The landscape of etiological patterns of hepatocellular carcinoma and intrahepatic cholangiocarcinoma in Thailand.

Thailand is among countries with the highest global incidence and mortality rates of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). While viral hepatitis and liver fluke infections have been associated with HCC and iCCA, respectively, other environmental risk factors, overall risk factor commonality and combinatorial roles, and effects on survival have not been systematically examined. We conducted a TIGER-LC consortium-based population study covering all high-incidence areas of both malignancies across Thailand: 837 HCC, 1474 iCCA, and 1112 controls (2011-2019) were comprehensively queried on lifelong environmental exposures, lifestyle, and medical history. Multivariate logistic regression and Cox proportional hazards analyses were used to evaluate risk factors and associated survival patterns. Our models identified shared risk factors between HCC and iCCA, such as viral hepatitis infection, liver fluke infection, and diabetes, including novel and shared associations of agricultural pesticide exposure (OR range of 1.50; 95% CI: 1.06-2.11 to 2.91; 95% CI: 1.82-4.63) along with vulnerable sources of drinking water. Most patients had multiple risk factors, magnifying their risk considerably. Patients with lower risk levels had better survival in both HCC (HR 0.78; 95% CI: 0.64-0.96) and iCCA (HR 0.84; 95% CI: 0.70-0.99). Risk factor co-exposures and their common associations with HCC and iCCA in Thailand emphasize the importance for future prevention and control measures, especially in its large agricultural sector. The observed mortality patterns suggest ways to stratify patients for anticipated survivorship and develop plans to support medical care of longer-term survivors, including behavioral changes to reduce exposures.

Changes in smoking use and subsequent lung cancer risk in the ATBC study.

BACKGROUND: Reducing cigarettes/day may lower the risk of lung cancer compared with continuing to smoke at the same intensity. Other changes in smoking behaviors, such as increasing cigarette consumption or quitting for a period and relapsing, may also affect lung cancer risk. METHODS: We examined changes in smoking status and cigarettes/day among 24,613 Finnish male smokers aged 50-69 years who participated in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Longitudinal data on smoking were collected during study follow-up visits three times a year (approximately every 4 months) between 1985 and 1993. Incident lung cancer cases through 2012 were identified by the Finnish Cancer Registry. Risk ratios (RRs) and 95% confidence intervals (95%CI) were estimated using Cox proportional hazards regression. RESULTS: Compared with smoking 20 cigarettes/day continuously across the intervention period, reducing an average of 5 cigarettes/day per year while smoking was associated with a 20% lower risk of lung cancer (95%CI : 0.71 to 0.90). A substantially lower risk of lung cancer was also observed when participants smoked at 50% (RR = 0.72; 95%CI : 0.57-0.90) and 10% (RR = 0.55; 95%CI : 0.36-0.83) of study visits, relative to smoked at 100% of study visits. CONCLUSIONS: Smokers may lower their risk of lung cancer by reducing smoking intensity (cigarettes per day while smoking) and the time they smoke. However, quitting smoking completely is the most effective way for smokers to reduce their risk of lung cancer.

A latent functional approach for modeling the effects of multidimensional exposures on disease risk.

Understanding the relationships between exposure and disease incidence is an important problem in environmental epidemiology. Typically, a large number of these exposures are measured, and it is found either that a few exposures transmit risk or that each exposure transmits a small amount of risk, but, taken together, these may pose a substantial disease risk. Further, these exposure effects can be nonlinear. We develop a latent functional approach, which assumes that the individual effect of each exposure can be characterized as one of a series of unobserved functions, where the number of latent functions is less than or equal to the number of exposures. We propose Bayesian methodology to fit models with a large number of exposures and show that existing Bayesian group LASSO approaches are a special case of the proposed model. An efficient Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian inference. The deviance information criterion is used to choose an appropriate number of nonlinear latent functions. We demonstrate the good properties of the approach using simulation studies. Further, we show that complex exposure relationships can be represented with only a few latent functional curves. The proposed methodology is illustrated with an analysis of the effect of cumulative pesticide exposure on cancer risk in a large cohort of farmers.

Uncovering circadian rhythms in metabolic longitudinal data: A Bayesian latent class modeling approach.

