.; Stanley, T. D.

2004-01-01

The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.

Statistical methods in regression and calibration analysis of chromosome aberration data

International Nuclear Information System (INIS)

Merkle, W.

1983-01-01

The method of iteratively reweighted least squares for the regression analysis of Poisson distributed chromosome aberration data is reviewed in the context of other fit procedures used in the cytogenetic literature. As an application of the resulting regression curves methods for calculating confidence intervals on dose from aberration yield are described and compared, and, for the linear quadratic model a confidence interval is given. Emphasis is placed on the rational interpretation and the limitations of various methods from a statistical point of view. (orig./MG)

Diagnosis of cranial hemangioma: Comparison between logistic regression analysis and neuronal network

International Nuclear Information System (INIS)

Arana, E.; Marti-Bonmati, L.; Bautista, D.; Paredes, R.

1998-01-01

To study the utility of logistic regression and the neuronal network in the diagnosis of cranial hemangiomas. Fifteen patients presenting hemangiomas were selected form a total of 167 patients with cranial lesions. All were evaluated by plain radiography and computed tomography (CT). Nineteen variables in their medical records were reviewed. Logistic regression and neuronal network models were constructed and validated by the jackknife (leave-one-out) approach. The yields of the two models were compared by means of ROC curves, using the area under the curve as parameter. Seven men and 8 women presented hemangiomas. The mean age of these patients was 38.4 (15.4 years (mea ± standard deviation). Logistic regression identified as significant variables the shape, soft tissue mass and periosteal reaction. The neuronal network lent more importance to the existence of ossified matrix, ruptured cortical vein and the mixed calcified-blastic (trabeculated) pattern. The neuronal network showed a greater yield than logistic regression (Az, 0.9409) (0.004 versus 0.7211± 0.075; p<0.001). The neuronal network discloses hidden interactions among the variables, providing a higher yield in the characterization of cranial hemangiomas and constituting a medical diagnostic acid. (Author)29 refs

The levels of adipokines in relation to hormonal changes during the menstrual cycle in young, normal-weight women

Directory of Open Access Journals (Sweden)

Katarzyna Wyskida

2017-11-01

Full Text Available Context: The aim of this study was to assess the plasma leptin, adiponectin, resistin, visfatin/NAMPT, omentin-1, vaspin, apelin, TNF-α, IL-6 and RBP4 levels in relation to hormonal changes during the menstrual cycle in young, healthy, normal-weight women. Methods: The study involved 52 young, healthy, normal-weight women. Anthropometric parameters, body composition and levels of plasma leptin, adiponectin, resistin, visfatin/NAMPT, omentin-1, vaspin, apelin, TNF-α, IL-6 and RBP4 in addition to serum FSH, LH, estradiol, progesterone, 17-OH progesterone, androgens, SHBG and insulin concentrations were measured during a morning in fasting state three times: between days 2–4, days 12–14 and days 24–26 of the menstrual cycle. Results: Plasma adiponectin, omentin-1, resistin and visfatin/NAMPT, apelin, TNF-α, IL-6 and RBP4 concentrations were stable during the menstrual cycle, while leptin and vaspin levels were significantly higher in both the midcycle and the luteal phases than those in the follicular phase. Multivariate regression analyses revealed that changes in leptin and vaspin levels between the follicular and the luteal phase are strongly related to changes in total testosterone levels. Conclusions: Our results revealed stable levels of adipokines during the phases of the physiological menstrual cycle, except for leptin and vaspin, which showed increased levels in both the midcycle and the luteal phases. This effect was significantly associated with changes in the secretion of testosterone, 17-OH progesterone and insulin in the luteal phase.

Determination of boiling point of petrochemicals by gas chromatography-mass spectrometry and multivariate regression analysis of structural activity relationship.

Science.gov (United States)

Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A

2014-08-01

Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries. Copyright © 2014 Elsevier B.V. All rights reserved.

Length bias correction in gene ontology enrichment analysis using logistic regression.

Science.gov (United States)

Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

2012-01-01

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

Analysis of designed experiments by stabilised PLS Regression and jack-knifing

DEFF Research Database (Denmark)

Martens, Harald; Høy, M.; Westad, F.

2001-01-01

Pragmatical, visually oriented methods for assessing and optimising bi-linear regression models are described, and applied to PLS Regression (PLSR) analysis of multi-response data from controlled experiments. The paper outlines some ways to stabilise the PLSR method to extend its range...... the reliability of the linear and bi-linear model parameter estimates. The paper illustrates how the obtained PLSR "significance" probabilities are similar to those from conventional factorial ANOVA, but the PLSR is shown to give important additional overview plots of the main relevant structures in the multi....... An Introduction, Wiley, Chichester, UK, 2001]....

Evaluation of Visual Field Progression in Glaucoma: Quasar Regression Program and Event Analysis.

Science.gov (United States)

Díaz-Alemán, Valentín T; González-Hernández, Marta; Perera-Sanz, Daniel; Armas-Domínguez, Karintia

2016-01-01

To determine the sensitivity, specificity and agreement between the Quasar program, glaucoma progression analysis (GPA II) event analysis and expert opinion in the detection of glaucomatous progression. The Quasar program is based on linear regression analysis of both mean defect (MD) and pattern standard deviation (PSD). Each series of visual fields was evaluated by three methods; Quasar, GPA II and four experts. The sensitivity, specificity and agreement (kappa) for each method was calculated, using expert opinion as the reference standard. The study included 439 SITA Standard visual fields of 56 eyes of 42 patients, with a mean of 7.8 ± 0.8 visual fields per eye. When suspected cases of progression were considered stable, sensitivity and specificity of Quasar, GPA II and the experts were 86.6% and 70.7%, 26.6% and 95.1%, and 86.6% and 92.6% respectively. When suspected cases of progression were considered as progressing, sensitivity and specificity of Quasar, GPA II and the experts were 79.1% and 81.2%, 45.8% and 90.6%, and 85.4% and 90.6% respectively. The agreement between Quasar and GPA II when suspected cases were considered stable or progressing was 0.03 and 0.28 respectively. The degree of agreement between Quasar and the experts when suspected cases were considered stable or progressing was 0.472 and 0.507. The degree of agreement between GPA II and the experts when suspected cases were considered stable or progressing was 0.262 and 0.342. The combination of MD and PSD regression analysis in the Quasar program showed better agreement with the experts and higher sensitivity than GPA II.

Replica analysis of overfitting in regression models for time-to-event data

Science.gov (United States)

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.

Retinol uptake and esterification in the rate sertoli cell

International Nuclear Information System (INIS)

Shingleton, J.L.

1989-01-01

The mechanism by which Sertoli cells accumulate retinol from retinol-binding protein (RBP) and the cellular metabolism of the accumulated retinol were investigated here using primary cultures of Sertoli cells isolated from 20 day-old rats. Cells incubated with [ 3 H]retinol-RBP accumulated [ 3 H]retinol in a time- and temperature dependent manner. The rate of [ 3 H] retinol accumulation declined when cellular [ 3 H] retinol concentrations reached approximately 0.53 pmol of retinol per μg of cellular DNA, equivalent to the cellular content of cellular retinol-binding protein (CRBP). Excess unlabeled retinol-RBP competed with [ 3 H] retinol-RBP for [ 3 H] retinol delivery to the cells but free retinol did not. Furthermore, free [ 3 H] retinol associated with Sertoli cells in a non-saturable manner. The transport constant for specific retinol accumulation from RBP was 1.9 μM, suggesting that any change in the normal circulating retinol-RBP level would directly affect the rate of retinol accumulation. Competition studies and studies using labeled RBP, cellular energy inhibitors, and lysosomal poisons indicated that the specific retinol accumulation by Sertoli cells occurs by interaction with a cell-surface receptor that internalizes retinol without concomitant internalization of RBP. Extraction and HPLC analysis of the radioactivity associated with Sertoli cells after incubation with [ 3 H] retinol-RBP yielded retinol and retinyl esters

Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis.

Science.gov (United States)

Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki

2017-05-01

This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Multiple linear regression analysis

Science.gov (United States)

Edwards, T. R.

1980-01-01

Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.

Multiple Regression Analysis of mRNA-miRNA Associations in Colorectal Cancer Pathway

Science.gov (United States)

Wang, Fengfeng; Wong, S. C. Cesar; Chan, Lawrence W. C.; Cho, William C. S.; Yip, S. P.; Yung, Benjamin Y. M.

2014-01-01

Background. MicroRNA (miRNA) is a short and endogenous RNA molecule that regulates posttranscriptional gene expression. It is an important factor for tumorigenesis of colorectal cancer (CRC), and a potential biomarker for diagnosis, prognosis, and therapy of CRC. Our objective is to identify the related miRNAs and their associations with genes frequently involved in CRC microsatellite instability (MSI) and chromosomal instability (CIN) signaling pathways. Results. A regression model was adopted to identify the significantly associated miRNAs targeting a set of candidate genes frequently involved in colorectal cancer MSI and CIN pathways. Multiple linear regression analysis was used to construct the model and find the significant mRNA-miRNA associations. We identified three significantly associated mRNA-miRNA pairs: BCL2 was positively associated with miR-16 and SMAD4 was positively associated with miR-567 in the CRC tissue, while MSH6 was positively associated with miR-142-5p in the normal tissue. As for the whole model, BCL2 and SMAD4 models were not significant, and MSH6 model was significant. The significant associations were different in the normal and the CRC tissues. Conclusion. Our results have laid down a solid foundation in exploration of novel CRC mechanisms, and identification of miRNA roles as oncomirs or tumor suppressor mirs in CRC. PMID:24895601

Laser-induced Breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

International Nuclear Information System (INIS)

Yang, Jianhong; Yi, Cancan; Xu, Jinwu; Ma, Xianghong

2015-01-01

A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine. - Highlights: • Both training and testing samples are considered for analytical lines selection. • The analytical lines are auto-selected based on the built-in characteristics of spectral lines. • The new method can achieve better prediction accuracy and modeling robustness. • Model predictions are given with confidence interval of probabilistic distribution

Application of range-test in multiple linear regression analysis in ...

African Journals Online (AJOL)

Application of range-test in multiple linear regression analysis in the presence of outliers is studied in this paper. First, the plot of the explanatory variables (i.e. Administration, Social/Commercial, Economic services and Transfer) on the dependent variable (i.e. GDP) was done to identify the statistical trend over the years.

Multiple regression analysis of anthropometric measurements influencing the cephalic index of male Japanese university students.

Science.gov (United States)

Hossain, Md Golam; Saw, Aik; Alam, Rashidul; Ohtsuki, Fumio; Kamarul, Tunku

2013-09-01

Cephalic index (CI), the ratio of head breadth to head length, is widely used to categorise human populations. The aim of this study was to access the impact of anthropometric measurements on the CI of male Japanese university students. This study included 1,215 male university students from Tokyo and Kyoto, selected using convenient sampling. Multiple regression analysis was used to determine the effect of anthropometric measurements on CI. The variance inflation factor (VIF) showed no evidence of a multicollinearity problem among independent variables. The coefficients of the regression line demonstrated a significant positive relationship between CI and minimum frontal breadth (p regression analysis showed a greater likelihood for minimum frontal breadth (p regression analysis revealed bizygomatic breadth, head circumference, minimum frontal breadth, head height and morphological facial height to be the best predictor craniofacial measurements with respect to CI. The results suggest that most of the variables considered in this study appear to influence the CI of adult male Japanese students.

Prevalence of rapid eye movement sleep behavior disorder (RBD) in Parkinson's disease: a meta and meta-regression analysis.

Science.gov (United States)

Zhang, Xiaona; Sun, Xiaoxuan; Wang, Junhong; Tang, Liou; Xie, Anmu

2017-01-01

Rapid eye movement sleep behavior disorder (RBD) is thought to be one of the most frequent preceding symptoms of Parkinson's disease (PD). However, the prevalence of RBD in PD stated in the published studies is still inconsistent. We conducted a meta and meta-regression analysis in this paper to estimate the pooled prevalence. We searched the electronic databases of PubMed, ScienceDirect, EMBASE and EBSCO up to June 2016 for related articles. STATA 12.0 statistics software was used to calculate the available data from each research. The prevalence of RBD in PD patients in each study was combined to a pooled prevalence with a 95 % confidence interval (CI). Subgroup analysis and meta-regression analysis were performed to search for the causes of the heterogeneity. A total of 28 studies with 6869 PD cases were deemed eligible and included in our meta-analysis based on the inclusion and exclusion criteria. The pooled prevalence of RBD in PD was 42.3 % (95 % CI 37.4-47.1 %). In subgroup analysis and meta-regression analysis, we found that the important causes of heterogeneity were the diagnosis criteria of RBD and age of PD patients (P = 0.016, P = 0.019, respectively). The results indicate that nearly half of the PD patients are suffering from RBD. Older age and longer duration are risk factors for RBD in PD. We can use the minimal diagnosis criteria for RBD according to the International Classification of Sleep Disorders to diagnose RBD patients in our daily work if polysomnography is not necessary.

Understanding poisson regression.

Science.gov (United States)

Hayat, Matthew J; Higgins, Melinda

2014-04-01

Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

Analysis of the influence of quantile regression model on mainland tourists' service satisfaction performance.

Science.gov (United States)

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.

Analysis of the Influence of Quantile Regression Model on Mainland Tourists' Service Satisfaction Performance

Science.gov (United States)

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

Analysis of the Influence of Quantile Regression Model on Mainland Tourists’ Service Satisfaction Performance

Directory of Open Access Journals (Sweden)

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.

Econometric analysis of realised covariation: high frequency covariance, regression and correlation in financial economics

OpenAIRE

Ole E. Barndorff-Nielsen; Neil Shephard

2002-01-01

This paper analyses multivariate high frequency financial data using realised covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis and covariance. It will be based on a fixed interval of time (e.g. a day or week), allowing the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions and covariances change through time. In particular w...

Prediction of radiation levels in residences: A methodological comparison of CART [Classification and Regression Tree Analysis] and conventional regression

International Nuclear Information System (INIS)

Janssen, I.; Stebbings, J.H.

1990-01-01

In environmental epidemiology, trace and toxic substance concentrations frequently have very highly skewed distributions ranging over one or more orders of magnitude, and prediction by conventional regression is often poor. Classification and Regression Tree Analysis (CART) is an alternative in such contexts. To compare the techniques, two Pennsylvania data sets and three independent variables are used: house radon progeny (RnD) and gamma levels as predicted by construction characteristics in 1330 houses; and ∼200 house radon (Rn) measurements as predicted by topographic parameters. CART may identify structural variables of interest not identified by conventional regression, and vice versa, but in general the regression models are similar. CART has major advantages in dealing with other common characteristics of environmental data sets, such as missing values, continuous variables requiring transformations, and large sets of potential independent variables. CART is most useful in the identification and screening of independent variables, greatly reducing the need for cross-tabulations and nested breakdown analyses. There is no need to discard cases with missing values for the independent variables because surrogate variables are intrinsic to CART. The tree-structured approach is also independent of the scale on which the independent variables are measured, so that transformations are unnecessary. CART identifies important interactions as well as main effects. The major advantages of CART appear to be in exploring data. Once the important variables are identified, conventional regressions seem to lead to results similar but more interpretable by most audiences. 12 refs., 8 figs., 10 tabs

A rotor optimization using regression analysis

Science.gov (United States)

Giansante, N.

1984-01-01

The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.

Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression

Energy Technology Data Exchange (ETDEWEB)

Verdoolaege, G., E-mail: geert.verdoolaege@ugent.be [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Laboratory for Plasma Physics, Royal Military Academy, B-1000 Brussels (Belgium); Shabbir, A. [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Max Planck Institute for Plasma Physics, Boltzmannstr. 2, 85748 Garching (Germany); Hornung, G. [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium)

2016-11-15

Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standard least squares.

Meta-regression analysis of commensal and pathogenic Escherichia coli survival in soil and water.

Science.gov (United States)

Franz, Eelco; Schijven, Jack; de Roda Husman, Ana Maria; Blaak, Hetty

2014-06-17

The extent to which pathogenic and commensal E. coli (respectively PEC and CEC) can survive, and which factors predominantly determine the rate of decline, are crucial issues from a public health point of view. The goal of this study was to provide a quantitative summary of the variability in E. coli survival in soil and water over a broad range of individual studies and to identify the most important sources of variability. To that end, a meta-regression analysis on available literature data was conducted. The considerable variation in reported decline rates indicated that the persistence of E. coli is not easily predictable. The meta-analysis demonstrated that for soil and water, the type of experiment (laboratory or field), the matrix subtype (type of water and soil), and temperature were the main factors included in the regression analysis. A higher average decline rate in soil of PEC compared with CEC was observed. The regression models explained at best 57% of the variation in decline rate in soil and 41% of the variation in decline rate in water. This indicates that additional factors, not included in the current meta-regression analysis, are of importance but rarely reported. More complete reporting of experimental conditions may allow future inference on the global effects of these variables on the decline rate of E. coli.

Vector regression introduced

Directory of Open Access Journals (Sweden)

Mok Tik

2014-06-01

Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

A modified parallel constitutive model for elevated temperature flow behavior of Ti-6Al-4V alloy based on multiple regression

Energy Technology Data Exchange (ETDEWEB)

Cai, Jun; Shi, Jiamin; Wang, Kuaishe; Wang, Wen; Wang, Qingjuan; Liu, Yingying [Xi' an Univ. of Architecture and Technology, Xi' an (China). School of Metallurgical Engineering; Li, Fuguo [Northwestern Polytechnical Univ., Xi' an (China). School of Materials Science and Engineering

2017-07-15

Constitutive analysis for hot working of Ti-6Al-4V alloy was carried out by using experimental stress-strain data from isothermal hot compression tests. A new kind of constitutive equation called a modified parallel constitutive model was proposed by considering the independent effects of strain, strain rate and temperature. The predicted flow stress data were compared with the experimental data. Statistical analysis was introduced to verify the validity of the developed constitutive equation. Subsequently, the accuracy of the proposed constitutive equations was evaluated by comparing with other constitutive models. The results showed that the developed modified parallel constitutive model based on multiple regression could predict flow stress of Ti-6Al-4V alloy with good correlation and generalization.

