Epidemiology Commons^™

Minimum Description Length And Empirical Bayes Methods Of Identifying Snps Associated With Disease, Ye Yang, David R. Bickel

COBRA Preprint Series

The goal of determining which of hundreds of thousands of SNPs are associated with disease poses one of the most challenging multiple testing problems. Using the empirical Bayes approach, the local false discovery rate (LFDR) estimated using popular semiparametric models has enjoyed success in simultaneous inference. However, the estimated LFDR can be biased because the semiparametric approach tends to overestimate the proportion of the non-associated single nucleotide polymorphisms (SNPs). One of the negative consequences is that, like conventional p-values, such LFDR estimates cannot quantify the amount of information in the data that favors the null hypothesis of no disease-association.

We …

The Handling Of Missing Data In Molecular Epidemiologic Studies, Manisha Desai, Jessica Kubo, Denise Esserman, Mary Beth Terry

COBRA Preprint Series

Background: Molecular epidemiologic studies face a missing data problem as biospecimen data are often collected on only a proportion of subjects eligible for study.

Methods: We investigated all molecular epidemiologic studies published in CEBP in 2009 to characterize the prevalence of missing data and to elucidate how the issue was addressed. We considered multiple imputation (MI), a missing data technique that is readily available and easy to implement, as a possible solution.

Results: While the majority of studies had missing data, only 16% compared subjects with and without missing data. Furthermore, 95% of the studies with missing data performed a …

The Use Of Multiple Imputation In Molecular Epidemiologic Studies Assessing Interaction Effects, Manisha Desai, Denise Esserman, Marilie Gammon, Mary Beth Terry

COBRA Preprint Series

Background: In molecular epidemiologic studies biospecimen data are collected on only a proportion of subjects eligible for study. This leads to a missing data problem. Missing data methods, however, are not typically incorporated into analyses. Instead, complete-case (CC) analyses are performed, which result in biased and inefficient estimates.

Methods: Through simulations, we characterized the bias that results from CC methods when interaction effects are estimated, as this is a major aim of many molecular epidemiologic studies. We also investigated whether standard multiple imputation (MI) could improve estimation over CC methods when the data are not missing at random (NMAR) and …