Researchers in biology and medicine have increasingly focused on characterizing circadian rhythms and their potential impact on disease. Understanding circadian variation in metabolomics, the study of chemical processes involving metabolites may provide insight into important aspects of biological mechanism. Of scientific importance is developing a statistical rigorous approach for characterizing different types of 24-hour patterns among high dimensional longitudinal metabolites. We develop a latent class approach to incorporate variation in 24-hour patterns across metabolites where profiles are modeled with finite mixtures of distinct shape-invariant circadian curves that themselves incorporate variation in amplitude and phase across metabolites. An efficient Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. When the model was fit separately by individual to the data from a small group of participants, two distinct 24-hour rhythms were identified, with one being sinusoidal and the other being more complex with multiple peaks. Interestingly, the latent pattern associated with circadian variation (simple sinusoidal curve) had a similar phase across the three participants, while the more complex latent pattern reflecting diurnal variation differed across individual. The results suggested that this modeling framework can be used to separate 24-hour rhythms into an endogenous circadian and one or more exogenous diurnal patterns in describing human metabolism.

metapack: An R Package for Bayesian Meta-Analysis and Network Meta-Analysis with a Unified Formula Interface.

Meta-analysis, a statistical procedure that compares, combines, and synthesizes research findings from multiple studies in a principled manner, has become popular in a variety of fields. Meta-analyses using study-level (or equivalently aggregate) data are of particular interest due to data availability and modeling flexibility. In this paper, we describe an R package metapack that introduces a unified formula interface for both meta-analysis and network meta-analysis. The user interface-and therefore the package-allows flexible variance-covariance modeling for multivariate meta-analysis models and univariate network meta-analysis models. Complicated computing for these models has prevented their widespread adoption. The package also provides functions to generate relevant plots and perform statistical inferences like model assessments. Use cases are demonstrated using two real data sets contained in metapack.

Identification of novel candidate genes implicated in odontogenic potential in the developing mouse tooth germ using transcriptome analysis.

BACKGROUND: In tooth bioengineering for replacement therapy of missing teeth, the utilized cells must possess an inductive signal-forming ability to initiate odontogenesis. This ability is called odontogenic potential. In mice, the odontogenic potential signal is known to be translocated from the epithelium to the mesenchyme at the early bud stage in the developing molar tooth germ. However, the identity of the molecular constituents of this process remains unclear. OBJECTIVE: The purpose of this study is to determine the molecular identity of odontogenic potential and to provide a new perspective in the field of tooth development research. METHODS: In this study, whole transcriptome profiles of the mouse molar tooth germ epithelium and mesenchyme were investigated using the RNA sequencing (RNA-seq) technique. The analyzed transcriptomes corresponded to two developmental stages, embryonic day 11.5 (E11.5) and 14.5 (E14.5), which represent the odontogenic potential shifts. RESULTS: We identified differentially expressed genes (DEGs), which were specifically overexpressed in both the E11.5 epithelium and E14.5 mesenchyme, but not expressed in their respective counterparts. Of the 55 DEGs identified, the top three most expressed transcription factor genes (transcription factor AP-2 beta isoform 3 [TFAP2B], developing brain homeobox protein 2 [DBX2], and insulin gene enhancer protein ISL-1 [ISL1]) and three tooth development-related genes (transcription factor HES-5 [HES5], platelet-derived growth factor D precursor [PDGFD], semaphrin-3 A precursor [SEMA3A]) were selected and validated by quantitative RT-PCR. Using immunofluorescence staining, the TFAP2B protein expression was found to be localized only at the E11.5 epithelium and E14.5 mesenchyme. CONCLUSIONS: Thus, our empirical findings in the present study may provide a new perspective into the characterization of the molecules responsible for the odontogenic potential and may have an implication in the cell-based whole tooth regeneration strategy.

IFN-λ4 is associated with increased risk and earlier occurrence of several common infections in African children.