Repeated Results Analysis for Middleware Regression Benchmarking

Czech Academy of Sciences Publication Activity Database

Bulej, Lubomír; Kalibera, T.; Tůma, P.

2005-01-01

Roč. 60, - (2005), s. 345-358 ISSN 0166-5316 R&D Projects: GA ČR GA102/03/0672 Institutional research plan: CEZ:AV0Z10300504 Keywords : middleware benchmarking * regression benchmarking * regression testing Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.756, year: 2005

Bayesian Analysis for Penalized Spline Regression Using WinBUGS

Directory of Open Access Journals (Sweden)

Ciprian M. Crainiceanu

2005-09-01

Full Text Available Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.

Forecasting municipal solid waste generation using prognostic tools and regression analysis.

Science.gov (United States)

Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria

2016-11-01

For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

Development of Compressive Failure Strength for Composite Laminate Using Regression Analysis Method

Energy Technology Data Exchange (ETDEWEB)

Lee, Myoung Keon [Agency for Defense Development, Daejeon (Korea, Republic of); Lee, Jeong Won; Yoon, Dong Hyun; Kim, Jae Hoon [Chungnam Nat’l Univ., Daejeon (Korea, Republic of)

2016-10-15

This paper provides the compressive failure strength value of composite laminate developed by using regression analysis method. Composite material in this document is a Carbon/Epoxy unidirection(UD) tape prepreg(Cycom G40-800/5276-1) cured at 350°F(177°C). The operating temperature is –60°F~+200°F(-55°C - +95°C). A total of 56 compression tests were conducted on specimens from eight (8) distinct laminates that were laid up by standard angle layers (0°, +45°, –45° and 90°). The ASTM-D-6484 standard was used for test method. The regression analysis was performed with the response variable being the laminate ultimate fracture strength and the regressor variables being two ply orientations (0° and ±45°)

Development of Compressive Failure Strength for Composite Laminate Using Regression Analysis Method

International Nuclear Information System (INIS)

Lee, Myoung Keon; Lee, Jeong Won; Yoon, Dong Hyun; Kim, Jae Hoon

2016-01-01

This paper provides the compressive failure strength value of composite laminate developed by using regression analysis method. Composite material in this document is a Carbon/Epoxy unidirection(UD) tape prepreg(Cycom G40-800/5276-1) cured at 350°F(177°C). The operating temperature is –60°F~+200°F(-55°C - +95°C). A total of 56 compression tests were conducted on specimens from eight (8) distinct laminates that were laid up by standard angle layers (0°, +45°, –45° and 90°). The ASTM-D-6484 standard was used for test method. The regression analysis was performed with the response variable being the laminate ultimate fracture strength and the regressor variables being two ply orientations (0° and ±45°)

Standards for Standardized Logistic Regression Coefficients

Science.gov (United States)

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

Cell type-specific translational repression of Cyclin B during meiosis in males.

Science.gov (United States)

Baker, Catherine Craig; Gim, Byung Soo; Fuller, Margaret T

2015-10-01

The unique cell cycle dynamics of meiosis are controlled by layers of regulation imposed on core mitotic cell cycle machinery components by the program of germ cell development. Although the mechanisms that regulate Cdk1/Cyclin B activity in meiosis in oocytes have been well studied, little is known about the trans-acting factors responsible for developmental control of these factors in male gametogenesis. During meiotic prophase in Drosophila males, transcript for the core cell cycle protein Cyclin B1 (CycB) is expressed in spermatocytes, but the protein does not accumulate in spermatocytes until just before the meiotic divisions. Here, we show that two interacting proteins, Rbp4 and Fest, expressed at the onset of spermatocyte differentiation under control of the developmental program of male gametogenesis, function to direct cell type- and stage-specific repression of translation of the core G2/M cell cycle component cycB during the specialized cell cycle of male meiosis. Binding of Fest to Rbp4 requires a 31-amino acid region within Rbp4. Rbp4 and Fest are required for translational repression of cycB in immature spermatocytes, with Rbp4 binding sequences in a cell type-specific shortened form of the cycB 3' UTR. Finally, we show that Fest is required for proper execution of meiosis I. © 2015. Published by The Company of Biologists Ltd.

Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree analysis.

Science.gov (United States)

Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H

2016-01-01

Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.

Exergy Analysis of a Subcritical Reheat Steam Power Plant with Regression Modeling and Optimization

Directory of Open Access Journals (Sweden)

MUHIB ALI RAJPER

2016-07-01

Full Text Available In this paper, exergy analysis of a 210 MW SPP (Steam Power Plant is performed. Firstly, the plant is modeled and validated, followed by a parametric study to show the effects of various operating parameters on the performance parameters. The net power output, energy efficiency, and exergy efficiency are taken as the performance parameters, while the condenser pressure, main steam pressure, bled steam pressures, main steam temperature, and reheat steam temperature isnominated as the operating parameters. Moreover, multiple polynomial regression models are developed to correlate each performance parameter with the operating parameters. The performance is then optimizedby using Direct-searchmethod. According to the results, the net power output, energy efficiency, and exergy efficiency are calculated as 186.5 MW, 31.37 and 30.41%, respectively under normal operating conditions as a base case. The condenser is a major contributor towards the energy loss, followed by the boiler, whereas the highest irreversibilities occur in the boiler and turbine. According to the parametric study, variation in the operating parameters greatly influences the performance parameters. The regression models have appeared to be a good estimator of the performance parameters. The optimum net power output, energy efficiency and exergy efficiency are obtained as 227.6 MW, 37.4 and 36.4, respectively, which have been calculated along with optimal values of selected operating parameters.

Inferring gene expression dynamics via functional regression analysis

Directory of Open Access Journals (Sweden)

Leng Xiaoyan

2008-01-01

Full Text Available Abstract Background Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other. Results We demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles. Conclusion Our findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.

Diagnostic accuracy of atypical p-ANCA in autoimmune hepatitis using ROC- and multivariate regression analysis.

Science.gov (United States)

Terjung, B; Bogsch, F; Klein, R; Söhne, J; Reichel, C; Wasmuth, J-C; Beuers, U; Sauerbruch, T; Spengler, U

2004-09-29

Antineutrophil cytoplasmic antibodies (atypical p-ANCA) are detected at high prevalence in sera from patients with autoimmune hepatitis (AIH), but their diagnostic relevance for AIH has not been systematically evaluated so far. Here, we studied sera from 357 patients with autoimmune (autoimmune hepatitis n=175, primary sclerosing cholangitis (PSC) n=35, primary biliary cirrhosis n=45), non-autoimmune chronic liver disease (alcoholic liver cirrhosis n=62; chronic hepatitis C virus infection (HCV) n=21) or healthy controls (n=19) for the presence of various non-organ specific autoantibodies. Atypical p-ANCA, antinuclear antibodies (ANA), antibodies against smooth muscles (SMA), antibodies against liver/kidney microsomes (anti-Lkm1) and antimitochondrial antibodies (AMA) were detected by indirect immunofluorescence microscopy, antibodies against the M2 antigen (anti-M2), antibodies against soluble liver antigen (anti-SLA/LP) and anti-Lkm1 by using enzyme linked immunosorbent assays. To define the diagnostic precision of the autoantibodies, results of autoantibody testing were analyzed by receiver operating characteristics (ROC) and forward conditional logistic regression analysis. Atypical p-ANCA were detected at high prevalence in sera from patients with AIH (81%) and PSC (94%). ROC- and logistic regression analysis revealed atypical p-ANCA and SMA, but not ANA as significant diagnostic seromarkers for AIH (atypical p-ANCA: AUC 0.754+/-0.026, odds ratio [OR] 3.4; SMA: 0.652+/-0.028, OR 4.1). Atypical p-ANCA also emerged as the only diagnostically relevant seromarker for PSC (AUC 0.690+/-0.04, OR 3.4). None of the tested antibodies yielded a significant diagnostic accuracy for patients with alcoholic liver cirrhosis, HCV or healthy controls. Atypical p-ANCA along with SMA represent a seromarker with high diagnostic accuracy for AIH and should be explicitly considered in a revised version of the diagnostic score for AIH.

Survival analysis II: Cox regression

NARCIS (Netherlands)

Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.

2011-01-01

In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the

Use of generalized ordered logistic regression for the analysis of multidrug resistance data.

Science.gov (United States)

Agga, Getahun E; Scott, H Morgan

2015-10-01

Statistical analysis of antimicrobial resistance data largely focuses on individual antimicrobial's binary outcome (susceptible or resistant). However, bacteria are becoming increasingly multidrug resistant (MDR). Statistical analysis of MDR data is mostly descriptive often with tabular or graphical presentations. Here we report the applicability of generalized ordinal logistic regression model for the analysis of MDR data. A total of 1,152 Escherichia coli, isolated from the feces of weaned pigs experimentally supplemented with chlortetracycline (CTC) and copper, were tested for susceptibilities against 15 antimicrobials and were binary classified into resistant or susceptible. The 15 antimicrobial agents tested were grouped into eight different antimicrobial classes. We defined MDR as the number of antimicrobial classes to which E. coli isolates were resistant ranging from 0 to 8. Proportionality of the odds assumption of the ordinal logistic regression model was violated only for the effect of treatment period (pre-treatment, during-treatment and post-treatment); but not for the effect of CTC or copper supplementation. Subsequently, a partially constrained generalized ordinal logistic model was built that allows for the effect of treatment period to vary while constraining the effects of treatment (CTC and copper supplementation) to be constant across the levels of MDR classes. Copper (Proportional Odds Ratio [Prop OR]=1.03; 95% CI=0.73-1.47) and CTC (Prop OR=1.1; 95% CI=0.78-1.56) supplementation were not significantly associated with the level of MDR adjusted for the effect of treatment period. MDR generally declined over the trial period. In conclusion, generalized ordered logistic regression can be used for the analysis of ordinal data such as MDR data when the proportionality assumptions for ordered logistic regression are violated. Published by Elsevier B.V.

Regression analysis of growth responses to water depth in three wetland plant species

DEFF Research Database (Denmark)

Sorrell, Brian K; Tanner, Chris C; Brix, Hans

2012-01-01

depths from 0 – 0.5 m. Morphological and growth responses to depth were followed for 54 days before harvest, and then analysed by repeated measures analysis of covariance, and non-linear and quantile regression analysis (QRA), to compare flooding tolerances. Principal results Growth responses to depth...

A SOCIOLOGICAL ANALYSIS OF THE CHILDBEARING COEFFICIENT IN THE ALTAI REGION BASED ON METHOD OF FUZZY LINEAR REGRESSION

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Sergei Vladimirovich Varaksin

2017-06-01

Full Text Available Purpose. Construction of a mathematical model of the dynamics of childbearing change in the Altai region in 2000–2016, analysis of the dynamics of changes in birth rates for multiple age categories of women of childbearing age. Methodology. A auxiliary analysis element is the construction of linear mathematical models of the dynamics of childbearing by using fuzzy linear regression method based on fuzzy numbers. Fuzzy linear regression is considered as an alternative to standard statistical linear regression for short time series and unknown distribution law. The parameters of fuzzy linear and standard statistical regressions for childbearing time series were defined with using the built in language MatLab algorithm. Method of fuzzy linear regression is not used in sociological researches yet. Results. There are made the conclusions about the socio-demographic changes in society, the high efficiency of the demographic policy of the leadership of the region and the country, and the applicability of the method of fuzzy linear regression for sociological analysis.

Multiple Logistic Regression Analysis of Cigarette Use among High School Students

Science.gov (United States)

Adwere-Boamah, Joseph

2011-01-01

A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…

THE PROGNOSIS OF RUSSIAN DEFENSE INDUSTRY DEVELOPMENT IMPLEMENTED THROUGH REGRESSION ANALYSIS

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L.M. Kapustina

2007-03-01

Full Text Available The article illustrates the results of investigation the major internal and external factors which influence the development of the defense industry, as well as the results of regression analysis which quantitatively displays the factorial contribution in the growth rate of Russian defense industry. On the basis of calculated regression dependences the authors fulfilled the medium-term prognosis of defense industry. Optimistic and inertial versions of defense product growth rate for the period up to 2009 are based on scenario conditions in Russian economy worked out by the Ministry of economy and development. In conclusion authors point out which factors and conditions have the largest impact on successful and stable operation of Russian defense industry.

Applied linear regression

CERN Document Server

Weisberg, Sanford

2013-01-01

Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus

Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation

Science.gov (United States)

Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa

2011-08-01

In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.

Multiple Regression Analysis of Unconfined Compression Strength of Mine Tailings Matrices

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Mahmood Ali A.

2017-01-01

Full Text Available As part of a novel approach of sustainable development of mine tailings, experimental and numerical analysis is carried out on newly formulated tailings matrices. Several physical characteristic tests are carried out including the unconfined compression strength test to ascertain the integrity of these matrices when subjected to loading. The current paper attempts a multiple regression analysis of the unconfined compressive strength test results of these matrices to investigate the most pertinent factors affecting their strength. Results of this analysis showed that the suggested equation is reasonably applicable to the range of binder combinations used.

The Regression Analysis of Individual Financial Performance: Evidence from Croatia

OpenAIRE

Bahovec, Vlasta; Barbić, Dajana; Palić, Irena

2017-01-01

Background: A large body of empirical literature indicates that gender and financial literacy are significant determinants of individual financial performance. Objectives: The purpose of this paper is to recognize the impact of the variable financial literacy and the variable gender on the variation of the financial performance using the regression analysis. Methods/Approach: The survey was conducted using the systematically chosen random sample of Croatian financial consumers. The cross sect...

Relationship between Serum 25-Hydroxyvitamin D and Lower Extremity Arterial Disease in Type 2 Diabetes Mellitus Patients and the Analysis of the Intervention of Vitamin D

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Wan Zhou

2015-01-01

Full Text Available The aim of this study was to explore the relationship between serum 25-hydroxyvitamin D [25(OHD] concentrations and lower extremity arterial disease (LEAD in type 2 diabetes mellitus (T2DM patients and to investigate the intervention effect of vitamin D. 145 subjects were assigned to a control group (Group NC, T2DM group (Group DM1, and T2DM complicated with LEAD group (Group DM2; then Group DM2 were randomly divided into Group DM3 who received oral hypoglycemic agents and Group DM4 who received oral hypoglycemic drugs and vitamin D3 therapy. Compared to Group NC, 25(OHD was significantly lower in Group DM2 and marginally lower in Group DM1. In contrast to baseline and Group DM3, 25(OHD rose while low density lipoprotein (LDL, retinol binding protein 4 (RBP4, and HbA1c significantly lowered in Group DM4. Statistical analysis revealed that 25(OHD had a negative correlation with RBP4, duration, HbA1c, homeostasis model assessment for insulin resistance (HOMA-IR, and fasting plasma glucose (FPG. LDL, systolic blood pressure (SBP, FPG, and smoking were risk factors of LEAD while high density lipoprotein (HDL and 25(OHD were protective ones. Therefore, we deduced that low level of 25(OHD is significantly associated with the occurrence of T2DM complicated with LEAD.

A systematic review and meta-regression analysis of mivacurium for tracheal intubation

NARCIS (Netherlands)

Vanlinthout, L.E.H.; Mesfin, S.H.; Hens, N.; Vanacker, B.F.; Robertson, E.N.; Booij, L.H.D.J.

2014-01-01

We systematically reviewed factors associated with intubation conditions in randomised controlled trials of mivacurium, using random-effects meta-regression analysis. We included 29 studies of 1050 healthy participants. Four factors explained 72.9% of the variation in the probability of excellent

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.

Science.gov (United States)

van Smeden, Maarten; de Groot, Joris A H; Moons, Karel G M; Collins, Gary S; Altman, Douglas G; Eijkemans, Marinus J C; Reitsma, Johannes B

2016-11-24

Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth's correction, are compared. The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect ('separation'). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth's correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

Latent Variable Regression 4-Level Hierarchical Model Using Multisite Multiple-Cohorts Longitudinal Data. CRESST Report 801

Science.gov (United States)

Choi, Kilchan

2011-01-01

This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…

Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis.

Science.gov (United States)

Khalil, Mohamed H; Shebl, Mostafa K; Kosba, Mohamed A; El-Sabrout, Karim; Zaki, Nesma

2016-08-01

This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens' eggs. Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens.

Logistic Regression: Concept and Application

Science.gov (United States)

Cokluk, Omay

2010-01-01

The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

Personality disorders, violence, and antisocial behavior: a systematic review and meta-regression analysis.

Science.gov (United States)

Yu, Rongqin; Geddes, John R; Fazel, Seena

2012-10-01

The risk of antisocial outcomes in individuals with personality disorder (PD) remains uncertain. The authors synthesize the current evidence on the risks of antisocial behavior, violence, and repeat offending in PD, and they explore sources of heterogeneity in risk estimates through a systematic review and meta-regression analysis of observational studies comparing antisocial outcomes in personality disordered individuals with controls groups. Fourteen studies examined risk of antisocial and violent behavior in 10,007 individuals with PD, compared with over 12 million general population controls. There was a substantially increased risk of violent outcomes in studies with all PDs (random-effects pooled odds ratio [OR] = 3.0, 95% CI = 2.6 to 3.5). Meta-regression revealed that antisocial PD and gender were associated with higher risks (p = .01 and .07, respectively). The odds of all antisocial outcomes were also elevated. Twenty-five studies reported the risk of repeat offending in PD compared with other offenders. The risk of a repeat offense was also increased (fixed-effects pooled OR = 2.4, 95% CI = 2.2 to 2.7) in offenders with PD. The authors conclude that although PD is associated with antisocial outcomes and repeat offending, the risk appears to differ by PD category, gender, and whether individuals are offenders or not.

