Variable selection in linear-circular regression models.

Applications of circular regression models are ubiquitous in many disciplines, particularly in meteorology, biology and geology. In circular regression models, variable selection problem continues to be a remarkable open question. In this paper, we address variable selection in linear-circular regression models where uni-variate linear dependent and a mixed set of circular and linear independent variables constitute the data set. We consider Bayesian lasso which is a popular choice for variable selection in classical linear regression models. We show that Bayesian lasso in linear-circular regression models is not able to produce robust inference as the coefficient estimates are sensitive to the choice of hyper-prior setting for the tuning parameter. To eradicate the problem, we propose a robustified Bayesian lasso that is based on an empirical Bayes (EB) type methodology to construct a hyper-prior for the tuning parameter while using Gibbs Sampling. This hyper-prior construction is computationally more feasible than the hyper-priors that are based on correlation measures. We show in a comprehensive simulation study that Bayesian lasso with EB-GS hyper-prior leads to a more robust inference. Overall, the method offers an efficient Bayesian lasso for variable selection in linear-circular regression while reducing model complexity.

A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements.

Joint mixed modeling is an attractive approach for the analysis of a scalar response measured at a primary endpoint and longitudinal measurements on a covariate. In the standard Bayesian analysis of these models, measurement error variance and the variance/covariance of random effects are a priori modeled independently. The key point is that these variances cannot be assumed independent given the total variation in a response. This article presents a joint Bayesian analysis in which these variance terms are a priori modeled jointly. Simulations illustrate that analysis with multivariate variance prior in general lead to reduced bias (smaller relative bias) and improved efficiency (smaller interquartile range) in the posterior inference compared with the analysis with independent variance priors.

Factors affecting clinical pregnancy rates after IUI for the treatment of unexplained infertility and mild male subfertility.

OBJECTIVE: The aim of the present retrospective study was to evaluate intrauterine insemination (IUI) clinical experiences and to define the variables for predicting success. MATERIAL AND METHODS: The present study was an observational trial performed in a private IVF center on subfertile couples who had applied for treatment between 2002 and 2012, in which the data of 503 IUI cases were retrospectively reviewed. Couples who had been diagnosed with unexplained and mild male subfertility were included. The primary outcome measure was the clinical pregnancy rate in an attempt to form a predictive model for the odds of a clinical pregnancy. Recorded parameters were used to determine the prediction model. RESULTS: Utilizing univariate logistic regression analysis, clinical pregnancy was positively associated with the duration of infertility (OR=1.09, p=0.089), secondary infertility (OR=1.77, p=0.050), and +4 sperm motility after preparation (OR=1.03, p=0.091). Following an adjustment analysis involving a multivariate logistic regression, clinical pregnancy was still found to positively associate with secondary infertility (OR=2.51, p=0.008). CONCLUSION: IUI success in secondary infertile couples who were in the unexplained infertility and mild male subfertility groups was higher than that in primary infertile couples, and the chances of pregnancy increased as sperm numbers with +4 motility increased. It is difficult to concomitantly evaluate all these parameters and to determine a predictive parameter in IUI independent from other factors.

One-hour versus two-hour postprandial blood glucose measurement in women with gestational diabetes mellitus: which is more predictive?

The purpose of this study is to investigate postprandial 1-h (PP1) and 2-h (PP2) blood glucose measurements' correlation with adverse perinatal outcomes. This prospective cohort study consisted of 259 women with gestational diabetes mellitus. During each antenatal visit, HbA1c and fasting plasma glucose (FPG) as well as plasma glucose at PP1 and PP2 were analyzed. There were 144 patients on insulin therapy and 115 patients on diet therapy. A total of 531 blood glucose measurements were obtained at different gestational ages between 24 and 41 gestational weeks. PP2 plasma glucose measurements (but not PP1) were positively correlated with fetal macrosomia. But on adjusted analysis, neither PP1 nor PP2 measurements predicted perinatal complications. In addition to PP1 and PP2, neither FPG nor HbA1c were able to predict perinatal complications or fetal macrosomia when controlled for confounding factors except for a positive correlation between fetal macrosomia and HbA1c in patients on diet therapy. Postprandial 1-h and postprandial 2-h plasma glucose measurements were not superior to each other in predicting fetal macrosomia or perinatal complications. Based on our findings, it can be concluded that both methods may be suitable for follow-up as there are no clear advantages of one measurement over the other.

Effects of in utero cord blood collection on post-cesarean hemoglobin levels.