Genetic polymorphisms within the IFNL3/IFNL4 genomic region, which encodes type III interferons, have been strongly associated with clearance of hepatitis C virus. We hypothesized that type III interferons might be important for the immune response to other pathogens as well. In a cohort of 914 Malian children, we genotyped functional variants IFNL4-rs368234815, IFNL4-rs117648444, and IFNL3-rs4803217 and analyzed episodes of malaria, gastrointestinal, and respiratory infections recorded at 30,626 clinic visits from birth up to 5 years of age. Compared to children with the rs368234815-TT/TT genotype (IFN-λ4-Null), rs368234815-dG allele was most strongly associated with an earlier time-to-first episode of gastrointestinal infections (p = 0.003). The risk of experiencing an infection episode during the follow-up was also significantly increased with rs368234815-dG allele, with OR = 1.53, 95%CI (1.13-2.07), p = 0.005 for gastrointestinal infections and OR = 1.30, 95%CI (1.02-1.65), p = 0.033 for malaria. All the associations for the moderately linked rs4803217 (r2 = 0.78 in this set) were weaker and lost significance after adjusting for rs368234815. We also analyzed all outcomes in relation to IFN-λ4-P70S groups. Our results implicate IFN-λ4 and not IFN-λ3 as the primary functional cause of genetic associations with increased overall risk and younger age at first clinical episodes but not with recurrence or intensity of several common pediatric infections.

Bayesian network meta-regression hierarchical models using heavy-tailed multivariate random effects with covariate-dependent variances.

Network meta-analysis (NMA) is gaining popularity in evidence synthesis and network meta-regression allows us to incorporate potentially important covariates into network meta-analysis. In this article, we propose a Bayesian network meta-regression hierarchical model and assume a general multivariate t distribution for the random treatment effects. The multivariate t distribution is desired for heavy-tailed random effects and converges to the multivariate normal distribution when the degrees of freedom go to infinity. Moreover, in NMA, some treatments are compared only in a single study. To overcome such sparsity, we propose a log-linear regression model for the variances of the random effects and incorporate aggregate covariates into modeling the variance components. We develop a Markov chain Monte Carlo sampling algorithm to sample from the posterior distribution via the collapsed Gibbs technique. We further use the deviance information criterion and the logarithm of the pseudo-marginal likelihood for model comparison. A simulation study is conducted and a detailed analysis from our motivating case study is carried out to further demonstrate the proposed methodology.

Methylated DNA Markers of Esophageal Squamous Cancer and Dysplasia: An International Study.

BACKGROUND: Discovery of methylated DNA markers (MDM) of esophageal squamous cell carcinoma (ESCC) has sparked interest in assessing these markers in tissue. We evaluated MDMs in ESCC from three geographically and ethnically distinct populations, and explored the feasibility of assaying MDMs from DNA obtained by swallowed balloon devices. METHODS: MDMs were assayed in ESCC and normal tissues obtained from the populations of United States, Iran, and China, and from exfoliative cytology specimens obtained by balloons in a Chinese population. Areas under the receiver operating curve (AUC) of MDMs discriminating ESCC from normal tissues were calculated. Random forest prediction models were built, trained on U.S. cases and controls, and calibrated to U.S.-only controls (model 1) and three-country controls (model 2). Statistical tests were used to assess the relationship between dysplasia and MDM levels in balloons. RESULTS: Extracted DNA from 333 ESCC and 322 normal tissues was analyzed, in addition to archival DNA from 98 balloons. For ESCC, model 1 validated in Iranian and Chinese tissues with AUCs of 0.90 and 0.87, and model 2 yielded AUCs of 0.99, 0.96, and 0.94 in tissues from the United States, Iran, and China, respectively. In Chinese balloons, MDMs showed a statistically significant trend of increasing levels with increasing grades of dysplasia (P < 0.004). CONCLUSIONS: MDMs accurately discriminate ESCC from normal esophagus in tissues obtained from high- and low-incidence countries. Preliminary data suggest that levels of MDMs assayed in DNA from swallowed balloon devices increase with dysplasia grade. Larger studies are needed to validate these results. IMPACT: MDMs coupled with minimally invasive collection methods have the potential for worldwide application in ESCC screening.

Bayesian flexible hierarchical skew heavy-tailed multivariate meta regression models for individual patient data with applications.