A multiple regression analysis for accurate background subtraction in 99Tcm-DTPA renography

International Nuclear Information System (INIS)

Middleton, G.W.; Thomson, W.H.; Davies, I.H.; Morgan, A.

1989-01-01

A technique for accurate background subtraction in 99 Tc m -DTPA renography is described. The technique is based on a multiple regression analysis of the renal curves and separate heart and soft tissue curves which together represent background activity. It is compared, in over 100 renograms, with a previously described linear regression technique. Results show that the method provides accurate background subtraction, even in very poorly functioning kidneys, thus enabling relative renal filtration and excretion to be accurately estimated. (author)

Relationship between the curve of Spee and craniofacial variables: A regression analysis.

Science.gov (United States)

Halimi, Abdelali; Benyahia, Hicham; Azeroual, Mohamed-Faouzi; Bahije, Loubna; Zaoui, Fatima

2018-06-01

observed in the hyperdivergent population included in this study. For the multivariate analysis, the overbite and the sellion-articulare distance remained independently related to the curve of Spee according to the breathing type, Angle's classification, and overjet. This regression model explains 21.4% of the changes in the curve of Spee. Copyright © 2018. Published by Elsevier Masson SAS.

Repeated Exposure to Neurotoxic Levels of Chlorpyriphos Alters Hippocampal Expression of Neurotrophins and Neuropeptides

Science.gov (United States)

2016-01-13

hormone bindi Bdnf BDNF Brain-derived neurotrophic factor Mdk MDK Midkine (neurite growth -promoting fa Rbp4 RBP4 Retinol binding protein 4, plasma...cause cholinergic crisis are associated with problems in cognitive function (i.e., learning and memory deficits ), but the biological mechanism(s...neurobehavioral deficits following subchronic exposure to CPF at a level that inhibits hippocampal cholinesterase to less than 20% of control. An equally

Development of an empirical model of turbine efficiency using the Taylor expansion and regression analysis

International Nuclear Information System (INIS)

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.

Econometric analysis of realized covariation: high frequency based covariance, regression, and correlation in financial economics

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Shephard, N.

2004-01-01

This paper analyses multivariate high frequency financial data using realized covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis, and covariance. It will be based on a fixed interval of time (e.g., a day or week), allowing...... the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions, and covariances change through time. In particular we provide confidence intervals for each of these quantities....

Health care: necessity or luxury good? A meta-regression analysis

OpenAIRE

Iordache, Ioana Raluca

2014-01-01

When estimating the influence income per capita exerts on health care expenditure, the research in the field offers mixed results. Studies employ different data, estimation techniques and models, which brings about the question whether these differences in research design play any part in explaining the heterogeneity of reported outcomes. By employing meta-regression analysis, the present paper analyzes 220 estimates of health spending income elasticity collected from 54 studies and finds tha...

Detection of Cutting Tool Wear using Statistical Analysis and Regression Model

Science.gov (United States)

Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin

2010-10-01

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.

Distance Based Root Cause Analysis and Change Impact Analysis of Performance Regressions

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Junzan Zhou

2015-01-01

Full Text Available Performance regression testing is applied to uncover both performance and functional problems of software releases. A performance problem revealed by performance testing can be high response time, low throughput, or even being out of service. Mature performance testing process helps systematically detect software performance problems. However, it is difficult to identify the root cause and evaluate the potential change impact. In this paper, we present an approach leveraging server side logs for identifying root causes of performance problems. Firstly, server side logs are used to recover call tree of each business transaction. We define a novel distance based metric computed from call trees for root cause analysis and apply inverted index from methods to business transactions for change impact analysis. Empirical studies show that our approach can effectively and efficiently help developers diagnose root cause of performance problems.

The thermodynamic dissociation constants of losartan, paracetamol, phenylephrine and quinine by the regression analysis of spectrophotometric data

International Nuclear Information System (INIS)

Meloun, Milan; Syrovy, Tomas; Vrana, Ales

2005-01-01

The mixed dissociation constants of four drug acids - losartan, paracetamol, phenylephrine and quinine - at various ionic strengths I of range 0.01 and 1.0 and at temperatures of 25 and 37 deg. C were determined using SPECFIT32 and SQUAD(84) regression analysis of the pH-spectrophotometric titration data. A proposed strategy of efficient experimentation in a dissociation constants determination, followed by a computational strategy for the chemical model with a dissociation constants determination, is presented on the protonation equilibria of losartan. Indices of precise methods predict the correct number of components, and even the presence of minor ones when the data quality is high and the instrumental error is known. Improved identification of the number of species uses the second or third derivative function for some indices, namely when the number of species in the mixture is higher than 3 and when, due to large variations in the indicator values even at logarithmic scale, the indicator curve does not reach an obvious point where the slope changes. The thermodynamic dissociation constant pKaT was estimated by nonlinear regression of {pK a , I} data at 25 and 37 deg. C: for losartan pKa,1T=3.63(1) and 3.57(3), pKa,2T=4.84(1) and 4.80(3), for paracetamol pKa,1T=9.78(1) and 9.65(1), for phenylephrine pKa,1T=9.17(1) and 8.95(1), pKa,2T=10.45(1) and 10.22(1), for quinine pKa,1T=4.25(1) and 4.12(1), pKa,2T=8.72(1) and 8.46(2). Goodness-of-fit tests for various regression diagnostics enabled the reliability of the parameter estimates to be found

The thermodynamic dissociation constants of losartan, paracetamol, phenylephrine and quinine by the regression analysis of spectrophotometric data

Energy Technology Data Exchange (ETDEWEB)

Meloun, Milan [Department of Analytical Chemistry, University of Pardubice, 53210 Pardubice (Czech Republic)]. E-mail: milan.meloun@upce.cz; Syrovy, Tomas [Department of Analytical Chemistry, University of Pardubice, 53210 Pardubice (Czech Republic)]. E-mail: tomas.syrovy@upce.cz; Vrana, Ales [IVAX Pharmaceuticals, s.r.o. 74770 Opava (Czech Republic)]. E-mail: ales_vrana@ivax-cr.com

2005-03-21

The mixed dissociation constants of four drug acids - losartan, paracetamol, phenylephrine and quinine - at various ionic strengths I of range 0.01 and 1.0 and at temperatures of 25 and 37 deg. C were determined using SPECFIT32 and SQUAD(84) regression analysis of the pH-spectrophotometric titration data. A proposed strategy of efficient experimentation in a dissociation constants determination, followed by a computational strategy for the chemical model with a dissociation constants determination, is presented on the protonation equilibria of losartan. Indices of precise methods predict the correct number of components, and even the presence of minor ones when the data quality is high and the instrumental error is known. Improved identification of the number of species uses the second or third derivative function for some indices, namely when the number of species in the mixture is higher than 3 and when, due to large variations in the indicator values even at logarithmic scale, the indicator curve does not reach an obvious point where the slope changes. The thermodynamic dissociation constant pKaT was estimated by nonlinear regression of {l_brace}pK{sub a}, I{r_brace} data at 25 and 37 deg. C: for losartan pKa,1T=3.63(1) and 3.57(3), pKa,2T=4.84(1) and 4.80(3), for paracetamol pKa,1T=9.78(1) and 9.65(1), for phenylephrine pKa,1T=9.17(1) and 8.95(1), pKa,2T=10.45(1) and 10.22(1), for quinine pKa,1T=4.25(1) and 4.12(1), pKa,2T=8.72(1) and 8.46(2). Goodness-of-fit tests for various regression diagnostics enabled the reliability of the parameter estimates to be found.

Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

Science.gov (United States)

Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

Advanced statistics: linear regression, part II: multiple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Translating Response During Therapy into Ultimate Treatment Outcome: A Personalized 4-Dimensional MRI Tumor Volumetric Regression Approach in Cervical Cancer

International Nuclear Information System (INIS)

Mayr, Nina A.; Wang, Jian Z.; Lo, Simon S.; Zhang Dongqing; Grecula, John C.; Lu Lanchun; Montebello, Joseph F.; Fowler, Jeffrey M.; Yuh, William T.C.

2010-01-01

Purpose: To assess individual volumetric tumor regression pattern in cervical cancer during therapy using serial four-dimensional MRI and to define the regression parameters' prognostic value validated with local control and survival correlation. Methods and Materials: One hundred and fifteen patients with Stage IB 2 -IVA cervical cancer treated with radiation therapy (RT) underwent serial MRI before (MRI 1) and during RT, at 2-2.5 weeks (MRI 2, at 20-25 Gy), and at 4-5 weeks (MRI 3, at 40-50 Gy). Eighty patients had a fourth MRI 1-2 months post-RT. Mean follow-up was 5.3 years. Tumor volume was measured by MRI-based three-dimensional volumetry, and plotted as dose(time)/volume regression curves. Volume regression parameters were correlated with local control, disease-specific, and overall survival. Results: Residual tumor volume, slope, and area under the regression curve correlated significantly with local control and survival. Residual volumes ≥20% at 40-50 Gy were independently associated with inferior 5-year local control (53% vs. 97%, p <0.001) and disease-specific survival rates (50% vs. 72%, p = 0.009) than smaller volumes. Patients with post-RT residual volumes ≥10% had 0% local control and 17% disease-specific survival, compared with 91% and 72% for <10% volume (p <0.001). Conclusion: Using more accurate four-dimensional volumetric regression analysis, tumor response can now be directly translated into individual patients' outcome for clinical application. Our results define two temporal thresholds critically influencing local control and survival. In patients with ≥20% residual volume at 40-50 Gy and ≥10% post-RT, the risk for local failure and death are so high that aggressive intervention may be warranted.

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis

Directory of Open Access Journals (Sweden)

Maarten van Smeden

2016-11-01

Full Text Available Abstract Background Ten events per variable (EPV is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. Methods The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth’s correction, are compared. Results The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect (‘separation’. We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth’s correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. Conclusions The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu

2015-01-01

predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

Science.gov (United States)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

The Effect of Sitagliptin on the Regression of Carotid Intima-Media Thickening in Patients with Type 2 Diabetes Mellitus: A Post Hoc Analysis of the Sitagliptin Preventive Study of Intima-Media Thickness Evaluation

Directory of Open Access Journals (Sweden)

Tomoya Mita

2017-01-01

Full Text Available Background. The effect of dipeptidyl peptidase-4 (DPP-4 inhibitors on the regression of carotid IMT remains largely unknown. The present study aimed to clarify whether sitagliptin, DPP-4 inhibitor, could regress carotid intima-media thickness (IMT in insulin-treated patients with type 2 diabetes mellitus (T2DM. Methods. This is an exploratory analysis of a randomized trial in which we investigated the effect of sitagliptin on the progression of carotid IMT in insulin-treated patients with T2DM. Here, we compared the efficacy of sitagliptin treatment on the number of patients who showed regression of carotid IMT of ≥0.10 mm in a post hoc analysis. Results. The percentages of the number of the patients who showed regression of mean-IMT-CCA (28.9% in the sitagliptin group versus 16.4% in the conventional group, P = 0.022 and left max-IMT-CCA (43.0% in the sitagliptin group versus 26.2% in the conventional group, P = 0.007, but not right max-IMT-CCA, were higher in the sitagliptin treatment group compared with those in the non-DPP-4 inhibitor treatment group. In multiple logistic regression analysis, sitagliptin treatment significantly achieved higher target attainment of mean-IMT-CCA ≥0.10 mm and right and left max-IMT-CCA ≥0.10 mm compared to conventional treatment. Conclusions. Our data suggested that DPP-4 inhibitors were associated with the regression of carotid atherosclerosis in insulin-treated T2DM patients. This study has been registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN000007396.

Multivariate regression analysis for determining short-term values of radon and its decay products from filter measurements

International Nuclear Information System (INIS)

Kraut, W.; Schwarz, W.; Wilhelm, A.

1994-01-01

A multivariate regression analysis is applied to decay measurements of α-resp. β-filter activcity. Activity concentrations for Po-218, Pb-214 and Bi-214, resp. for the Rn-222 equilibrium equivalent concentration are obtained explicitly. The regression analysis takes into account properly the variances of the measured count rates and their influence on the resulting activity concentrations. (orig.) [de

AcEST: DK953178 [AcEST

Lifescience Database Archive (English)

Full Text Available rio cholerae... 31 1.7 sp|Q4Z8K6|RBP9X_DROME Protein midlife crisis OS=Drosophila melan... 30 2.9 sp|Q7MHN6|...QIRA 11 +TG+GVDEV KI A +P GD ++A Sbjct: 164 KTGVGVDEVLEKIVSAIPAPQGDPDAPLQA 193 >sp|Q4Z8K6|RBP9X_DROME Protein midlife crisis

Prediction for steatosis in type-2 diabetes: clinico-biological markers versus 1H-MR spectroscopy

International Nuclear Information System (INIS)

Guiu, Boris; Krause, Denis; Cercueil, Jean-Pierre; Crevisy-Girod, Elodie; Binquet, Christine; Duvillard, Laurence; Masson, David; Lepage, Come; Hamza, Samia; Minello, Anne; Hillon, Patrick; Verges, Bruno; Petit, Jean-Michel

2012-01-01

The SteatoTest, fatty liver index (FLI) and hepatic steatosis index (HSI) are clinico-biological scores of steatosis validated in general or selected populations. Serum adiponectin (s-adiponectin) and retinol binding protein 4 (s-RBP4) are adipokines that could predict liver steatosis. We investigated whether the Steatotest, FLI, HSI, s-adiponectin and s-RBP4 could be valid predictors of liver steatosis in type-2 diabetic (T2D) patients. We enrolled 220 consecutive T2D patients. Reference standard was 3.0 T 1 H-MR spectroscopy (corrected for T1 and T2 decays). Intraclass correlation coefficients (ICCs), Kappa statistic measures of agreement, receiver operating characteristic (ROC) curves were assessed. Median liver fat content was 91 mg triglyceride/g liver tissue (range: 0-392). ICCs among the Steatotest, FLI, HSI, s-adiponectin, s-RBP4 and spectroscopy were low: 0.384, 0.281, 0.087, -0.297 and 0.048. Agreement between scores and spectroscopy was poor (Kappa range: 0.042-0.281). The areas under the ROC curves were low: 0.674, 0.647, 0.637, 0.616 and 0.540. S-adiponectin and s-RBP4 levels were strongly related to the presence of diabetic nephropathy (P = 0.0037 and P = 0.004; Mann-Whitney). The SteatoTest, FLI, HSI, s-adiponectin, s-RBP4 are not valid predictors of steatosis in T2D patients. Clino-biological markers cannot replace 1 H-MR spectroscopy for the assessment of liver fat in this population. (orig.)

Prediction for steatosis in type-2 diabetes: clinico-biological markers versus {sup 1}H-MR spectroscopy

Energy Technology Data Exchange (ETDEWEB)

Guiu, Boris; Krause, Denis; Cercueil, Jean-Pierre [University of Burgundy, INSERM U866, BP 87900, Dijon (France); CHU (University Hospital), Department of Radiology, 2 boulevard Marechal de Lattre de Tassigny, BP 77908, Dijon (France); Crevisy-Girod, Elodie [CHU (University Hospital), Department of Endocrinology, Diabetology, and Metabolic Diseases, BP 77908, Dijon (France); Binquet, Christine [University of Burgundy, INSERM U866, BP 87900, Dijon (France); CHU (University Hospital), Department of Biostatistics and Medical Informatics, BP 77908, Dijon (France); Duvillard, Laurence [University of Burgundy, INSERM U866, BP 87900, Dijon (France); Masson, David [University of Burgundy, INSERM U866, BP 87900, Dijon (France); CHU (University Hospital), Department of Biochemistry, BP 77908, Dijon (France); Lepage, Come; Hamza, Samia; Minello, Anne; Hillon, Patrick [University of Burgundy, INSERM U866, BP 87900, Dijon (France); CHU (University Hospital), Department of Hepatology, BP 77908, Dijon (France); Verges, Bruno; Petit, Jean-Michel [University of Burgundy, INSERM U866, BP 87900, Dijon (France); CHU (University Hospital), Department of Endocrinology, Diabetology, and Metabolic Diseases, BP 77908, Dijon (France)

2012-04-15

The SteatoTest, fatty liver index (FLI) and hepatic steatosis index (HSI) are clinico-biological scores of steatosis validated in general or selected populations. Serum adiponectin (s-adiponectin) and retinol binding protein 4 (s-RBP4) are adipokines that could predict liver steatosis. We investigated whether the Steatotest, FLI, HSI, s-adiponectin and s-RBP4 could be valid predictors of liver steatosis in type-2 diabetic (T2D) patients. We enrolled 220 consecutive T2D patients. Reference standard was 3.0 T {sup 1}H-MR spectroscopy (corrected for T1 and T2 decays). Intraclass correlation coefficients (ICCs), Kappa statistic measures of agreement, receiver operating characteristic (ROC) curves were assessed. Median liver fat content was 91 mg triglyceride/g liver tissue (range: 0-392). ICCs among the Steatotest, FLI, HSI, s-adiponectin, s-RBP4 and spectroscopy were low: 0.384, 0.281, 0.087, -0.297 and 0.048. Agreement between scores and spectroscopy was poor (Kappa range: 0.042-0.281). The areas under the ROC curves were low: 0.674, 0.647, 0.637, 0.616 and 0.540. S-adiponectin and s-RBP4 levels were strongly related to the presence of diabetic nephropathy (P = 0.0037 and P = 0.004; Mann-Whitney). The SteatoTest, FLI, HSI, s-adiponectin, s-RBP4 are not valid predictors of steatosis in T2D patients. Clino-biological markers cannot replace {sup 1}H-MR spectroscopy for the assessment of liver fat in this population. (orig.)