OBJECTIVE: To assess effects of in utero cord blood collection on postoperative hemoglobin, hematocrit levels. STUDY DESIGN: Elective cesarean deliveries in which cord blood was collected were compared with match paired elective cesarean deliveries without cord blood collection. Pre-post-operative hemoglobin and hematocrit level differences were compared between study groups with Student's t test. Multivariate regression models were used to address confounders. Correlation between volume of collected UCB and mean decrease in blood count parameters was analyzed. RESULTS: A total of 399 cesarean deliveries during a 12 months period were included in the analysis. Mean decrease in hemoglobin levels was 1.08g/dL (SD=1.0) in UCB collected group compared to 0.84g/dL (SD=1.0) in control group (p=0.002). Mean decrease in hematocrit levels was 3.1% (SD=3.4) in cord blood collected cesarean delivery group compared to 1.9% (SD=2.4) in control group (p=0.002). Univariate analysis has shown the collected UCB volume to be uncorrelated with the change in hemoglobin levels (r=0.013). Multivariate regression models, after adjusting for birth weight, age and number of prior cesarean, have shown the UCB collection to be significantly associated with the mean decrease in blood count parameters (estimate=0.23g/dL, t=-2.23, p=0.02). CONCLUSION: In utero UCB collection is associated with a small increase in bleeding of little clinical importance. Amount of UCB is not associated with amount of change in hemoglobin and hematocrit levels. In utero UCB collection seems to be safe for expectant mothers scheduled for low-risk cesarean delivery.

Interleukin-28 gene polymorphisms may contribute to HBsAg persistence and the development of HBeAg-negative chronic hepatitis B.

BACKGROUND & AIMS: Aim of this study was to investigate whether a potential association exists between several single nucleotide polymorphisms (SNPs) of the IL-28B gene (rs12979860, rs1188122, rs8099917, rs8105790, rs12980275) and HBsAg persistence. Further, a potential effect on the development of HBeAg-negative CHB vs. inactive HBsAg carrier state was assessed in a genotype D HBV cohort. A cohort of chronic HDV patients was also used to see if they behave differently compared to chronic HBV patients. METHODS: This study was conducted in three main patient cohorts: Group 1 consisted of 482 patients with HBsAg persistence. Of them 143 were inactive carriers, 94 had HBeAg-positive chronic hepatitis B (CHB) and 245 had anti-HBe-positive CHB. Group 2 represents spontaneously recovered HBV patients; they were anti-HBs and anti-HBc positive. Group 3 consisted of 176 chronic hepatitis delta (CHD) patients with antidelta and HDV-RNA positivity. DNA sequencing was performed for genotyping. RESULTS: When patients with HBsAg persistence were compared with spontaneously recovered patients, a significant difference was observed for rs8105790 (P < 0.0001), rs12980275 (P < 0.02). Patients who had the CC/TC genotype for rs8105790 (P < 0.0001) and AA genotype for 1188122 (P < 0.02) were more likely to be inactive HBsAg carriers, when inactive HBsAg carriers were compared with HBeAg-negative CHB patients. Comparison of CHD patients vs. recovered HBV patients was parallel to that of HBV persistence vs. recovered HBV with similar significant differences in same SNPs. CONCLUSION: These results suggest that IL-28B polymorphisms may contribute to HBsAg persistence and the development of the inactive HBsAg carrier state.

Cancer-testis gene expression is associated with the methylenetetrahydrofolate reductase 677 C>T polymorphism in non-small cell lung carcinoma.

BACKGROUND: Tumor-specific, coordinate expression of cancer-testis (CT) genes, mapping to the X chromosome, is observed in more than 60% of non-small cell lung cancer (NSCLC) patients. Although CT gene expression has been unequivocally related to DNA demethylation of promoter regions, the underlying mechanism leading to loss of promoter methylation remains elusive. Polymorphisms of enzymes within the 1-carbon pathway have been shown to affect S-adenosyl methionine (SAM) production, which is the sole methyl donor in the cell. Allelic variants of several enzymes within this pathway have been associated with altered SAM levels either directly, or indirectly as reflected by altered levels of SAH and Homocysteine levels, and altered levels of DNA methylation. We, therefore, asked whether the five most commonly occurring polymorphisms in four of the enzymes in the 1-carbon pathway associated with CT gene expression status in patients with NSCLC. METHODS: Fifty patients among a cohort of 763 with NSCLC were selected based on CT gene expression status and typed for five polymorphisms in four genes known to affect SAM generation by allele specific q-PCR and RFLP. RESULTS: We identified a significant association between CT gene expression and the MTHFR 677 CC genotype, as well as the C allele of the SNP, in this cohort of patients. Multivariate analysis revealed that the genotype and allele strongly associate with CT gene expression, independent of potential confounders. CONCLUSIONS: Although CT gene expression is associated with DNA demethylation, in NSCLC, our data suggests this is unlikely to be the result of decreased MTHFR function.