A flexible class of multivariate meta-regression models are proposed for Individual Patient Data (IPD). The methodology is well motivated from 26 pivotal Merck clinical trials that compare statins (cholesterol lowering drugs) in combination with ezetimibe and statins alone on treatment-naïve patients and those continuing on statins at baseline. The research goal is to jointly analyze the multivariate outcomes, Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG). These three continuous outcome measures are correlated and shed much light on a subject's lipid status. The proposed multivariate meta-regression models allow for different skewness parameters and different degrees of freedom for the multivariate outcomes from different trials under a general class of skew t-distributions. The theoretical properties of the proposed models are examined and an efficient Markov chain Monte Carlo (MCMC) sampling algorithm is developed for carrying out Bayesian inference under the proposed multivariate meta-regression model. In addition, the Conditional Predictive Ordinates (CPOs) are computed via an efficient Monte Carlo method. Consequently, the logarithm of the pseudo marginal likelihood and Bayesian residuals are obtained for model comparison and assessment, respectively. A detailed analysis of the IPD meta data from the 26 Merck clinical trials is carried out to demonstrate the usefulness of the proposed methodology.

Glycaemic status during pregnancy and longitudinal measures of fetal growth in a multi-racial US population: a prospective cohort study.

BACKGROUND: The timepoint at which fetal growth begins to differ by maternal glycaemic status is not well understood. To address this lack of data, we examined gestational diabetes, impaired glucose tolerance, and early pregnancy glucose concentrations in relation to fetal growth trajectories. METHODS: This cohort study included 2458 pregnant women from the NICHD Fetal Growth Studies-Singletons study, which took place between 2009 and 2013. Women were recruited from 12 clinical centres in the USA. Women aged 18-40 years without major chronic conditions when entering pregnancy were included and those with records of neither glucose screening test or glucose tolerance test were excluded from the study. Women were enrolled at gestational weeks 8-13 and randomly assigned to four ultrasonogram schedules (Group A; weeks 16, 24, 30, 34; Group B: weeks 18, 26, 31, 35, 39; Group C: weeks 20, 28, 32, 36; Group D: weeks 22, 29, 33, 37, 41) to capture weekly fetal growth. Gestational diabetes, impaired glucose tolerance, and normal glucose tolerance were defined by medical record review. Glucose was measured in a subsample of women at weeks 10-14. We modelled fetal growth trajectories using linear mixed models with cubic splines. This study is registered with ClinicalTrials.gov, NCT00912132. FINDINGS: Of the 2458 women included in this study, 107 (4·4%) had gestational diabetes, 118 (4·8%) had impaired glucose tolerance, and 2020 (82·2%) had NGT. 213 women were excluded from the main analysis. The cohort with gestational diabetes was associated with a larger estimated fetal weight that started at week 20 and was significant at week 28-40 (at week 37: 3061 g [95% CI 2967-3164] for women with gestational diabetes vs 2943 g [2924-2962] for women with normal glucose tolerance, adjusted p=0·02). In addition, glucose levels at weeks 10-14 were positively associated with estimated fetal weight starting at week 23 and the association became significant at week 27 (at week 37: 3073 g [2983-3167] in the highest tertile vs 2853 g [2755-2955] in the lowest tertile, adjusted p=0·0009. INTERPRETATION: Gestational diabetes was associated with a larger fetal size that started at week 20 and became significant at gestational week 28. Efforts to mitigate gestational diabetes-related fetal overgrowth should start before 24-28 gestational weeks, when gestational diabetes is typically screened for in the USA. FUNDING: National Institutes of Health.

Fetal growth patterns in pregnancy-associated hypertensive disorders: NICHD Fetal Growth Studies.