An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models

DEFF Research Database (Denmark)

Kinnebrock, Silja; Podolskij, Mark

This paper introduces a new estimator to measure the ex-post covariation between high-frequency financial time series under market microstructure noise. We provide an asymptotic limit theory (including feasible central limit theorems) for standard methods such as regression, correlation analysis...... process can be relaxed and how our method can be applied to non-synchronous observations. We also present an empirical study of how high-frequency correlations, regressions and covariances change through time....

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

Science.gov (United States)

Karabatsos, George

2017-02-01

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected

Predicting the cross-reactivities of polycyclic aromatic hydrocarbons in ELISA by regression analysis and CoMFA methods

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yan-Feng; Dai, Shu-Gui [College of Environmental Science and Engineering, Nankai University, Key Laboratory for Pollution Process and Environmental Criteria of Ministry of Education, Tianjin (China); Ma, Yi [College of Chemistry, Nankai University, Institute of Elemento-Organic Chemistry, Tianjin (China); Gao, Zhi-Xian [Institute of Hygiene and Environmental Medicine, Tianjin (China)

2010-07-15

Immunoassays have been regarded as a possible alternative or supplement for measuring polycyclic aromatic hydrocarbons (PAHs) in the environment. Since there are too many potential cross-reactants for PAH immunoassays, it is difficult to determine all the cross-reactivities (CRs) by experimental tests. The relationship between CR and the physical-chemical properties of PAHs and related compounds was investigated using the CR data from a commercial enzyme-linked immunosorbent assay (ELISA) kit test. Two quantitative structure-activity relationship (QSAR) techniques, regression analysis and comparative molecular field analysis (CoMFA), were applied for predicting the CR of PAHs in this ELISA kit. Parabolic regression indicates that the CRs are significantly correlated with the logarithm of the partition coefficient for the octanol-water system (log K{sub ow}) (r{sup 2}=0.643, n=23, P<0.0001), suggesting that hydrophobic interactions play an important role in the antigen-antibody binding and the cross-reactions in this ELISA test. The CoMFA model obtained shows that the CRs of the PAHs are correlated with the 3D structure of the molecules (r{sub cv}{sup 2}=0.663, r{sup 2}=0.873, F{sub 4,32}=55.086). The contributions of the steric and electrostatic fields to CR were 40.4 and 59.6%, respectively. Both of the QSAR models satisfactorily predict the CR in this PAH immunoassay kit, and help in understanding the mechanisms of antigen-antibody interaction. (orig.)

Regression analysis of mixed recurrent-event and panel-count data.

Science.gov (United States)

Zhu, Liang; Tong, Xinwei; Sun, Jianguo; Chen, Manhua; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L

2014-07-01

In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20: , 1-42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Testing Heteroscedasticity in Robust Regression

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2011-01-01

Roč. 1, č. 4 (2011), s. 25-28 ISSN 2045-3345 Grant - others:GA ČR(CZ) GA402/09/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust regression * heteroscedasticity * regression quantiles * diagnostics Subject RIV: BB - Applied Statistics , Operational Research http://www.researchjournals.co.uk/documents/Vol4/06%20Kalina.pdf

[Application of negative binomial regression and modified Poisson regression in the research of risk factors for injury frequency].

Science.gov (United States)

Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan

2011-11-01

To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.

Independent contrasts and PGLS regression estimators are equivalent.

Science.gov (United States)

Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

2012-05-01

We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

Forecasting Model for IPTV Service in Korea Using Bootstrap Ridge Regression Analysis

Science.gov (United States)

Lee, Byoung Chul; Kee, Seho; Kim, Jae Bum; Kim, Yun Bae

The telecom firms in Korea are taking new step to prepare for the next generation of convergence services, IPTV. In this paper we described our analysis on the effective method for demand forecasting about IPTV broadcasting. We have tried according to 3 types of scenarios based on some aspects of IPTV potential market and made a comparison among the results. The forecasting method used in this paper is the multi generation substitution model with bootstrap ridge regression analysis.

An Additive-Multiplicative Cox-Aalen Regression Model

DEFF Research Database (Denmark)

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...

Marital status integration and suicide: A meta-analysis and meta-regression.

Science.gov (United States)

Kyung-Sook, Woo; SangSoo, Shin; Sangjin, Shin; Young-Jeon, Shin

2018-01-01

Marital status is an index of the phenomenon of social integration within social structures and has long been identified as an important predictor suicide. However, previous meta-analyses have focused only on a particular marital status, or not sufficiently explored moderators. A meta-analysis of observational studies was conducted to explore the relationships between marital status and suicide and to understand the important moderating factors in this association. Electronic databases were searched to identify studies conducted between January 1, 2000 and June 30, 2016. We performed a meta-analysis, subgroup analysis, and meta-regression of 170 suicide risk estimates from 36 publications. Using random effects model with adjustment for covariates, the study found that the suicide risk for non-married versus married was OR = 1.92 (95% CI: 1.75-2.12). The suicide risk was higher for non-married individuals aged analysis by gender, non-married men exhibited a greater risk of suicide than their married counterparts in all sub-analyses, but women aged 65 years or older showed no significant association between marital status and suicide. The suicide risk in divorced individuals was higher than for non-married individuals in both men and women. The meta-regression showed that gender, age, and sample size affected between-study variation. The results of the study indicated that non-married individuals have an aggregate higher suicide risk than married ones. In addition, gender and age were confirmed as important moderating factors in the relationship between marital status and suicide. Copyright © 2017 Elsevier Ltd. All rights reserved.

Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis

Directory of Open Access Journals (Sweden)

Renfu Jia

2016-01-01

Full Text Available This paper introduces an integrated approach to find out the major factors influencing efficiency of irrigation water use in China. It combines multiple stepwise regression (MSR and principal component analysis (PCA to obtain more realistic results. In real world case studies, classical linear regression model often involves too many explanatory variables and the linear correlation issue among variables cannot be eliminated. Linearly correlated variables will cause the invalidity of the factor analysis results. To overcome this issue and reduce the number of the variables, PCA technique has been used combining with MSR. As such, the irrigation water use status in China was analyzed to find out the five major factors that have significant impacts on irrigation water use efficiency. To illustrate the performance of the proposed approach, the calculation based on real data was conducted and the results were shown in this paper.

A regression analysis of the effect of energy use in agriculture

International Nuclear Information System (INIS)

Karkacier, Osman; Gokalp Goktolga, Z.; Cicek, Adnan

2006-01-01

This study investigates the impacts of energy use on productivity of Turkey's agriculture. It reports the results of a regression analysis of the relationship between energy use and agricultural productivity. The study is based on the analysis of the yearbook data for the period 1971-2003. Agricultural productivity was specified as a function of its energy consumption (TOE) and gross additions of fixed assets during the year. Least square (LS) was employed to estimate equation parameters. The data of this study comes from the State Institute of Statistics (SIS) and The Ministry of Energy of Turkey

Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks

Science.gov (United States)

Nishtala, Sneha; Neelamraju, Yaseswini; Janga, Sarath Chandra

2016-05-01

RNA-binding proteins (RBPs) are pivotal in orchestrating several steps in the metabolism of RNA in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Here, we employed CLIP (cross-linking immunoprecipitation)-seq datasets for 60 human RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a network of genome-wide RBP- target RNA interactions for each RBP. We show in humans that majority (~78%) of the RBPs are strongly associated with their target transcripts at transcript level while ~95% of the studied RBPs were also found to be strongly associated with expression levels of target transcripts when protein expression levels of RBPs were employed. At transcript level, RBP - RNA interaction data for the yeast genome, exhibited a strong association for 63% of the RBPs, confirming the association to be conserved across large phylogenetic distances. Analysis to uncover the features contributing to these associations revealed the number of target transcripts and length of the selected protein-coding transcript of an RBP at the transcript level while intensity of the CLIP signal, number of RNA-Binding domains, location of the binding site on the transcript, to be significant at the protein level. Our analysis will contribute to improved modelling and prediction of post-transcriptional networks.

Prediction of hearing outcomes by multiple regression analysis in patients with idiopathic sudden sensorineural hearing loss.

Science.gov (United States)

Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki

2014-12-01

This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.

Regression of environmental noise in LIGO data

International Nuclear Information System (INIS)

Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G

2015-01-01

We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK method has been extended, incorporating banks of Wiener filters in the time–frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we present the first results on regression of the bi-coherent noise in the LIGO data. (paper)

[Multiple linear regression analysis of X-ray measurement and WOMAC scores of knee osteoarthritis].

Science.gov (United States)

Ma, Yu-Feng; Wang, Qing-Fu; Chen, Zhao-Jun; Du, Chun-Lin; Li, Jun-Hai; Huang, Hu; Shi, Zong-Ting; Yin, Yue-Shan; Zhang, Lei; A-Di, Li-Jiang; Dong, Shi-Yu; Wu, Ji

2012-05-01

To perform Multiple Linear Regression analysis of X-ray measurement and WOMAC scores of knee osteoarthritis, and to analyze their relationship with clinical and biomechanical concepts. From March 2011 to July 2011, 140 patients (250 knees) were reviewed, including 132 knees in the left and 118 knees in the right; ranging in age from 40 to 71 years, with an average of 54.68 years. The MB-RULER measurement software was applied to measure femoral angle, tibial angle, femorotibial angle, joint gap angle from antero-posterir and lateral position of X-rays. The WOMAC scores were also collected. Then multiple regression equations was applied for the linear regression analysis of correlation between the X-ray measurement and WOMAC scores. There was statistical significance in the regression equation of AP X-rays value and WOMAC scores (Pregression equation of lateral X-ray value and WOMAC scores (P>0.05). 1) X-ray measurement of knee joint can reflect the WOMAC scores to a certain extent. 2) It is necessary to measure the X-ray mechanical axis of knee, which is important for diagnosis and treatment of osteoarthritis. 3) The correlation between tibial angle,joint gap angle on antero-posterior X-ray and WOMAC scores is significant, which can be used to assess the functional recovery of patients before and after treatment.

Neck-focused panic attacks among Cambodian refugees; a logistic and linear regression analysis.

Science.gov (United States)

Hinton, Devon E; Chhean, Dara; Pich, Vuth; Um, Khin; Fama, Jeanne M; Pollack, Mark H

2006-01-01

Consecutive Cambodian refugees attending a psychiatric clinic were assessed for the presence and severity of current--i.e., at least one episode in the last month--neck-focused panic. Among the whole sample (N=130), in a logistic regression analysis, the Anxiety Sensitivity Index (ASI; odds ratio=3.70) and the Clinician-Administered PTSD Scale (CAPS; odds ratio=2.61) significantly predicted the presence of current neck panic (NP). Among the neck panic patients (N=60), in the linear regression analysis, NP severity was significantly predicted by NP-associated flashbacks (beta=.42), NP-associated catastrophic cognitions (beta=.22), and CAPS score (beta=.28). Further analysis revealed the effect of the CAPS score to be significantly mediated (Sobel test [Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182]) by both NP-associated flashbacks and catastrophic cognitions. In the care of traumatized Cambodian refugees, NP severity, as well as NP-associated flashbacks and catastrophic cognitions, should be specifically assessed and treated.

Study on REE bound proteins in natural plant fern dicranopteris dichotomy by MAA

International Nuclear Information System (INIS)

Guo Fanqing; Wang Yuqi; Sun Jingxing; Chen Hongmin; Xu Lei; Cao Guoyin

1997-01-01

Biochemical techniques, including pH variation, outsalting, ultracentrifugation, gel filtration chromatography and electrophoresis, etc., have been employed together with instrumental neutron activation analysis (INAA) to study the rare earth elements (REE) bound proteins in the natural plant fern, Dicranopteris dichotomy. INAA was also used to identify whether the proteins were bound firmly with REE. The results obtained show that two REE bound proteins (RBP-I and RBP-II) have been separated. The molecular mass (molecular weight, MW) of RBP-I on Sephadex G-200 gel column is about 8 x 10 5 and that of RBP-II is less than 12400, respectively. However, SDS-PAGE of the two proteins shows that they mainly have two protein subunits with MW 14100 and 38700. They are probably conjugated proteins, glycoproteins with different glycol-units

Finding determinants of audit delay by pooled OLS regression analysis

OpenAIRE

Vuko, Tina; Čular, Marko

2014-01-01

The aim of this paper is to investigate determinants of audit delay. Audit delay is measured as the length of time (i.e. the number of calendar days) from the fiscal year-end to the audit report date. It is important to understand factors that influence audit delay since it directly affects the timeliness of financial reporting. The research is conducted on a sample of Croatian listed companies, covering the period of four years (from 2008 to 2011). We use pooled OLS regression analysis, mode...

Tumor regression patterns in retinoblastoma

International Nuclear Information System (INIS)

Zafar, S.N.; Siddique, S.N.; Zaheer, N.

2016-01-01

To observe the types of tumor regression after treatment, and identify the common pattern of regression in our patients. Study Design: Descriptive study. Place and Duration of Study: Department of Pediatric Ophthalmology and Strabismus, Al-Shifa Trust Eye Hospital, Rawalpindi, Pakistan, from October 2011 to October 2014. Methodology: Children with unilateral and bilateral retinoblastoma were included in the study. Patients were referred to Pakistan Institute of Medical Sciences, Islamabad, for chemotherapy. After every cycle of chemotherapy, dilated funds examination under anesthesia was performed to record response of the treatment. Regression patterns were recorded on RetCam II. Results: Seventy-four tumors were included in the study. Out of 74 tumors, 3 were ICRB group A tumors, 43 were ICRB group B tumors, 14 tumors belonged to ICRB group C, and remaining 14 were ICRB group D tumors. Type IV regression was seen in 39.1% (n=29) tumors, type II in 29.7% (n=22), type III in 25.6% (n=19), and type I in 5.4% (n=4). All group A tumors (100%) showed type IV regression. Seventeen (39.5%) group B tumors showed type IV regression. In group C, 5 tumors (35.7%) showed type II regression and 5 tumors (35.7%) showed type IV regression. In group D, 6 tumors (42.9%) regressed to type II non-calcified remnants. Conclusion: The response and success of the focal and systemic treatment, as judged by the appearance of different patterns of tumor regression, varies with the ICRB grouping of the tumor. (author)

A Seemingly Unrelated Poisson Regression Model

OpenAIRE

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.

Quantile regression theory and applications

CERN Document Server

Davino, Cristina; Vistocco, Domenico

2013-01-01

A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and

Beyond the mean estimate: a quantile regression analysis of inequalities in educational outcomes using INVALSI survey data

Directory of Open Access Journals (Sweden)

Antonella Costanzo

2017-09-01

Full Text Available Abstract The number of studies addressing issues of inequality in educational outcomes using cognitive achievement tests and variables from large-scale assessment data has increased. Here the value of using a quantile regression approach is compared with a classical regression analysis approach to study the relationships between educational outcomes and likely predictor variables. Italian primary school data from INVALSI large-scale assessments were analyzed using both quantile and standard regression approaches. Mathematics and reading scores were regressed on students' characteristics and geographical variables selected for their theoretical and policy relevance. The results demonstrated that, in Italy, the role of gender and immigrant status varied across the entire conditional distribution of students’ performance. Analogous results emerged pertaining to the difference in students’ performance across Italian geographic areas. These findings suggest that quantile regression analysis is a useful tool to explore the determinants and mechanisms of inequality in educational outcomes. A proper interpretation of quantile estimates may enable teachers to identify effective learning activities and help policymakers to develop tailored programs that increase equity in education.

Machine learning of swimming data via wisdom of crowd and regression analysis.

Science.gov (United States)

Xie, Jiang; Xu, Junfu; Nie, Celine; Nie, Qing

2017-04-01

Every performance, in an officially sanctioned meet, by a registered USA swimmer is recorded into an online database with times dating back to 1980. For the first time, statistical analysis and machine learning methods are systematically applied to 4,022,631 swim records. In this study, we investigate performance features for all strokes as a function of age and gender. The variances in performance of males and females for different ages and strokes were studied, and the correlations of performances for different ages were estimated using the Pearson correlation. Regression analysis show the performance trends for both males and females at different ages and suggest critical ages for peak training. Moreover, we assess twelve popular machine learning methods to predict or classify swimmer performance. Each method exhibited different strengths or weaknesses in different cases, indicating no one method could predict well for all strokes. To address this problem, we propose a new method by combining multiple inference methods to derive Wisdom of Crowd Classifier (WoCC). Our simulation experiments demonstrate that the WoCC is a consistent method with better overall prediction accuracy. Our study reveals several new age-dependent trends in swimming and provides an accurate method for classifying and predicting swimming times.

Weighted functional linear regression models for gene-based association analysis.

