Bayesian hierarchical joint modeling of repeatedly measured mixed biomarkers of disease severity and time-to-event

In many clinical follow-up studies, patients are observed at irregular intervals for more than one biomarker of disease severity. Although these biomarkers are often meant to measure the same disease severity, they may differ due to the instruments or reagents used as well as the scale of measurements. They could show different patterns for treatment because clinicians prescribe medications based on the severity of disease. Moreover, if these markers are modeled separately to determine the factors that are associated with disease progression over time or to predict the event of interest given different treatments, they may yield misleading or inefficient results. Joint modeling of correlated biomarkers alone or with time-to-event data leads to efficient results, hence better clinical decisions. In this study, we have first developed a joint model to analyze multivariate unbalanced repeatedly measured outcomes of mixed types, in particular, continuous and ordinal outcomes. Secondly, we have extended the first model to include time-to-event data. The postulated models assumes that the outcomes are from distributions that are in the exponential family and hence modeled as a multivariate generalized linear mixed effects model linked through random effects. The Markov Chain Monte Carlo (MCMC) Bayesian approach is used to approximate the posterior distribution and draw inference on the parameters. These joint models provide a flexible framework to account for the hierarchical structure of the highly unbalanced data as well as the association between the multiple mixed types of outcomes and time-to-event. Moreover, the simulation studies show that estimates obtained from the joint models are consistently less biased and more efficient than those obtained from the separate models. We applied our models to diabetes data from an observational study. Diabetes and its associated complications such as heart attack and stroke are of serious public health concerns across the globe. Proper treatment can help control and prevent the development of these complications and hence improve the quality of life of millions of people. This work proposes to efficiently estimate the treatment effect by introducing state-of-the-art statistical methods. This will help researchers identify effective treatments that can slow down the disease progression

Stratified randomization controls better for batch effects in 450K methylation analysis: a cautionary tale

Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. Methods: Two pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for chip- and row-specific batch effects. For each study, the DNA of 46 obese men and 46 lean men were assayed using Illumina's Infinium HumanMethylation450 BeadChip. In the first study (Sample One), samples from obese and lean subjects were examined on separate chips. In the second study (Sample Two), the samples were balanced on the chips by lean/obese status, age group, and census region. We used methylumi, watermelon, and limma R packages, as well as ComBat, to analyze the data. Principal component analysis and linear regression were respectively employed to identify the top principal components and to test for their association with the batches and lean/obese status. To identify differentially methylated positions (DMPs) between obese and lean males at each locus, we used a moderated t-test.Results: Chip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After removing'' batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.Conclusion: Even the best possible statistical adjustments for batch effects may not completely remove them. Proper study design is vital for guarding against spurious findings due to such effects