BACKGROUND: Fetal growth patterns in pregnancy-associated hypertensive disorders is poorly understood because prospective longitudinal data are lacking. OBJECTIVE: The objective of the study was to compare longitudinal fetal growth trajectories between normotensive women and those with pregnancy-associated hypertensive disorders. STUDY DESIGN: This is a study based on data from a prospective longitudinal cohort study of fetal growth performed at 12 US sites (2009-2013). Project gestational age was confirmed by ultrasound between 8 weeks 0 days and 13 weels 6 days, and up to 6 ultrasounds were performed across gestation. Hypertensive disorders were diagnosed based on 2002 American College of Obstetricians and Gynecologists guidelines and grouped hierarchically as severe preeclampsia (including eclampsia or HELLP [hemolysis, elevated liver enzymes, and low platelet count] syndrome), mild preeclampsia, severe gestational hypertension, mild gestational hypertension, or unspecified hypertension. Women without any hypertensive disorder constituted the normotensive group. Growth curves for estimated fetal weight and individual biometric parameters including biparietal diameter, head circumference, abdominal circumference, and femur and humerus length were calculated for each group using linear mixed models with cubic splines. Global and weekly pairwise comparisons were performed between women with a hypertensive disorder compared with normotensive women to analyze differences while adjusting for confounding variables. Delivery gestational age and birthweights were compared among groups. RESULTS: Of 2462 women analyzed, 2296 (93.3%) were normotensive, 63 (2.6%) had mild gestational hypertension, 54 (2.2%) mild preeclampsia, 32 (1.3%) severe preeclampsia, and 17 (0.7%) unspecified hypertension. Compared with normotensive women, those with severe preeclampsia had estimated fetal weights that were reduced between 22 and 38 weeks (all weekly pairwise values of P < .008). Women with severe preeclampsia compared with those without hypertension also had significantly smaller fetal abdominal circumference between 23-31 and 33-37 weeks' gestation (weekly pairwise values of P < .04). Scattered weekly growth differences were noted on other biometric parameters between these 2 groups. The consistent differences in estimated fetal weight and abdominal circumference were not observed between women with other hypertensive disorders and those who were normotensive. Women with severe preeclampsia delivered significantly earlier (mean gestational age 35.9 ± 3.2 weeks) than the other groups (global P < .0001). Birthweights in the severe preeclampsia group were also significantly lower (mean -949.5 g [95% confidence interval, -1117.7 to -781.2 g]; P < .0001) than in the normotensive group. CONCLUSION: Among women with pregnancy-associated hypertensive disorders, only those destined to develop severe preeclampsia demonstrated a significant and consistent difference in fetal growth (ie, smaller estimated fetal weight and abdominal circumference) when compared with normotensive women.

Non-Hodgkin lymphoma risk and organophosphate and carbamate insecticide use in the north American pooled project.

Organophosphates and carbamates have been among the most commonly used insecticides, with both agricultural and residential uses. Previous studies have suggested associations of non-Hodgkin lymphoma (NHL) with some of these chemicals; however, many studies have been limited in their ability to evaluate associations with lymphoma subtypes. We evaluated the use of eleven organophosphate and two carbamate insecticides in association with NHL in the North American Pooled Project, which includes data from case-control studies in the United States and Canada (1690 cases/5131 controls). We used unconditional logistic regression adjusting for potential confounders, including use of other pesticides, to estimate odds ratios (OR) and 95% confidence intervals (CI) for associations between these chemicals and NHL overall, and NHL subtypes, i.e., follicular (FL), diffuse large B-cell (DLBCL), small lymphocytic lymphoma (SLL) and others. Ever use of malathion was associated with increased risk of NHL overall (OR = 1.43; 95% CI: 1.14-1.81) compared with never users. Categories using tertiles of duration (<4 yrs., 4-12 yrs., and >12 yrs) also showed a significant exposure-response for increasing years of use of malathion and risk of NHL (OR<4vsUnex = 1.33 (0.88, 2.03), OR4-12vsUnex = 1.42 (1.02, 1.96), OR>12vsUnex = 1.55 (1.05, 2.28, p-trend < 0.01)). In addition, malathion use was statistically significantly associated with FL (OR = 1.58; 95% CI: 1.11-2.27) and DLBCL (OR = 1.61; 95% CI: 1.16-2.22) while there were no apparent associations with SLL or other subtypes, the p-value for heterogeneity across subtypes, however, was not significant. These results support previous studies suggesting an association between insecticide use and NHL overall, and provide new information on associations with NHL subtypes.

A contemporary amniotic fluid volume chart for the United States: The NICHD Fetal Growth Studies-Singletons.

