Medicine and Health Sciences 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 …

Surrogate Screening Models For The Low Physical Activity Criterion Of Frailty, Sandrah P. Eckel, Karen Bandeen-Roche, Paulo H.M. Chaves, Linda P. Fried, Thomas A. Louis

Johns Hopkins University, Dept. of Biostatistics Working Papers

Background and Aims. Low physical activity, one of five criteria in a validated clinical phenotype of frailty, is assessed by a standardized questionnaire on up to 20 leisure time activities. Because of the time demanded to collect the interview data, it has been challenging to translate to studies other than the Cardiovascular Health Study (CHS), for which it was developed. Considering subsets of activities, we identified and evaluated streamlined surrogate assessment methods and compared them to one implemented in the Women’s Health and Aging Study (WHAS).

Methods. Using data on men and women ages 65 and older from the CHS, …

Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations, Lu Wang, Andrea Rotnitzky, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

The Impact Of Coarsening The Explanatory Variable Of Interest In Making Causal Inferences: Implicit Assumptions Behind Dichotomizing Variables, Ori M. Stitelman, Alan E. Hubbard, Nicholas P. Jewell

U.C. Berkeley Division of Biostatistics Working Paper Series

It is common in analyses designed to estimate the causal effect of a continuous exposure/treatment to dichotomize the variable of interest. By dichotomizing the variable and assessing the causal effect of the newly fabricated variable practitioners are implicitly making assumptions. However, in most analyses these assumptions are ignored. In this article we formally address what assumptions are made in dichotomizing variables to assess causal effects. We introduce two assumptions, either of which must be met, in order for the estimates of the causal effects to be unbiased estimates of the parameters of interest. We title those assumptions the Mechanism Equivalence …