Science.gov (United States)

Belonogova, Nadezhda M; Svishcheva, Gulnara R; Wilson, James F; Campbell, Harry; Axenovich, Tatiana I

2018-01-01

Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

Data analysis and approximate models model choice, location-scale, analysis of variance, nonparametric regression and image analysis

CERN Document Server

Davies, Patrick Laurie

2014-01-01

Introduction IntroductionApproximate Models Notation Two Modes of Statistical AnalysisTowards One Mode of Analysis Approximation, Randomness, Chaos, Determinism ApproximationA Concept of Approximation Approximation Approximating a Data Set by a Model Approximation Regions Functionals and EquivarianceRegularization and Optimality Metrics and DiscrepanciesStrong and Weak Topologies On Being (almost) Honest Simulations and Tables Degree of Approximation and p-values ScalesStability of Analysis The Choice of En(α, P) Independence Procedures, Approximation and VaguenessDiscrete Models The Empirical Density Metrics and Discrepancies The Total Variation Metric The Kullback-Leibler and Chi-Squared Discrepancies The Po(λ) ModelThe b(k, p) and nb(k, p) Models The Flying Bomb Data The Student Study Times Data OutliersOutliers, Data Analysis and Models Breakdown Points and Equivariance Identifying Outliers and Breakdown Outliers in Multivariate Data Outliers in Linear Regression Outliers in Structured Data The Location...

Regression analysis of mixed panel count data with dependent terminal events.

Science.gov (United States)

Yu, Guanglei; Zhu, Liang; Li, Yang; Sun, Jianguo; Robison, Leslie L

2017-05-10

Event history studies are commonly conducted in many fields, and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data earlier, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established, and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally, the methodology is applied to a childhood cancer study that motivated this study. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

An integrated study of surface roughness in EDM process using regression analysis and GSO algorithm

Science.gov (United States)

Zainal, Nurezayana; Zain, Azlan Mohd; Sharif, Safian; Nuzly Abdull Hamed, Haza; Mohamad Yusuf, Suhaila

2017-09-01

The aim of this study is to develop an integrated study of surface roughness (Ra) in the die-sinking electrical discharge machining (EDM) process of Ti-6AL-4V titanium alloy with positive polarity of copper-tungsten (Cu-W) electrode. Regression analysis and glowworm swarm optimization (GSO) algorithm were considered for modelling and optimization process. Pulse on time (A), pulse off time (B), peak current (C) and servo voltage (D) were selected as the machining parameters with various levels. The experiments have been conducted based on the two levels of full factorial design with an added center point design of experiments (DOE). Moreover, mathematical models with linear and 2 factor interaction (2FI) effects of the parameters chosen were developed. The validity test of the fit and the adequacy of the developed mathematical models have been carried out by using analysis of variance (ANOVA) and F-test. The statistical analysis showed that the 2FI model outperformed with the most minimal value of Ra compared to the linear model and experimental result.

How efficient are referral hospitals in Uganda? A data envelopment analysis and tobit regression approach.

Science.gov (United States)

Mujasi, Paschal N; Asbu, Eyob Z; Puig-Junoy, Jaume

2016-07-08

Hospitals represent a significant proportion of health expenditures in Uganda, accounting for about 26 % of total health expenditure. Improving the technical efficiency of hospitals in Uganda can result in large savings which can be devoted to expand access to services and improve quality of care. This paper explores the technical efficiency of referral hospitals in Uganda during the 2012/2013 financial year. This was a cross sectional study using secondary data. Input and output data were obtained from the Uganda Ministry of Health annual health sector performance report for the period July 1, 2012 to June 30, 2013 for the 14 public sector regional referral and 4 large private not for profit hospitals. We assumed an output-oriented model with Variable Returns to Scale to estimate the efficiency score for each hospital using Data Envelopment Analysis (DEA) with STATA13. Using a Tobit model DEA, efficiency scores were regressed against selected institutional and contextual/environmental factors to estimate their impacts on efficiency. The average variable returns to scale (Pure) technical efficiency score was 91.4 % and the average scale efficiency score was 87.1 % while the average constant returns to scale technical efficiency score was 79.4 %. Technically inefficient hospitals could have become more efficient by increasing the outpatient department visits by 45,943; and inpatient days by 31,425 without changing the total number of inputs. Alternatively, they would achieve efficiency by for example transferring the excess 216 medical staff and 454 beds to other levels of the health system without changing the total number of outputs. Tobit regression indicates that significant factors in explaining hospital efficiency are: hospital size (p Uganda.

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

Science.gov (United States)

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

A simultaneous confidence band for sparse longitudinal regression

KAUST Repository

Ma, Shujie; Yang, Lijian; Carroll, Raymond J.

2012-01-01

Functional data analysis has received considerable recent attention and a number of successful applications have been reported. In this paper, asymptotically simultaneous confidence bands are obtained for the mean function of the functional regression model, using piecewise constant spline estimation. Simulation experiments corroborate the asymptotic theory. The confidence band procedure is illustrated by analyzing CD4 cell counts of HIV infected patients.

Silent changes of tuberculosis in Iran (2005-2015: A joinpoint regression analysis

Directory of Open Access Journals (Sweden)

Abolfazl Marvi

2017-01-01

Full Text Available Introduction and Aim: Tuberculosis (TB poses a severe risk to public health through the world but excessively distresses low-income nations. The aim of this study is to analyze silent changes of TB in Iran (2005–2015: A joinpoint regression analysis. Materials and Methods: This is a trend study conducted on all patients (n = 70 that register in control disease center of Joibar (one of coastal cities and tourism destination in Northern Iran which was recognized as an independent town since 1998 during 2005–2015. The characteristics of patients imported to the SPSS 19 and variation in incidence rate of different forms of pulmonary TB (PTB (PTB+ or PTB– and extra-PTB (EPTB/year was analyzed. Variation in incidence rate of TB for male and female groups and different age groups (0–14, 15–24, 25–34, 35–44, 45–54, 55–64, and above 65 years was analyzed, variation in trend of this diseases for different groups was compared in intended years, and also, variation in incidence rate of TB was analyzed by Joinpoint Regression Software. Results: The total number of TB was 70 cases during 2005–2015. The mean age of patients was 42.31 ± 21.26 years and median age was 40 years. About 71.4% of patients were PTB (55.7% for with PTB+ and 15.7% with PTB– and rest of them (28.4% were EPTB. In regard to classification of cases, 97.1% of them were new cases, 1.45% of them were relapsed cases, and 1.45% of them imported cases. In addition, history of hospitalization due to TB was observed in 44.3%. Conclusion: Despite recent developments of governmental health-care system in Iran and proper access to it and considering this fact that identification of TB cases with passive surveillance is possible. Hence, developing certain programs for sensitization of the covered population is essential.

Analysis of sparse data in logistic regression in medical research: A newer approach

Directory of Open Access Journals (Sweden)

S Devika

2016-01-01

Full Text Available Background and Objective: In the analysis of dichotomous type response variable, logistic regression is usually used. However, the performance of logistic regression in the presence of sparse data is questionable. In such a situation, a common problem is the presence of high odds ratios (ORs with very wide 95% confidence interval (CI (OR: >999.999, 95% CI: 999.999. In this paper, we addressed this issue by using penalized logistic regression (PLR method. Materials and Methods: Data from case-control study on hyponatremia and hiccups conducted in Christian Medical College, Vellore, Tamil Nadu, India was used. The outcome variable was the presence/absence of hiccups and the main exposure variable was the status of hyponatremia. Simulation dataset was created with different sample sizes and with a different number of covariates. Results: A total of 23 cases and 50 controls were used for the analysis of ordinary and PLR methods. The main exposure variable hyponatremia was present in nine (39.13% of the cases and in four (8.0% of the controls. Of the 23 hiccup cases, all were males and among the controls, 46 (92.0% were males. Thus, the complete separation between gender and the disease group led into an infinite OR with 95% CI (OR: >999.999, 95% CI: 999.999 whereas there was a finite and consistent regression coefficient for gender (OR: 5.35; 95% CI: 0.42, 816.48 using PLR. After adjusting for all the confounding variables, hyponatremia entailed 7.9 (95% CI: 2.06, 38.86 times higher risk for the development of hiccups as was found using PLR whereas there was an overestimation of risk OR: 10.76 (95% CI: 2.17, 53.41 using the conventional method. Simulation experiment shows that the estimated coverage probability of this method is near the nominal level of 95% even for small sample sizes and for a large number of covariates. Conclusions: PLR is almost equal to the ordinary logistic regression when the sample size is large and is superior in small cell

Multiple Linear Regression Analysis of Factors Affecting Real Property Price Index From Case Study Research In Istanbul/Turkey

Science.gov (United States)

Denli, H. H.; Koc, Z.

2015-12-01

Estimation of real properties depending on standards is difficult to apply in time and location. Regression analysis construct mathematical models which describe or explain relationships that may exist between variables. The problem of identifying price differences of properties to obtain a price index can be converted into a regression problem, and standard techniques of regression analysis can be used to estimate the index. Considering regression analysis for real estate valuation, which are presented in real marketing process with its current characteristics and quantifiers, the method will help us to find the effective factors or variables in the formation of the value. In this study, prices of housing for sale in Zeytinburnu, a district in Istanbul, are associated with its characteristics to find a price index, based on information received from a real estate web page. The associated variables used for the analysis are age, size in m2, number of floors having the house, floor number of the estate and number of rooms. The price of the estate represents the dependent variable, whereas the rest are independent variables. Prices from 60 real estates have been used for the analysis. Same price valued locations have been found and plotted on the map and equivalence curves have been drawn identifying the same valued zones as lines.

Prevalence of treponema species detected in endodontic infections: systematic review and meta-regression analysis.

Science.gov (United States)

Leite, Fábio R M; Nascimento, Gustavo G; Demarco, Flávio F; Gomes, Brenda P F A; Pucci, Cesar R; Martinho, Frederico C

2015-05-01

This systematic review and meta-regression analysis aimed to calculate a combined prevalence estimate and evaluate the prevalence of different Treponema species in primary and secondary endodontic infections, including symptomatic and asymptomatic cases. The MEDLINE/PubMed, Embase, Scielo, Web of Knowledge, and Scopus databases were searched without starting date restriction up to and including March 2014. Only reports in English were included. The selected literature was reviewed by 2 authors and classified as suitable or not to be included in this review. Lists were compared, and, in case of disagreements, decisions were made after a discussion based on inclusion and exclusion criteria. A pooled prevalence of Treponema species in endodontic infections was estimated. Additionally, a meta-regression analysis was performed. Among the 265 articles identified in the initial search, only 51 were included in the final analysis. The studies were classified into 2 different groups according to the type of endodontic infection and whether it was an exclusively primary/secondary study (n = 36) or a primary/secondary comparison (n = 15). The pooled prevalence of Treponema species was 41.5% (95% confidence interval, 35.9-47.0). In the multivariate model of meta-regression analysis, primary endodontic infections (P apical abscess, symptomatic apical periodontitis (P < .001), and concomitant presence of 2 or more species (P = .028) explained the heterogeneity regarding the prevalence rates of Treponema species. Our findings suggest that Treponema species are important pathogens involved in endodontic infections, particularly in cases of primary and acute infections. Copyright © 2015 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

Science.gov (United States)

Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

2017-02-06

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

Science.gov (United States)

Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

2009-11-01

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

What Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis

Science.gov (United States)

Thomas, Emily H.; Galambos, Nora

2004-01-01

To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The…

Ca analysis: an Excel based program for the analysis of intracellular calcium transients including multiple, simultaneous regression analysis.

Science.gov (United States)

Greensmith, David J

2014-01-01

Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. Copyright © 2013 The Author. Published by Elsevier Ireland Ltd.. All rights reserved.

Robust Regression and its Application in Financial Data Analysis

OpenAIRE

Mansoor Momeni; Mahmoud Dehghan Nayeri; Ali Faal Ghayoumi; Hoda Ghorbani

2010-01-01

This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from th...

REE bound proteins in natural plant fern Dicranopteris dichitoma by MAA

International Nuclear Information System (INIS)

Guo, F.Q.; Wang, Y.Q.; Sun, J.X.; Chen, H.M.

1996-01-01

Biochemical techniques, including pH variation, outsalting, ultracentrifugation, gel filtration chromatography and electrophoresis, etc., have been employed together with instrumental neutron activation analysis (INAA) to study the rare earth elements (REE) bound proteins in the natural plant fern, Dicranopteris dichitoma. INAA was also used to identify whether the proteins were bound firmly with REE. The results obtained show that two REE bound proteins (RBP-I and RBP-II) have been separated. The molecular weight of RBP-I on Sephadex G-200 gel column is about 8 x 10 5 Daltons and that of RBP-II is less than 12,400 Daltons, respectively. However, SDS-PAGE of the two proteins shows that they mainly have two protein subunits with MW 14,100 and 38,700 Daltons. They are probably conjugated proteins, glycoproteins with different glyco-units. (author). 22 refs., 7 figs., 1 tab

Support vector methods for survival analysis: a comparison between ranking and regression approaches.

Science.gov (United States)

Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

2011-10-01

To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods

Prostate cancer mortality in Serbia, 1991-2010: a joinpoint regression analysis.

Science.gov (United States)

Ilic, Milena; Ilic, Irena

2016-06-01

The aim of this descriptive epidemiological study was to analyze the mortality trend of prostate cancer in Serbia (excluding the Kosovo and Metohia) from 1991 to 2010. The age-standardized prostate cancer mortality rates (per 100 000) were calculated by direct standardization, using the World Standard Population. Average annual percentage of change (AAPC) and the corresponding 95% confidence interval (CI) was computed for trend using the joinpoint regression analysis. Significantly increased trend in prostate cancer mortality was recorded in Serbia continuously from 1991 to 2010 (AAPC = +2.2, 95% CI = 1.6-2.9). Mortality rates for prostate cancer showed a significant upward trend in all men aged 50 and over: AAPC (95% CI) was +1.9% (0.1-3.8) in aged 50-59 years, +1.7% (0.9-2.6) in aged 60-69 years, +2.0% (1.2-2.9) in aged 70-79 years and +3.5% (2.4-4.6) in aged 80 years and over. According to comparability test, prostate cancer mortality trends in majority of age groups were parallel (final selected model failed to reject parallelism, P > 0.05). The increasing prostate cancer mortality trend implies the need for more effective measures of prevention, screening and early diagnosis, as well as prostate cancer treatment in Serbia. © The Author 2015. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

[Bibliometrics and visualization analysis of land use regression models in ambient air pollution research].

Science.gov (United States)

Zhang, Y J; Zhou, D H; Bai, Z P; Xue, F X

2018-02-10

Objective: To quantitatively analyze the current status and development trends regarding the land use regression (LUR) models on ambient air pollution studies. Methods: Relevant literature from the PubMed database before June 30, 2017 was analyzed, using the Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.0). Keywords co-occurrence networks, cluster mapping and timeline mapping were generated, using the CiteSpace 5.1.R5 software. Relevant literature identified in three Chinese databases was also reviewed. Results: Four hundred sixty four relevant papers were retrieved from the PubMed database. The number of papers published showed an annual increase, in line with the growing trend of the index. Most papers were published in the journal of Environmental Health Perspectives . Results from the Co-word cluster analysis identified five clusters: cluster#0 consisted of birth cohort studies related to the health effects of prenatal exposure to air pollution; cluster#1 referred to land use regression modeling and exposure assessment; cluster#2 was related to the epidemiology on traffic exposure; cluster#3 dealt with the exposure to ultrafine particles and related health effects; cluster#4 described the exposure to black carbon and related health effects. Data from Timeline mapping indicated that cluster#0 and#1 were the main research areas while cluster#3 and#4 were the up-coming hot areas of research. Ninety four relevant papers were retrieved from the Chinese databases with most of them related to studies on modeling. Conclusion: In order to better assess the health-related risks of ambient air pollution, and to best inform preventative public health intervention policies, application of LUR models to environmental epidemiology studies in China should be encouraged.

Regression Analysis for Multivariate Dependent Count Data Using Convolved Gaussian Processes

OpenAIRE

Sofro, A'yunin; Shi, Jian Qing; Cao, Chunzheng

2017-01-01

Research on Poisson regression analysis for dependent data has been developed rapidly in the last decade. One of difficult problems in a multivariate case is how to construct a cross-correlation structure and at the meantime make sure that the covariance matrix is positive definite. To address the issue, we propose to use convolved Gaussian process (CGP) in this paper. The approach provides a semi-parametric model and offers a natural framework for modeling common mean structure and covarianc...

The correlation between serum free thyroxine and regression of dyslipidemia in adult males: A 4.5-year prospective study.

Science.gov (United States)

Wang, Haoyu; Liu, Aihua; Zhou, Yingying; Xiao, Yue; Yan, Yumeng; Zhao, Tong; Gong, Xun; Pang, Tianxiao; Fan, Chenling; Zhao, Jiajun; Teng, Weiping; Shan, Zhongyan; Lai, Yaxin

2017-09-01

Elevated free thyroxine (FT4) levels may play a protective role in development of dyslipidemia. However, few prospective studies have been performed to definite the effects of thyroid hormones on the improvement of dyslipidemia and its components. Thus, this study aims to clarify the association between thyroid hormones within normal range and reversal of dyslipidemia in the absence of intervention.A prospective analysis including 134 adult males was performed between 2010 and 2014. Anthropometric parameters, thyroid function, and lipid profile were measured at baseline and during follow-up. Logistic regression and receiver operating characteristic (ROC) analysis were conducted to identify the variables in forecasting the reversal of dyslipidemia and its components.During 4.5-year follow-up, 36.6% (49/134) patients resolved their dyslipidemia status without drug intervention. Compared with the continuous dyslipidemia group, subjects in reversal group had elevated FT4 and high-density lipoprotein cholesterol (HDL-C) levels, as well as decreased total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) levels at baseline. Furthermore, baseline FT4 is negatively associated with the change percentages of TG (r = -0.286, P = .001), while positively associated with HDL-C (r = 0.227, P = .008). However, no correlation of lipid profile change percentages with FT3 and TSH were observed. Furthermore, the improving effects of baseline FT4 on dyslipidemia, high TG, and low HDL-C status were still observed after multivariable adjustment. In ROC analysis, areas under curve (AUCs) for FT4 in predicting the reversal of dyslipidemia, high TG, and low HDL-C were 0.666, 0.643, and 0.702, respectively (P = .001 for dyslipidemia, .018 for high TG, and .001 for low HDL-C).Higher FT4 value within normal range may ameliorate the dyslipidemia, especially high TG and low HDL-C status, in males without drug intervention. This suggests

Genetic variants of retinol-binding protein 4 in adolescents are ...