BACKGROUND: Amniotic fluid is essential to normal fetal development and is estimated clinically with ultrasound scanning to identify pregnancies that are at risk for poor perinatal outcome. OBJECTIVE: Our goal was to develop a United States standard for amniotic fluid volume that is estimated by the amniotic fluid index and single deepest pocket. STUDY DESIGN: We performed a planned secondary analysis of a multicenter observational study of 2334 low-risk women with normal singleton gestations from 1 of 4 self-reported racial/ethnic groups. Eligible women had confirmed first-trimester dating criteria with health status, lifestyles, and medical and obstetric histories that were associated with normal fetal growth. Consenting women underwent serial (up to 5) sonographic evaluations of amniotic fluid between 15 and 40 weeks of gestation after being assigned randomly to 1 of 4 gestational age observation schedules. Twelve United States perinatal centers participated, and all sonograms were performed by credentialed sonographers who used identical, high-resolution equipment; caregivers were unaware of results but were notified for oligohydramnios. Women (n=597) who were subsequently found to have clinically significant antepartum complications were excluded. Racial/ethnic-specific nomograms for amniotic fluid index and single deepest pocket across gestation were developed with the use of linear mixed models with cubic splines; racial/ethnic differences were evaluated both with global and between-group tests. Median, 3rd, 5th, 10th, 90th, 95th and 97th percentile values were also estimated. We further considered the possible confounding effects of selected maternal characteristics and the estimated fetal weight at each sonogram. RESULTS: A total of 1719 pregnant women met inclusion criteria and had available data. These included 480 non-Hispanic white women, 418 non-Hispanic black women, 485 Hispanic women, and 336 Asian women. Both the amniotic fluid index and the single deepest pocket varied across gestation with maximal values at 26 and 33 weeks of gestation, respectively. Statistically significant differences were observed by maternal race/ethnicity. The between-group differences that were observed at 17-22 and 35-40 weeks of gestation remained statistically significant after adjustment for maternal characteristics and estimated fetal weight. These between-group racial/ethnic differences were most prominent after 35 weeks of gestation and at the extremes of dispersion (3rd and 97th percentiles). All 3rd and 97th percentile amniotic fluid index values were within the range of commonly used cutoffs to define oligohydramnios (≤5 cm) and polyhydramnios (≥25 cm). However, the 3rd percentile values ranged between 5.9 cm at 40 weeks of gestation and 10.1 cm at 25-27 weeks of gestation; the 97th percentile values ranged between 24.8 cm at 38 weeks of gestation and 15.7 cm at 15 weeks of gestation. CONCLUSION: Sonographic amniotic fluid volume estimates vary by racial/ethnic group, but the absolute differences appear to be small and may not be clinically significant. Selected maternal characteristics and estimated fetal weight did not affect the racial/ethnic differences. Between-group differences are maximal after 35 weeks of gestation and at the extremes of the upper and lower dispersion estimates. Given the observed variability in extreme (3rd and 97th percentile) dispersion values over the gestation, use of single cutoffs to define out-of-range measurements may not be appropriate clinically. These data might form a contemporary United States standard for amniotic fluid estimation that uses the amniotic fluid index and the single deepest pocket.

Bayesian multivariate skew meta-regression models for individual patient data.

We examine a class of multivariate meta-regression models in the presence of individual patient data. The methodology is well motivated from several studies of cholesterol-lowering drugs where the goal is to jointly analyze the multivariate outcomes, low density lipoprotein cholesterol, high density lipoprotein cholesterol, and triglycerides. These three continuous outcome measures are correlated and shed much light on a subject's lipid status. One of the main goals in lipid research is the joint analysis of these three outcome measures in a meta-regression setting. Since these outcome measures are not typically multivariate normal, one must consider classes of distributions that allow for skewness in one or more of the outcomes. In this paper, we consider a new general class of multivariate skew distributions for multivariate meta-regression and examine their theoretical properties. Using these distributions, we construct a Bayesian model for the meta-data and develop an efficient Markov chain Monte Carlo computational scheme for carrying out the computations. In addition, we develop a multivariate L measure for model comparison, Bayesian residuals for model assessment, and a Bayesian procedure for detecting outlying trials. The proposed multivariate L measure, Bayesian residuals, and Bayesian outlying trial detection procedure are particularly suitable and computationally attractive in the multivariate meta-regression setting. A detailed case study demonstrating the usefulness of the proposed methodology is carried out in an individual patient data multivariate meta-regression setting using 26 pivotal Merck clinical trials that compare statins (cholesterol-lowering drugs) in combination with ezetimibe and statins alone on treatment-naïve patients and those continuing on statins at baseline.