Impacts of salinity and fish-exuded kairomone on the survival and macromolecular profile of Daphnia pulex.

Global warming is already causing salinization of freshwater ecosystems located in semi-arid regions, including Turkey. Daphnids, which are important grazers on phytoplankton and a major food source for fish and invertebrates, are sensitive to not only changes in salinity levels, but also presence of predators. In this study, the interactive effect of salinity toxicity (abiotic factor) with predation pressure mimicked by the fish-exuded kairomone (biotic factor) and the effect of salt acclimation on daphnids were investigated. Impacts of these stressors on daphnid survival, life history and molecular profile were observed. The presence of the kairomone antagonistically alters the effect of salinity, as observed from the 24- and 48-h LC(50) values and survival results. Molecular findings provided solid evidence to this antagonism at even lower salt concentrations, for which antagonism was not evident with organismal data. Fish predation counterbalances the negative effect of salinity in terms of reserve energy density. Therefore, it is important to investigate multiple stressor effects in ecotoxicological bioassays complemented with molecular techniques. The single effect of increasing salinity resulted in increased mortality, decreased fecundity, and slower somatic growth in Daphnia, despite their acclimation to salinity. This insignificance of acclimation indicates that Daphnia do not have any physiological mechanisms to buffer the adverse effects of salinity, making it a very crucial factor. Salinity-induced reduction in population growth rate of freshwater keystone species Daphnia-despite acclimation-indicates that global warming-induced salinity may cascade through the food web and lead to dramatic environmental consequences in the structure of lake ecosystems.

Relationships between epidemiological features and tumor characteristics of breast cancer.

OBJECTIVES: Breast cancer is a histological, morphological and molecular heterogenous disease. Like clinical outcomes and prognoses of different subtypes, etiologies might also be different. Therefore, epidemiologic risk factors like sociologic, demographic, antropometric, reproductive, and menstrual factors can be considered as an entity reflected in tumor features. This study was planned to explore the relation between well known risk factors of breast cancer and histological and molecular features of the tumor. MATERIALS AND METHODS: Epidemiologic data for 250 breast cancer patients followed-up by our clinic and 250 healthy individuals without any diagnosis of malignancy were obtained. The data displaying a relation to breast cancer are age, height, weight, body mass index (BMI), place of birth and province, educational level, menstrual status, age of menarche and menopause, number of births, age at first childbirth, family history of breast cancer, history of smoking and hormone treatment, mammographic screening, and presence of benign lesions. The tumor characteristics of patients in the breast cancer group were recorded. RESULTS: Advanced age, nulliparity, low educational level, irregular mammographic screening, early menarche and late menopause, and high BMI in postmenopausal period were found to be related to increased breast cancer risk. Striking results in terms of the relation between epidemiological factors and tumor features were the early diagnosis of breast cancer in patients with regular mammographic screening. Tumor size was decreased with increased age and increased with increased BMI. Advanced age, prolonged lactation, increased number of births, and high education level were found to decrease axillary involvement. CONCLUSIONS: Multiparity still continues to be the strongest protective factor against breast cancer in our society. The decrease in menarche age may be an early sign of the increased breast cancer incidence. Women should be informed about the relation between postmenopausal obesity and breast cancer and encouraged to attend physical activity and exercise programmes. Regular physical examination and mammographic screening are protective against breast cancer.

Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions.

In modern genetic epidemiology studies, the association between the disease and a genomic region, such as a candidate gene, is often investigated using multiple SNPs. We propose a multilocus test of genetic association that can account for genetic effects that might be modified by variants in other genes or by environmental factors. We consider use of the venerable and parsimonious Tukey's 1-degree-of-freedom model of interaction, which is natural when individual SNPs within a gene are associated with disease through a common biological mechanism; in contrast, many standard regression models are designed as if each SNP has unique functional significance. On the basis of Tukey's model, we propose a novel but computationally simple generalized test of association that can simultaneously capture both the main effects of the variants within a genomic region and their interactions with the variants in another region or with an environmental exposure. We compared performance of our method with that of two standard tests of association, one ignoring gene-gene/gene-environment interactions and the other based on a saturated model of interactions. We demonstrate major power advantages of our method both in analysis of data from a case-control study of the association between colorectal adenoma and DNA variants in the NAT2 genomic region, which are well known to be related to a common biological phenotype, and under different models of gene-gene interactions with use of simulated data.