Indian Academy of Sciences (India)

2015-09-03

Sep 3, 2015 ... associated with T2D and serum triglyceride levels in another study of a Chinese ... Japan). Serum levels of high-density lipoprotein cholesterol .... a positive correlation between RBP genetic variants and obe- sity, IR or MetS ...

Circulating omentin-1 might be associated with metabolic health status in different phenotypes of body size.

Science.gov (United States)

Alizadeh, Shahab; Mirzaei, Khadijeh; Mohammadi, Chonur; Keshavarz, Seyed Ali; Maghbooli, Zhila

2017-12-01

Adipokines are mediators of body composition and are involved in obesity complications. This study aimed to assess the association of circulating omentin-1, vaspin, and RBP-4 with body composition indices and metabolic health status (MHS) in different phenotypes of body size. A total of 350 subjects were included in the current cross-sectional study. Body composition was measured using a body composition analyzer, and serum concentrations of omentin-1, vaspin, and RBP-4 were assessed by ELISA kits. Circulating omentin-1 was significantly (OR = 1.81, 95% CI: 1.00-1.91, P = 0.01) and marginally (OR = 1.63, 95%CI: 1.00-1.75, P = 0.06) associated with MHS in the overweight and obese subjects, respectively. But no association was seen between omentin-1 and MHS in normal-weight subjects. Serum levels of vaspin and RBP-4 were not correlated with MHS. Furthermore, a significant positive correlation was observed between circulating omentin-1 and body mass index (BMI) as well as fat percentage (P = 0.02) in the MHS group. Serum vaspin concentrations were not related to body composition components in both groups. In addition, in the MHS group, circulating RBP-4 was positively correlated with fat percentage and fat mass (FM) (p body water (TBW) (p < 0.0001). In contrast, in the metabolically unhealthy group, RBP-4 was negatively correlated with fat percentage, FM, and BMI (p < 0.0001) and was positively correlated with FFM and TBW (p < 0.0001). This study showed that circulating levels of omentin-1 are useful predictors of metabolic health status in overweight and obese people.

Robust best linear estimation for regression analysis using surrogate and instrumental variables.

Science.gov (United States)

Wang, C Y

2012-04-01

We investigate methods for regression analysis when covariates are measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies the classical measurement error model, but it may not have repeated measurements. In addition to the surrogate variables that are available among the subjects in the calibration sample, we assume that there is an instrumental variable (IV) that is available for all study subjects. An IV is correlated with the unobserved true exposure variable and hence can be useful in the estimation of the regression coefficients. We propose a robust best linear estimator that uses all the available data, which is the most efficient among a class of consistent estimators. The proposed estimator is shown to be consistent and asymptotically normal under very weak distributional assumptions. For Poisson or linear regression, the proposed estimator is consistent even if the measurement error from the surrogate or IV is heteroscedastic. Finite-sample performance of the proposed estimator is examined and compared with other estimators via intensive simulation studies. The proposed method and other methods are applied to a bladder cancer case-control study.

Adipokines as Possible New Predictors of Cardiovascular Diseases: A Case Control Study

Directory of Open Access Journals (Sweden)

Laura Pala

2012-01-01

Full Text Available Background and Aims. The secretion of several adipocytokines, such as adiponectin, retinol-binding protein 4 (RBP4, adipocyte fatty acid binding protein (aFABP, and visfatin, is altered in subjects with abdominal adiposity; these endocrine alterations could contribute to increased cardiovascular risk. The aim of the study was to assess the relationship among adiponectin, RBP4, aFABP, and visfatin, and incident cardiovascular disease. Methods and Results. A case-control study, nested within a prospective cohort, on 2945 subjects enrolled for a diabetes screening program was performed. We studied 18 patients with incident fatal or nonfatal IHD (Ischemic Heart Disease or CVD (Cerebrovascular Disease, compared with 18 matched control subjects. Circulating adiponectin levels were significantly lower in cases of IHD with respect to controls. Circulating RBP4 levels were significantly increased in CVD and decreased in IHD with respect to controls. Circulating aFABP4 levels were significantly increased in CVD, while no difference was associated with IHD. Circulating visfatin levels were significantly lower in cases of both CVD and IHD with respect to controls, while no difference was associated with CVD. Conclusions. The present study confirms that low adiponectin is associated with increased incidents of IHD, but not CVD, and suggests, for the first time, a major effect of visfatin, aFABP, and RBP4 in the development of cardiovascular disease.

Sensitivity of Microstructural Factors Influencing the Impact Toughness of Hypoeutectoid Steels with Ferrite-Pearlite Structure using Multiple Regression Analysis

International Nuclear Information System (INIS)

Lee, Seung-Yong; Lee, Sang-In; Hwang, Byoung-chul

2016-01-01

In this study, the effect of microstructural factors on the impact toughness of hypoeutectoid steels with ferrite-pearlite structure was quantitatively investigated using multiple regression analysis. Microstructural analysis results showed that the pearlite fraction increased with increasing austenitizing temperature and decreasing transformation temperature which substantially decreased the pearlite interlamellar spacing and cementite thickness depending on carbon content. The impact toughness of hypoeutectoid steels usually increased as interlamellar spacing or cementite thickness decreased, although the impact toughness was largely associated with pearlite fraction. Based on these results, multiple regression analysis was performed to understand the individual effect of pearlite fraction, interlamellar spacing, and cementite thickness on the impact toughness. The regression analysis results revealed that pearlite fraction significantly affected impact toughness at room temperature, while cementite thickness did at low temperature.

Predicting Insolvency : A comparison between discriminant analysis and logistic regression using principal components

OpenAIRE

Geroukis, Asterios; Brorson, Erik

2014-01-01

In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio ­– using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of ...

A systematic review and meta-regression analysis of mivacurium for tracheal intubation.

Science.gov (United States)

Vanlinthout, L E H; Mesfin, S H; Hens, N; Vanacker, B F; Robertson, E N; Booij, L H D J

2014-12-01

We systematically reviewed factors associated with intubation conditions in randomised controlled trials of mivacurium, using random-effects meta-regression analysis. We included 29 studies of 1050 healthy participants. Four factors explained 72.9% of the variation in the probability of excellent intubation conditions: mivacurium dose, 24.4%; opioid use, 29.9%; time to intubation and age together, 18.6%. The odds ratio (95% CI) for excellent intubation was 3.14 (1.65-5.73) for doubling the mivacurium dose, 5.99 (2.14-15.18) for adding opioids to the intubation sequence, and 6.55 (6.01-7.74) for increasing the delay between mivacurium injection and airway insertion from 1 to 2 min in subjects aged 25 years and 2.17 (2.01-2.69) for subjects aged 70 years, p < 0.001 for all. We conclude that good conditions for tracheal intubation are more likely by delaying laryngoscopy after injecting a higher dose of mivacurium with an opioid, particularly in older people. © 2014 The Association of Anaesthetists of Great Britain and Ireland.

A REVIEW ON THE USE OF REGRESSION ANALYSIS IN STUDIES OF AUDIT QUALITY

Directory of Open Access Journals (Sweden)

Agung Dodit Muliawan

2015-07-01

Full Text Available This study aimed to review how regression analysis has been used in studies of abstract phenomenon, such as audit quality, an importance concept in the auditing practice (Schroeder et al., 1986, yet is not well defined. The articles reviewed were the research articles that include audit quality as research variable, either as dependent or independent variables. The articles were purposefully selected to represent balance combination between audit specific and more general accounting journals and between Anglo Saxon and Anglo American journals. The articles were published between 1983-2011 and from the A/A class journal based on ERA 2010’s classifications. The study found that most of the articles reviewed used multiple regression analysis and treated audit quality as dependent variable and measured it by using a proxy. This study also highlights the size of data sample used and the lack of discussions about the assumptions of the statistical analysis used in most of the articles reviewed. This study concluded that the effectiveness and validity of multiple regressions do not only depends on its application by the researchers but also on how the researchers communicate their findings to the audience. KEYWORDS Audit quality, regression analysis ABSTRAK Kajian ini bertujuan untuk mereviu bagaimana analisa regresi digunakan dalam suatu fenomena abstrak seperti kualitas audit, suatu konsep yang penting dalam praktik audit (Schroeder et al., 1986 namun belum terdefinisi dengan jelas. Artikel yang direviu dalam kajian ini adalah artikel penelitian yang memasukkan kualitas audit sebagai variabel penelitian, baik sebagai variabel independen maupun dependen. Artikel-artikel tersebut dipilih dengan cara purposif sampling untuk mendapatkan keterwakilan yang seimbang antara artikel jurnal khusus audit dan akuntansi secara umum, serta mewakili jurnal Anglo Saxon dan Anglo American. Artikel yang direviu diterbitkan pada periode 1983-2011 oleh jurnal yang

Logistic regression applied to natural hazards: rare event logistic regression with replications

OpenAIRE

Guns, M.; Vanacker, Veerle

2012-01-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logisti...

Differentiating regressed melanoma from regressed lichenoid keratosis.

Science.gov (United States)

Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A

2017-04-01

Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

Science.gov (United States)

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

Identification of cotton properties to improve yarn count quality by using regression analysis

International Nuclear Information System (INIS)

Amin, M.; Ullah, M.; Akbar, A.

2014-01-01

Identification of raw material characteristics towards yarn count variation was studied by using statistical techniques. Regression analysis is used to meet the objective. Stepwise regression is used for mode) selection, and coefficient of determination and mean squared error (MSE) criteria are used to identify the contributing factors of cotton properties for yam count. Statistical assumptions of normality, autocorrelation and multicollinearity are evaluated by using probability plot, Durbin Watson test, variance inflation factor (VIF), and then model fitting is carried out. It is found that, invisible (INV), nepness (Nep), grayness (RD), cotton trash (TR) and uniformity index (VI) are the main contributing cotton properties for yarn count variation. The results are also verified by Pareto chart. (author)

A tandem regression-outlier analysis of a ligand cellular system for key structural modifications around ligand binding.

Science.gov (United States)

Lin, Ying-Ting

2013-04-30

A tandem technique of hard equipment is often used for the chemical analysis of a single cell to first isolate and then detect the wanted identities. The first part is the separation of wanted chemicals from the bulk of a cell; the second part is the actual detection of the important identities. To identify the key structural modifications around ligand binding, the present study aims to develop a counterpart of tandem technique for cheminformatics. A statistical regression and its outliers act as a computational technique for separation. A PPARγ (peroxisome proliferator-activated receptor gamma) agonist cellular system was subjected to such an investigation. Results show that this tandem regression-outlier analysis, or the prioritization of the context equations tagged with features of the outliers, is an effective regression technique of cheminformatics to detect key structural modifications, as well as their tendency of impact to ligand binding. The key structural modifications around ligand binding are effectively extracted or characterized out of cellular reactions. This is because molecular binding is the paramount factor in such ligand cellular system and key structural modifications around ligand binding are expected to create outliers. Therefore, such outliers can be captured by this tandem regression-outlier analysis.

Regression analysis: An evaluation of the inuences behindthe pricing of beer

OpenAIRE

Eriksson, Sara; Häggmark, Jonas

2017-01-01

This bachelor thesis in applied mathematics is an analysis of which factors affect the pricing of beer at the Swedish market. A multiple linear regression model is created with the statistical programming language R through a study of the influences for several explanatory variables. For example these variables include country of origin, beer style, volume sold and a Bayesian weighted mean rating from RateBeer, a popular website for beer enthusiasts. The main goal of the project is to find si...

Few crystal balls are crystal clear : eyeballing regression

International Nuclear Information System (INIS)

Wittebrood, R.T.

1998-01-01

The theory of regression and statistical analysis as it applies to reservoir analysis was discussed. It was argued that regression lines are not always the final truth. It was suggested that regression lines and eyeballed lines are often equally accurate. The many conditions that must be fulfilled to calculate a proper regression were discussed. Mentioned among these conditions were the distribution of the data, hidden variables, knowledge of how the data was obtained, the need for causal correlation of the variables, and knowledge of the manner in which the regression results are going to be used. 1 tab., 13 figs

Validation of reference genes for gene expression analysis in olive (Olea europaea) mesocarp tissue by quantitative real-time RT-PCR

Science.gov (United States)

2014-01-01

Background Gene expression analysis using quantitative reverse transcription PCR (qRT-PCR) is a robust method wherein the expression levels of target genes are normalised using internal control genes, known as reference genes, to derive changes in gene expression levels. Although reference genes have recently been suggested for olive tissues, combined/independent analysis on different cultivars has not yet been tested. Therefore, an assessment of reference genes was required to validate the recent findings and select stably expressed genes across different olive cultivars. Results A total of eight candidate reference genes [glyceraldehyde 3-phosphate dehydrogenase (GAPDH), serine/threonine-protein phosphatase catalytic subunit (PP2A), elongation factor 1 alpha (EF1-alpha), polyubiquitin (OUB2), aquaporin tonoplast intrinsic protein (TIP2), tubulin alpha (TUBA), 60S ribosomal protein L18-3 (60S RBP L18-3) and polypyrimidine tract-binding protein homolog 3 (PTB)] were chosen based on their stability in olive tissues as well as in other plants. Expression stability was examined by qRT-PCR across 12 biological samples, representing mesocarp tissues at various developmental stages in three different olive cultivars, Barnea, Frantoio and Picual, independently and together during the 2009 season with two software programs, GeNorm and BestKeeper. Both software packages identified GAPDH, EF1-alpha and PP2A as the three most stable reference genes across the three cultivars and in the cultivar, Barnea. GAPDH, EF1-alpha and 60S RBP L18-3 were found to be most stable reference genes in the cultivar Frantoio while 60S RBP L18-3, OUB2 and PP2A were found to be most stable reference genes in the cultivar Picual. Conclusions The analyses of expression stability of reference genes using qRT-PCR revealed that GAPDH, EF1-alpha, PP2A, 60S RBP L18-3 and OUB2 are suitable reference genes for expression analysis in developing Olea europaea mesocarp tissues, displaying the highest level

Role of regression analysis and variation of rheological data in calculation of pressure drop for sludge pipelines.

Science.gov (United States)

Farno, E; Coventry, K; Slatter, P; Eshtiaghi, N

2018-06-15

Sludge pumps in wastewater treatment plants are often oversized due to uncertainty in calculation of pressure drop. This issue costs millions of dollars for industry to purchase and operate the oversized pumps. Besides costs, higher electricity consumption is associated with extra CO 2 emission which creates huge environmental impacts. Calculation of pressure drop via current pipe flow theory requires model estimation of flow curve data which depends on regression analysis and also varies with natural variation of rheological data. This study investigates impact of variation of rheological data and regression analysis on variation of pressure drop calculated via current pipe flow theories. Results compare the variation of calculated pressure drop between different models and regression methods and suggest on the suitability of each method. Copyright © 2018 Elsevier Ltd. All rights reserved.

COLOR IMAGE RETRIEVAL BASED ON FEATURE FUSION THROUGH MULTIPLE LINEAR REGRESSION ANALYSIS

Directory of Open Access Journals (Sweden)

K. Seetharaman

2015-08-01

Full Text Available This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The F-measure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.

Logistic regression analysis to predict Medical Licensing Examination of Thailand (MLET) Step1 success or failure.

Science.gov (United States)

Wanvarie, Samkaew; Sathapatayavongs, Boonmee

2007-09-01

The aim of this paper was to assess factors that predict students' performance in the Medical Licensing Examination of Thailand (MLET) Step1 examination. The hypothesis was that demographic factors and academic records would predict the students' performance in the Step1 Licensing Examination. A logistic regression analysis of demographic factors (age, sex and residence) and academic records [high school grade point average (GPA), National University Entrance Examination Score and GPAs of the pre-clinical years] with the MLET Step1 outcome was accomplished using the data of 117 third-year Ramathibodi medical students. Twenty-three (19.7%) students failed the MLET Step1 examination. Stepwise logistic regression analysis showed that the significant predictors of MLET Step1 success/failure were residence background and GPAs of the second and third preclinical years. For students whose sophomore and third-year GPAs increased by an average of 1 point, the odds of passing the MLET Step1 examination increased by a factor of 16.3 and 12.8 respectively. The minimum GPAs for students from urban and rural backgrounds to pass the examination were estimated from the equation (2.35 vs 2.65 from 4.00 scale). Students from rural backgrounds and/or low-grade point averages in their second and third preclinical years of medical school are at risk of failing the MLET Step1 examination. They should be given intensive tutorials during the second and third pre-clinical years.

PATH ANALYSIS WITH LOGISTIC REGRESSION MODELS : EFFECT ANALYSIS OF FULLY RECURSIVE CAUSAL SYSTEMS OF CATEGORICAL VARIABLES

OpenAIRE

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.