A correlated Bayesian rank likelihood approach to multiple ROC curves for endometriosis.

In analysis of diagnostic data with multiple tests, it is often the case that these tests are correlated. Modeling the correlation explicitly not only produces valid inference results but also enables borrowing of information. Motivated by the Physician Reliability Study (PRS) that investigated the diagnostic performance of physicians in diagnosing endometriosis, we construct a correlated modeling framework to estimate ROC curves and the associated area under the curves. This correlated approach is quite appealing for the PRS data set that suffers from the problem of small sample sizes, as it enables information borrowing between physician groups and sessions. Given that the test scores appear to be non-normal even after logarithm transformation, we use the ranks of the data to conduct likelihood estimation and inference. We use the deviance information criterion to select competing models and conduct simulation studies to assess model performances. In application to the PRS data set, we found that the physicians are not significantly different in their diagnostic performance between groups; however, they are different between the sessions. This suggests that clinical information may play a more important role in physicians' diagnostic performance than their experiences. Our empirical evidence also demonstrates that when using both woman- and physician-specific random effects, the model parameter estimates are much smoother.

Bayesian inference for network meta-regression using multivariate random effects with applications to cholesterol lowering drugs.

Low-density lipoprotein cholesterol (LDL-C) has been identified as a causative factor for atherosclerosis and related coronary heart disease, and as the main target for cholesterol- and lipid-lowering therapy. Statin drugs inhibit cholesterol synthesis in the liver and are typically the first line of therapy to lower elevated levels of LDL-C. On the other hand, a different drug, Ezetimibe, inhibits the absorption of cholesterol by the small intestine and provides a different mechanism of action. Many clinical trials have been carried out on safety and efficacy evaluation of cholesterol lowering drugs. To synthesize the results from different clinical trials, we examine treatment level (aggregate) network meta-data from 29 double-blind, randomized, active, or placebo-controlled statins +/$-$ Ezetimibe clinical trials on adult treatment-naïve patients with primary hypercholesterolemia. In this article, we propose a new approach to carry out Bayesian inference for arm-based network meta-regression. Specifically, we develop a new strategy of grouping the variances of random effects, in which we first formulate possible sets of the groups of the treatments based on their clinical mechanisms of action and then use Bayesian model comparison criteria to select the best set of groups. The proposed approach is especially useful when some treatment arms are involved in only a single trial. In addition, a Markov chain Monte Carlo sampling algorithm is developed to carry out the posterior computations. In particular, the correlation matrix is generated from its full conditional distribution via partial correlations. The proposed methodology is further applied to analyze the network meta-data from 29 trials with 11 treatment arms.

Diurnal variation of metabolites in three individual participants.

The circadian system influences virtually all biological functions. Understanding the impact of circadian variation on metabolism may provide insight into mechanisms of circadian-associated disorders and guide the implementation of chrono-therapy. Previous research has reported circadian variation in 7-20% of metabolites in human blood. In this study, untargeted metabolomics profiles were measured using blood of two healthy men and one healthy woman, collected every 2 h for up to 48 h under carefully controlled conditions. The pattern of variation of each metabolite over time was examined on each participant separately, using both one- and two-order harmonic models. A total of 100 of 663 metabolites, representing all metabolite categories, showed diurnal rhythmic concentrations that exceeded the Bonferroni threshold (P < 2.5 × 10-5). Overall, peak times of many metabolites were clustered during the afternoon-midnight, including the majority of amino acids, all peptides, all lysolipids and all phospholipids, whereas the majority of steroids peaked in the morning. We observed substantial inter-individual variation for both peak times and amplitudes in these three subjects. In conclusion, at least 15% of blood metabolites, representing a broad group of biological pathways, exhibit diurnal variation in three participants. The average peak times of most of these metabolites are clustered in morning or afternoon-midnight. Further work is needed to validate and extend this work in more individuals.