Case-control and case-only designs with genotype and family history data: estimating relative risk, residual familial aggregation, and cumulative risk.

In case-control studies of inherited diseases, participating subjects (probands) are often interviewed to collect detailed data about disease history and age-at-onset information in their family members. Genotype data are typically collected from the probands, but not from their relatives. In this article, we introduce an approach that combines case-control analysis of data on the probands with kin-cohort analysis of disease history data on relatives. Assuming a marginally specified multivariate survival model for joint risk of disease among family members, we describe methods for estimating relative risk, cumulative risk, and residual familial aggregation. We also describe a variation of the methodology that can be used for kin-cohort analysis of the family history data from a sample of genotyped cases only. We perform simulation studies to assess performance of the proposed methodologies with correct and mis-specified models for familial aggregation. We illustrate the proposed methodologies by estimating the risk of breast cancer from BRCA1/2 mutations using data from the Washington Ashkenazi Study.

Hepatitis C virus load and survival among injection drug users in the United States.

Persons chronically infected with hepatitis C virus (HCV), some of whom may be coinfected with HIV and human T-lymphotropic virus type II (HTLV-II), are at high risk for end-stage liver disease (ESLD). We evaluated whether ESLD death was associated with premorbid HCV RNA level or specific HCV protein antibodies among persons with or without HIV/HTLV-II coinfection in a cohort of 6,570 injection drug users who enrolled in 9 US cities between 1987 and 1991. We compared 84 ESLD descendents and 305 randomly selected cohort participants with detectable HCV RNA, stratified by sex, race, HIV, and HTLV-II strata. Relative hazard (RH) of ESLD death was derived from the proportional hazard model. Risk of ESLD death was unrelated to the intensity of antibodies against the HCV c-22(p), c-33(p), c-100(p), and NS5 proteins, individually or combined, but it increased with HCV RNA level (RH(adj) = 2.26 per log(10) IU/mL, 95% CI: 1.45-5.92). The association between HCV RNA level and ESLD death remained significant after adjustment for alcohol consumption (RH(adj) = 2.57 per log(10) IU/mL, 95% CI: 1.50-8.10). Deaths from AIDS (n = 45) and other causes (n = 43) were unrelated to HCV RNA (RH(adj)= 1.14 and 1.29 per log(10) IU/mL, respectively). HIV infection was not associated with ESLD risk in multivariate analyses adjusted for HCV RNA. Men had an increased risk of ESLD death in unadjusted analyses (RH = 1.92, 95% CI: 1.15-3.56) but not in multivariate analysis (RH(adj) = 0.98, 95% CI: 0.48-2.88). Non-black patients were at increased risk for ESLD death (RH(adj)= 2.76, 95% CI: 1.49-10.09). In conclusion, HCV RNA level is a predictor of ESLD death among persons with chronic HCV infection.

Exploiting gene-environment independence in family-based case-control studies: increased power for detecting associations, interactions and joint effects.

Family-based case-control studies are popularly used to study the effect of genes and gene-environment interactions in the etiology of rare complex diseases. We consider methods for the analysis of such studies under the assumption that genetic susceptibility (G) and environmental exposures (E) are independently distributed of each other within families in the source population. Conditional logistic regression, the traditional method of analysis of the data, fails to exploit the independence assumption and hence can be inefficient. Alternatively, one can estimate the multiplicative interaction between G and E more efficiently using cases only, but the required population-based G-E independence assumption is very stringent. In this article, we propose a novel conditional likelihood framework for exploiting the within-family G-E independence assumption. This approach leads to a simple and yet highly efficient method of estimating interaction and various other risk parameters of scientific interest. Moreover, we show that the same paradigm also leads to a number of alternative and even more efficient methods for analysis of family-based case-control studies when parental genotype information is available on the case-control study participants. Based on these methods, we evaluate different family-based study designs by examining their relative efficiencies to each other and their efficiencies compared to a population-based case-control design of unrelated subjects. These comparisons reveal important design implications. Extensions of the methodologies for dealing with complex family studies are also discussed.