Análise de fatores e regressão bissegmentada em estudos de estratificação ambiental e adaptabilidade em milho Factor analysis and bissegmented regression for studies about environmental stratification and maize adaptability

Directory of Open Access Journals (Sweden)

Deoclécio Domingos Garbuglio

2007-02-01

Full Text Available O objetivo deste trabalho foi verificar possíveis divergências entre os resultados obtidos nas avaliações da adaptabilidade de 27 genótipos de milho (Zea mays L., e na estratificação de 22 ambientes no Estado do Paraná, por meio de técnicas baseadas na análise de fatores e regressão bissegmentada. As estratificações ambientais foram feitas por meio do método tradicional e por análise de fatores, aliada ao porcentual da porção simples da interação GxA (PS%. As análises de adaptabilidade foram realizadas por meio de regressão bissegmentada e análise de fatores. Pela análise de regressão bissegmentada, os genótipos estudados apresentaram alta performance produtiva; no entanto, não foi constatado o genótipo considerado como ideal. A adaptabilidade dos genótipos, analisada por meio de plotagens gráficas, apresentou respostas diferenciadas quando comparada à regressão bissegmentada. A análise de fatores mostrou-se eficiente nos processos de estratificação ambiental e adaptabilidade dos genótipos de milho.The objective of this work was to verify possible divergences among results obtained on adaptability evaluations of 27 maize genotypes (Zea mays L., and on stratification of 22 environments on Paraná State, Brazil, through techniques of factor analysis and bissegmented regression. The environmental stratifications were made through the traditional methodology and by factor analysis, allied to the percentage of the simple portion of GxE interaction (PS%. Adaptability analyses were carried out through bissegmented regression and factor analysis. By the analysis of bissegmented regression, studied genotypes had presented high productive performance; however, it was not evidenced the genotype considered as ideal. The adaptability of the genotypes, analyzed through graphs, presented different answers when compared to bissegmented regression. Factor analysis was efficient in the processes of environment stratification and

Determinants of orphan drugs prices in France: a regression analysis.

Science.gov (United States)

Korchagina, Daria; Millier, Aurelie; Vataire, Anne-Lise; Aballea, Samuel; Falissard, Bruno; Toumi, Mondher

2017-04-21

The introduction of the orphan drug legislation led to the increase in the number of available orphan drugs, but the access to them is often limited due to the high price. Social preferences regarding funding orphan drugs as well as the criteria taken into consideration while setting the price remain unclear. The study aimed at identifying the determinant of orphan drug prices in France using a regression analysis. All drugs with a valid orphan designation at the moment of launch for which the price was available in France were included in the analysis. The selection of covariates was based on a literature review and included drug characteristics (Anatomical Therapeutic Chemical (ATC) class, treatment line, age of target population), diseases characteristics (severity, prevalence, availability of alternative therapeutic options), health technology assessment (HTA) details (actual benefit (AB) and improvement in actual benefit (IAB) scores, delay between the HTA and commercialisation), and study characteristics (type of study, comparator, type of endpoint). The main data sources were European public assessment reports, HTA reports, summaries of opinion on orphan designation of the European Medicines Agency, and the French insurance database of drugs and tariffs. A generalized regression model was developed to test the association between the annual treatment cost and selected covariates. A total of 68 drugs were included. The mean annual treatment cost was €96,518. In the univariate analysis, the ATC class (p = 0.01), availability of alternative treatment options (p = 0.02) and the prevalence (p = 0.02) showed a significant correlation with the annual cost. The multivariate analysis demonstrated significant association between the annual cost and availability of alternative treatment options, ATC class, IAB score, type of comparator in the pivotal clinical trial, as well as commercialisation date and delay between the HTA and commercialisation. The

Radiation-induced brachial plexus neuropathy in breast cancer patients

Energy Technology Data Exchange (ETDEWEB)

Olsen, N.K.; Pfeiffer, P.; Mondrup, K.; Rose, C. (Odense Univ. Hospital (Denmark). Dept. of Neurology Odense Univ. Hospital (Denmark). Dept. of Clinical Neurophysiology Odense Univ. Hospital (Denmark). Dept. of Oncology R)

1990-01-01

The incidence and latency period of radiation-induced brachial plexopathy (RBP) were assessed in 79 breast cancer patients by a neurological follow-up examination at least 60 months (range 67-130 months) after the primary treatment. All patients were treated primarily with simple mastectomy, axillary nodal sampling and radiotherapy (RT). Postoperatively, pre- and postmenopausal patients were randomly allocated chemotherapy for antiestrogen treatment. All patients were recurrence-free at time of examination. Clinically, 35% (25-47%) of the patients had RBP; 19% (11-29%) had definite RBP, i.e. were physically disabled, and 16% (9-26%) had probable RBP. Fifty percent (31-69%) had affection of the entire plexus, 18% (7-35%) of the upper trunk only, and 4% (1-18%) of the lower trunk. In 28% (14-48%) of cases assessment of a definite level was not possible. RBP was more common after radiotherapy and chemotherapy (42%) than after radiotherapy alone (26%) but the difference was not statistically significant (p = 0.10). The incidence of definite RBP was significantly higher in the younger age group (p = 0.02). This could be due to more extensive axillary surgery but also to the fact that chemotherapy was given to most premenopausal patients. In most patients with RBP the symptoms began during or immediately after radiotherapy, and were thus without significant latency. Chemotherapy might enhance the radiation-induced effect on nerve tissue, thus diminishing the latency period. Lymphedema was present in 22% (14-32%), especially in the older patients, and not associated with the development of RBP. In conclusion, the damaging effect of RT on peripheral nerve tissue was documented. Since no successful treatment is available, restricted use of RT to the brachial plexus is warranted, especially when administered concomitantly with cytotoxic therapy. (orig.).

Logistic regression applied to natural hazards: rare event logistic regression with replications

Science.gov (United States)

Guns, M.; Vanacker, V.

2012-06-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

Soil organic carbon distribution in Mediterranean areas under a climate change scenario via multiple linear regression analysis.

Science.gov (United States)

Olaya-Abril, Alfonso; Parras-Alcántara, Luis; Lozano-García, Beatriz; Obregón-Romero, Rafael

2017-08-15

Over time, the interest on soil studies has increased due to its role in carbon sequestration in terrestrial ecosystems, which could contribute to decreasing atmospheric CO 2 rates. In many studies, independent variables were related to soil organic carbon (SOC) alone, however, the contribution degree of each variable with the experimentally determined SOC content were not considered. In this study, samples from 612 soil profiles were obtained in a natural protected (Red Natura 2000) of Sierra Morena (Mediterranean area, South Spain), considering only the topsoil 0-25cm, for better comparison between results. 24 independent variables were used to define it relationship with SOC content. Subsequently, using a multiple linear regression analysis, the effects of these variables on the SOC correlation was considered. Finally, the best parameters determined with the regression analysis were used in a climatic change scenario. The model indicated that SOC in a future scenario of climate change depends on average temperature of coldest quarter (41.9%), average temperature of warmest quarter (34.5%), annual precipitation (22.2%) and annual average temperature (1.3%). When the current and future situations were compared, the SOC content in the study area was reduced a 35.4%, and a trend towards migration to higher latitude and altitude was observed. Copyright © 2017 Elsevier B.V. All rights reserved.

Stepwise versus Hierarchical Regression: Pros and Cons

Science.gov (United States)

Lewis, Mitzi

2007-01-01

Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements

KAUST Repository

Ryu, Duchwan

2010-09-28

We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.

Detrended fluctuation analysis as a regression framework: Estimating dependence at different scales

Czech Academy of Sciences Publication Activity Database

Krištoufek, Ladislav

2015-01-01

Roč. 91, č. 1 (2015), 022802-1-022802-5 ISSN 1539-3755 R&D Projects: GA ČR(CZ) GP14-11402P Grant - others:GA ČR(CZ) GAP402/11/0948 Program:GA Institutional support: RVO:67985556 Keywords : Detrended cross-correlation analysis * Regression * Scales Subject RIV: AH - Economics Impact factor: 2.288, year: 2014 http://library.utia.cas.cz/separaty/2015/E/kristoufek-0452315.pdf

MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY

OpenAIRE

Chayalakshmi C.L

2018-01-01

MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY ABSTRACT Calculation of boiler efficiency is essential if its parameters need to be controlled for either maintaining or enhancing its efficiency. But determination of boiler efficiency using conventional method is time consuming and very expensive. Hence, it is not recommended to find boiler efficiency frequently. The work presented in this paper deals with establishing the statistical mo...

Statistical learning method in regression analysis of simulated positron spectral data

International Nuclear Information System (INIS)

Avdic, S. Dz.

2005-01-01

Positron lifetime spectroscopy is a non-destructive tool for detection of radiation induced defects in nuclear reactor materials. This work concerns the applicability of the support vector machines method for the input data compression in the neural network analysis of positron lifetime spectra. It has been demonstrated that the SVM technique can be successfully applied to regression analysis of positron spectra. A substantial data compression of about 50 % and 8 % of the whole training set with two and three spectral components respectively has been achieved including a high accuracy of the spectra approximation. However, some parameters in the SVM approach such as the insensitivity zone e and the penalty parameter C have to be chosen carefully to obtain a good performance. (author)

RDE-4 preferentially binds long dsRNA and its dimerization is necessary for cleavage of dsRNA to siRNA.

Science.gov (United States)

Parker, Greg S; Eckert, Debra M; Bass, Brenda L

2006-05-01

In organisms ranging from Arabidopsis to humans, Dicer requires dsRNA-binding proteins (dsRBPs) to carry out its roles in RNA interference (RNAi) and micro-RNA (miRNA) processing. In Caenorhabditis elegans, the dsRBP RDE-4 acts with Dicer during the initiation of RNAi, when long dsRNA is cleaved to small interfering RNAs (siRNAs). RDE-4 is not required in subsequent steps, and how RDE-4 distinguishes between long dsRNA and short siRNA is unclear. We report the first detailed analysis of RDE-4 binding, using purified recombinant RDE-4 and various truncated proteins. We find that, similar to other dsRBPs, RDE-4 is not sequence-specific. However, consistent with its in vivo roles, RDE-4 binds with higher affinity to long dsRNA. We also observe that RDE-4 is a homodimer in solution, and that the C-terminal domain of the protein is required for dimerization. Using extracts from wild-type and rde-4 mutant C. elegans, we show that the C-terminal dimerization domain is required for the production of siRNA. Our findings suggest a model for RDE-4 function during the initiation of RNAi.

Regression: The Apple Does Not Fall Far From the Tree.

Science.gov (United States)

Vetter, Thomas R; Schober, Patrick

2018-05-15

Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

Regression Analysis

CERN Document Server

Freund, Rudolf J; Sa, Ping

2006-01-01

The book provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to determine, at least to some degree, the correct type of statistical analyses to be performed in a given situation, and have some appreciation of what constitutes good experimental design

Systematic review, meta-analysis, and meta-regression: Successful second-line treatment for Helicobacter pylori.

Science.gov (United States)

Muñoz, Neus; Sánchez-Delgado, Jordi; Baylina, Mireia; Puig, Ignasi; López-Góngora, Sheila; Suarez, David; Calvet, Xavier

2018-06-01

Multiple Helicobacter pylori second-line schedules have been described as potentially useful. It remains unclear, however, which are the best combinations, and which features of second-line treatments are related to better cure rates. The aim of this study was to determine that second-line treatments achieved excellent (>90%) cure rates by performing a systematic review and when possible a meta-analysis. A meta-regression was planned to determine the characteristics of treatments achieving excellent cure rates. A systematic review for studies evaluating second-line Helicobacter pylori treatment was carried out in multiple databases. A formal meta-analysis was performed when an adequate number of comparative studies was found, using RevMan5.3. A meta-regression for evaluating factors predicting cure rates >90% was performed using Stata Statistical Software. The systematic review identified 115 eligible studies, including 203 evaluable treatment arms. The results were extremely heterogeneous, with 61 treatment arms (30%) achieving optimal (>90%) cure rates. The meta-analysis favored quadruple therapies over triple (83.2% vs 76.1%, OR: 0.59:0.38-0.93; P = .02) and 14-day quadruple treatments over 7-day treatments (91.2% vs 81.5%, OR; 95% CI: 0.42:0.24-0.73; P = .002), although the differences were significant only in the per-protocol analysis. The meta-regression did not find any particular characteristics of the studies to be associated with excellent cure rates. Second-line Helicobacter pylori treatments achieving>90% cure rates are extremely heterogeneous. Quadruple therapy and 14-day treatments seem better than triple therapies and 7-day ones. No single characteristic of the treatments was related to excellent cure rates. Future approaches suitable for infectious diseases-thus considering antibiotic resistances-are needed to design rescue treatments that consistently achieve excellent cure rates. © 2018 John Wiley & Sons Ltd.

Multiple regression and beyond an introduction to multiple regression and structural equation modeling

CERN Document Server

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/.

Regression Association Analysis of Yield-Related Traits with RAPD Molecular Markers in Pistachio (Pistacia vera L.

Directory of Open Access Journals (Sweden)

Saeid Mirzaei

2017-10-01

Full Text Available Introduction: The pistachio (Pistacia vera, a member of the cashew family, is a small tree originating from Central Asia and the Middle East. The tree produces seeds that are widely consumed as food. Pistacia vera often is confused with other species in the genus Pistacia that are also known as pistachio. These other species can be distinguished by their geographic distributions and their seeds which are much smaller and have a soft shell. Continual advances in crop improvement through plant breeding are driven by the available genetic diversity. Therefore, the recognition and measurement of such diversity is crucial to breeding programs. In the past 20 years, the major effort in plant breeding has changed from quantitative to molecular genetics with emphasis on quantitative trait loci (QTL identification and marker assisted selection (MAS. The germplasm-regression-combined association studies not only allow mapping of genes/QTLs with higher level of confidence, but also allow detection of genes/QTLs, which will otherwise escape detection in linkage-based QTL studies based on the planned populations. The development of the marker-based technology offers a fast, reliable, and easy way to perform multiple regression analysis and comprise an alternative approach to breeding in diverse species of plants. The availability of many makers and morphological traits can help to regression analysis between these markers and morphological traits. Materials and Methods: In this study, 20 genotypes of Pistachio were studied and yield related traits were measured. Young well-expanded leaves were collected for DNA extraction and total genomic DNA was extracted. Genotyping was performed using 15 RAPD primers and PCR amplification products were visualized by gel electrophoresis. The reproducible RAPD fragments were scored on the basis of present (1 or absent (0 bands and a binary matrix constructed using each molecular marker. Association analysis between

Mathematical Modeling of Rotary Blood Pumps in a Pulsatile In Vitro Flow Environment.

Science.gov (United States)

Pirbodaghi, Tohid

2017-08-01

Nowadays, sacrificing animals to develop medical devices and receive regulatory approval has become more common, which increases ethical concerns. Although in vivo tests are necessary for development and evaluation of new devices, nonetheless, with appropriate in vitro setups and mathematical models, a part of the validation process can be performed using these models to reduce the number of sacrificed animals. The main aim of this study is to present a mathematical model simulating the hydrodynamic function of a rotary blood pump (RBP) in a pulsatile in vitro flow environment. This model relates the pressure head of the RBP to the flow rate, rotational speed, and time derivatives of flow rate and rotational speed. To identify the model parameters, an in vitro setup was constructed consisting of a piston pump, a compliance chamber, a throttle, a buffer reservoir, and the CentriMag RBP. A 40% glycerin-water mixture as a blood analog fluid and deionized water were used in the hydraulic circuit to investigate the effect of viscosity and density of the working fluid on the model parameters. First, model variables were physically measured and digitally acquired. Second, an identification algorithm based on regression analysis was used to derive the model parameters. Third, the completed model was validated with a totally different set of in vitro data. The model is usable for both mathematical simulations of the interaction between the pump and heart and indirect pressure measurement in a clinical context. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Adenoviral vaccination combined with CD40 stimulation and CTLA-4 blockage can lead to complete tumor regression in a murine melanoma model

DEFF Research Database (Denmark)

Sørensen, Maria Rathmann; Holst, Peter J; Steffensen, Maria Abildgaard

2010-01-01

that the delay in tumor growth can be converted to complete regression and long-term survival in 30-40% of the mice by a booster vaccination plus combinational treatment with agonistic anti-CD40 monoclonal antibodies (mAb) and anti-CTLA-4 mAb. Regarding the mechanism underlying the improved clinical effect......, analysis of the tumor-specific response revealed a significantly prolonged tumor-specific CD8 T cell response in spleens of the mice receiving the combinational treatment compared with mice receiving either treatment individually. Matching this, CD8 T cell depletion completely prevented tumor control...

Variable Selection for Regression Models of Percentile Flows

Science.gov (United States)

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

Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

Directory of Open Access Journals (Sweden)

ZHANG Long

2015-09-01

Full Text Available Near infrared reflectance spectroscopy (NIRS, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis (PLS-DA to discriminate the transgenic (TCTP and mi166 and wild type (Zhonghua 11 rice. Furthermore, rice lines transformed with protein gene (OsTCTP and regulation gene (Osmi166 were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000–8 000 cm-1 and 4 000–10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000–10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000–10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

Intermediate and advanced topics in multilevel logistic regression analysis.

Science.gov (United States)

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Global dengue death before and after the new World Health Organization 2009 case classification: A systematic review and meta-regression analysis.

Science.gov (United States)

Low, Gary Kim-Kuan; Ogston, Simon A; Yong, Mun-Hin; Gan, Seng-Chiew; Chee, Hui-Yee

2018-06-01

Since the introduction of 2009 WHO dengue case classification, no literature was found regarding its effect on dengue death. This study was to evaluate the effect of 2009 WHO dengue case classification towards dengue case fatality rate. Various databases were used to search relevant articles since 1995. Studies included were cohort and cross-sectional studies, all patients with dengue infection and must report the number of death or case fatality rate. The Joanna Briggs Institute appraisal checklist was used to evaluate the risk of bias of the full-texts. The studies were grouped according to the classification adopted: WHO 1997 and WHO 2009. Meta-regression was employed using a logistic transformation (log-odds) of the case fatality rate. The result of the meta-regression was the adjusted case fatality rate and odds ratio on the explanatory variables. A total of 77 studies were included in the meta-regression analysis. The case fatality rate for all studies combined was 1.14% with 95% confidence interval (CI) of 0.82-1.58%. The combined (unadjusted) case fatality rate for 69 studies which adopted WHO 1997 dengue case classification was 1.09% with 95% CI of 0.77-1.55%; and for eight studies with WHO 2009 was 1.62% with 95% CI of 0.64-4.02%. The unadjusted and adjusted odds ratio of case fatality using WHO 2009 dengue case classification was 1.49 (95% CI: 0.52, 4.24) and 0.83 (95% CI: 0.26, 2.63) respectively, compared to WHO 1997 dengue case classification. There was an apparent increase in trend of case fatality rate from the year 1992-2016. Neither was statistically significant. The WHO 2009 dengue case classification might have no effect towards the case fatality rate although the adjusted results indicated a lower case fatality rate. Future studies are required for an update in the meta-regression analysis to confirm the findings. Copyright © 2018 Elsevier B.V. All rights reserved.

Regression models of reactor diagnostic signals

International Nuclear Information System (INIS)

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)

Comparison of Classical Linear Regression and Orthogonal Regression According to the Sum of Squares Perpendicular Distances

OpenAIRE

KELEŞ, Taliha; ALTUN, Murat

2016-01-01

Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it wa...

Deletion of the Androgen Receptor in Adipose Tissue in Male Mice Elevates Retinol Binding Protein 4 and Reveals Independent Effects on Visceral Fat Mass and on Glucose Homeostasis

Science.gov (United States)

McInnes, Kerry J.; Smith, Lee B.; Hunger, Nicole I.; Saunders, Philippa T.K.; Andrew, Ruth; Walker, Brian R.

2012-01-01

Testosterone deficiency is epidemic in obese ageing males with type 2 diabetes, but the direction of causality remains unclear. Testosterone-deficient males and global androgen receptor (AR) knockout mice are insulin resistant with increased fat, but it is unclear whether AR signaling in adipose tissue mediates body fat redistribution and alters glucose homoeostasis. To investigate this, mice with selective knockdown of AR in adipocytes (fARKO) were generated. Male fARKO mice on normal diet had reduced perigonadal fat but were hyperinsulinemic and by age 12 months, were insulin deficient in the absence of obesity. On high-fat diet, fARKO mice had impaired compensatory insulin secretion and hyperglycemia, with increased susceptibility to visceral obesity. Adipokine screening in fARKO mice revealed a selective increase in plasma and intra-adipose retinol binding protein 4 (RBP4) that preceded obesity. AR activation in murine 3T3 adipocytes downregulated RBP4 mRNA. We conclude that AR signaling in adipocytes not only protects against high-fat diet–induced visceral obesity but also regulates insulin action and glucose homeostasis, independently of adiposity. Androgen deficiency in adipocytes in mice resembles human type 2 diabetes, with early insulin resistance and evolving insulin deficiency. PMID:22415878

Online Statistical Modeling (Regression Analysis) for Independent Responses

Science.gov (United States)

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.

A Simple Linear Regression Method for Quantitative Trait Loci Linkage Analysis With Censored Observations

OpenAIRE

Anderson, Carl A.; McRae, Allan F.; Visscher, Peter M.

2006-01-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using...

Logistic regression applied to natural hazards: rare event logistic regression with replications

Directory of Open Access Journals (Sweden)

M. Guns

2012-06-01

Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

Robust Methods for Moderation Analysis with a Two-Level Regression Model.

Science.gov (United States)

Yang, Miao; Yuan, Ke-Hai

2016-01-01

Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.

Oil and gas pipeline construction cost analysis and developing regression models for cost estimation

Science.gov (United States)

Thaduri, Ravi Kiran

In this study, cost data for 180 pipelines and 136 compressor stations have been analyzed. On the basis of the distribution analysis, regression models have been developed. Material, Labor, ROW and miscellaneous costs make up the total cost of a pipeline construction. The pipelines are analyzed based on different pipeline lengths, diameter, location, pipeline volume and year of completion. In a pipeline construction, labor costs dominate the total costs with a share of about 40%. Multiple non-linear regression models are developed to estimate the component costs of pipelines for various cross-sectional areas, lengths and locations. The Compressor stations are analyzed based on the capacity, year of completion and location. Unlike the pipeline costs, material costs dominate the total costs in the construction of compressor station, with an average share of about 50.6%. Land costs have very little influence on the total costs. Similar regression models are developed to estimate the component costs of compressor station for various capacities and locations.

Comparison of the rate of uptake and biologic effects of retinol added to human keratinocytes either directly to the culture medium or bound to serum retinol-binding protein

International Nuclear Information System (INIS)

Hodam, J.R.; St Hilaire, P.; Creek, K.E.

1991-01-01

Retinol circulates in the plasma bound to retinol-binding protein (RBP), but the mechanism by which retinol is transferred from RBP to target cells is not known. To study retinol delivery, human keratinocytes (HKc) were incubated with [3H]retinol added directly to the culture medium or bound to RBP and the uptake of [3H]retinol was determined at various times. During the first hour of incubation, the rate of [3H]retinol accumulation by HKc was about 40 times greater when the vitamin was added directly to the media rather than bound to RBP. Although maximal uptake of [3H]retinol added directly to the culture medium occurred at 3 h, the uptake of [3H]retinol from RBP was linear with time for at least 72 h. By 57 h, cell-associated [3H]retinol was the same whether it was added directly to the culture medium or bound to RBP. Excess unlabeled retinol or pretreatment of HKc with retinol had no effect on the uptake of [3H]retinol added directly to the culture medium or bound to RBP. Apo- but not holo-RBP was capable of competing with HKc for the uptake of [3H]retinol from RBP. No specific or saturable binding of 125I-labeled RBP to HKc cultured in the absence or the presence of retinol was found. The dose response of retinol inhibition of cholesterol sulfate synthesis and phorbol ester-induced ornithine decarboxylase activity or retinol modulation of keratin expression was the same whether the retinol was delivered to HKc bound to RBP or added directly to the medium. Our data support a mechanism for retinol delivery from RBP to HKc that does not involve cell-surface RBP receptors but instead suggest that the vitamin is first slowly released from RBP and then becomes cell-associated from the aqueous phase. This mechanism is consistent with the finding that HKc respond identically to retinol whether or not it is delivered to them bound to RBP

Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines.

Science.gov (United States)

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D; Hood, Darryl B; Skelton, Tyler

2013-02-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.

Framing an Nuclear Emergency Plan using Qualitative Regression Analysis

International Nuclear Information System (INIS)

Amy Hamijah Abdul Hamid; Ibrahim, M.Z.A.; Deris, S.R.

2014-01-01

Since the arising on safety maintenance issues due to post-Fukushima disaster, as well as, lack of literatures on disaster scenario investigation and theory development. This study is dealing with the initiation difficulty on the research purpose which is related to content and problem setting of the phenomenon. Therefore, the research design of this study refers to inductive approach which is interpreted and codified qualitatively according to primary findings and written reports. These data need to be classified inductively into thematic analysis as to develop conceptual framework related to several theoretical lenses. Moreover, the framing of the expected framework of the respective emergency plan as the improvised business process models are abundant of unstructured data abstraction and simplification. The structural methods of Qualitative Regression Analysis (QRA) and Work System snapshot applied to form the data into the proposed model conceptualization using rigorous analyses. These methods were helpful in organising and summarizing the snapshot into an ' as-is ' work system that being recommended as ' to-be' w ork system towards business process modelling. We conclude that these methods are useful to develop comprehensive and structured research framework for future enhancement in business process simulation. (author)

A new approach to nuclear reactor design optimization using genetic algorithms and regression analysis

International Nuclear Information System (INIS)

Kumar, Akansha; Tsvetkov, Pavel V.

2015-01-01

Highlights: • This paper presents a new method useful for the optimization of complex dynamic systems. • The method uses the strengths of; genetic algorithms (GA), and regression splines. • The method is applied to the design of a gas cooled fast breeder reactor design. • Tools like Java, R, and codes like MCNP, Matlab are used in this research. - Abstract: A module based optimization method using genetic algorithms (GA), and multivariate regression analysis has been developed to optimize a set of parameters in the design of a nuclear reactor. GA simulates natural evolution to perform optimization, and is widely used in recent times by the scientific community. The GA fits a population of random solutions to the optimized solution of a specific problem. In this work, we have developed a genetic algorithm to determine the values for a set of nuclear reactor parameters to design a gas cooled fast breeder reactor core including a basis thermal–hydraulics analysis, and energy transfer. Multivariate regression is implemented using regression splines (RS). Reactor designs are usually complex and a simulation needs a significantly large amount of time to execute, hence the implementation of GA or any other global optimization techniques is not feasible, therefore we present a new method of using RS in conjunction with GA. Due to using RS, we do not necessarily need to run the neutronics simulation for all the inputs generated from the GA module rather, run the simulations for a predefined set of inputs, build a multivariate regression fit to the input and the output parameters, and then use this fit to predict the output parameters for the inputs generated by GA. The reactor parameters are given by the, radius of a fuel pin cell, isotopic enrichment of the fissile material in the fuel, mass flow rate of the coolant, and temperature of the coolant at the core inlet. And, the optimization objectives for the reactor core are, high breeding of U-233 and Pu-239 in

Live Cell Genomics: RNA Exon-Specific RNA-Binding Protein Isolation.

Science.gov (United States)

Bell, Thomas J; Eberwine, James

2015-01-01

RNA-binding proteins (RBPs) are essential regulatory proteins that control all modes of RNA processing and regulation. New experimental approaches to isolate these indispensable proteins under in vivo conditions are needed to advance the field of RBP biology. Historically, in vitro biochemical approaches to isolate RBP complexes have been useful and productive, but biological relevance of the identified RBP complexes can be imprecise or erroneous. Here we review an inventive experimental to isolate RBPs under the in vivo conditions. The method is called peptide nucleic acid (PNA)-assisted identification of RBP (PAIR) technology and it uses cell-penetrating peptides (CPPs) to deliver photo-activatible RBP-capture molecule to the cytoplasm of the live cells. The PAIR methodology provides two significant advantages over the most commonly used approaches: (1) it overcomes the in vitro limitation of standard biochemical approaches and (2) the PAIR RBP-capture molecule is highly selective and adaptable which allows investigators to isolate exon-specific RBP complexes. Most importantly, the in vivo capture conditions and selectivity of the RBP-capture molecule yield biologically accurate and relevant RBP data.

Robust estimation for homoscedastic regression in the secondary analysis of case-control data

KAUST Repository

Wei, Jiawei; Carroll, Raymond J.; Mü ller, Ursula U.; Keilegom, Ingrid Van; Chatterjee, Nilanjan

2012-01-01

Primary analysis of case-control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case-control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case-control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case-control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.

Robust estimation for homoscedastic regression in the secondary analysis of case-control data

KAUST Repository

Wei, Jiawei

2012-12-04

Primary analysis of case-control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case-control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case-control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case-control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.

Variable and subset selection in PLS regression

DEFF Research Database (Denmark)

Høskuldsson, Agnar

2001-01-01

The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules.

Science.gov (United States)

Pang, Tiantian; Huang, Leidan; Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Gong, Xuehao; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules' 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively.

Interactions between cadmium and decabrominated diphenyl ether on blood cells count in rats—Multiple factorial regression analysis

International Nuclear Information System (INIS)

Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana

2017-01-01

The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200–240 g for 28 days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15 mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000 mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.

Science.gov (United States)

Vatcheva, Kristina P; Lee, MinJae; McCormick, Joseph B; Rahbar, Mohammad H

2016-04-01

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.

Testing contingency hypotheses in budgetary research: An evaluation of the use of moderated regression analysis

NARCIS (Netherlands)

Hartmann, Frank G.H.; Moers, Frank

1999-01-01

In the contingency literature on the behavioral and organizational effects of budgeting, use of the Moderated Regression Analysis (MRA) technique is prevalent. This technique is used to test contingency hypotheses that predict interaction effects between budgetary and contextual variables. This

Modified Regression Correlation Coefficient for Poisson Regression Model

Science.gov (United States)

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).

Clinical benefit from pharmacological elevation of high-density lipoprotein cholesterol: meta-regression analysis.

Science.gov (United States)

Hourcade-Potelleret, F; Laporte, S; Lehnert, V; Delmar, P; Benghozi, Renée; Torriani, U; Koch, R; Mismetti, P

2015-06-01

Epidemiological evidence that the risk of coronary heart disease is inversely associated with the level of high-density lipoprotein cholesterol (HDL-C) has motivated several phase III programmes with cholesteryl ester transfer protein (CETP) inhibitors. To assess alternative methods to predict clinical response of CETP inhibitors. Meta-regression analysis on raising HDL-C drugs (statins, fibrates, niacin) in randomised controlled trials. 51 trials in secondary prevention with a total of 167,311 patients for a follow-up >1 year where HDL-C was measured at baseline and during treatment. The meta-regression analysis showed no significant association between change in HDL-C (treatment vs comparator) and log risk ratio (RR) of clinical endpoint (non-fatal myocardial infarction or cardiac death). CETP inhibitors data are consistent with this finding (RR: 1.03; P5-P95: 0.99-1.21). A prespecified sensitivity analysis by drug class suggested that the strength of relationship might differ between pharmacological groups. A significant association for both statins (p<0.02, log RR=-0.169-0.0499*HDL-C change, R(2)=0.21) and niacin (p=0.02, log RR=1.07-0.185*HDL-C change, R(2)=0.61) but not fibrates (p=0.18, log RR=-0.367+0.077*HDL-C change, R(2)=0.40) was shown. However, the association was no longer detectable after adjustment for low-density lipoprotein cholesterol for statins or exclusion of open trials for niacin. Meta-regression suggested that CETP inhibitors might not influence coronary risk. The relation between change in HDL-C level and clinical endpoint may be drug dependent, which limits the use of HDL-C as a surrogate marker of coronary events. Other markers of HDL function may be more relevant. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

The estimation and prediction of the inventories for the liquid and gaseous radwaste systems using the linear regression analysis

International Nuclear Information System (INIS)

Kim, J. Y.; Shin, C. H.; Kim, J. K.; Lee, J. K.; Park, Y. J.

2003-01-01

The variation transitions of the inventories for the liquid radwaste system and the radioactive gas have being released in containment, and their predictive values according to the operation histories of Yonggwang(YGN) 3 and 4 were analyzed by linear regression analysis methodology. The results show that the variation transitions of the inventories for those systems are linearly increasing according to the operation histories but the inventories released to the environment are considerably lower than the recommended values based on the FSAR suggestions. It is considered that some conservation were presented in the estimation methodology in preparing stage of FSAR

Selenium Exposure and Cancer Risk: an Updated Meta-analysis and Meta-regression

Science.gov (United States)

Cai, Xianlei; Wang, Chen; Yu, Wanqi; Fan, Wenjie; Wang, Shan; Shen, Ning; Wu, Pengcheng; Li, Xiuyang; Wang, Fudi

2016-01-01

The objective of this study was to investigate the associations between selenium exposure and cancer risk. We identified 69 studies and applied meta-analysis, meta-regression and dose-response analysis to obtain available evidence. The results indicated that high selenium exposure had a protective effect on cancer risk (pooled OR = 0.78; 95%CI: 0.73–0.83). The results of linear and nonlinear dose-response analysis indicated that high serum/plasma selenium and toenail selenium had the efficacy on cancer prevention. However, we did not find a protective efficacy of selenium supplement. High selenium exposure may have different effects on specific types of cancer. It decreased the risk of breast cancer, lung cancer, esophageal cancer, gastric cancer, and prostate cancer, but it was not associated with colorectal cancer, bladder cancer, and skin cancer. PMID:26786590

[Logistic regression model of noninvasive prediction for portal hypertensive gastropathy in patients with hepatitis B associated cirrhosis].

Science.gov (United States)

Wang, Qingliang; Li, Xiaojie; Hu, Kunpeng; Zhao, Kun; Yang, Peisheng; Liu, Bo

2015-05-12

To explore the risk factors of portal hypertensive gastropathy (PHG) in patients with hepatitis B associated cirrhosis and establish a Logistic regression model of noninvasive prediction. The clinical data of 234 hospitalized patients with hepatitis B associated cirrhosis from March 2012 to March 2014 were analyzed retrospectively. The dependent variable was the occurrence of PHG while the independent variables were screened by binary Logistic analysis. Multivariate Logistic regression was used for further analysis of significant noninvasive independent variables. Logistic regression model was established and odds ratio was calculated for each factor. The accuracy, sensitivity and specificity of model were evaluated by the curve of receiver operating characteristic (ROC). According to univariate Logistic regression, the risk factors included hepatic dysfunction, albumin (ALB), bilirubin (TB), prothrombin time (PT), platelet (PLT), white blood cell (WBC), portal vein diameter, spleen index, splenic vein diameter, diameter ratio, PLT to spleen volume ratio, esophageal varices (EV) and gastric varices (GV). Multivariate analysis showed that hepatic dysfunction (X1), TB (X2), PLT (X3) and splenic vein diameter (X4) were the major occurring factors for PHG. The established regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4. The accuracy of model for PHG was 79.1% with a sensitivity of 77.2% and a specificity of 80.8%. Hepatic dysfunction, TB, PLT and splenic vein diameter are risk factors for PHG and the noninvasive predicted Logistic regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4.

Interpreting Bivariate Regression Coefficients: Going beyond the Average

Science.gov (United States)

Halcoussis, Dennis; Phillips, G. Michael

2010-01-01

Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…

Statistical analysis of sediment toxicity by additive monotone regression splines