presents
Student Seminars
Tina Davenport, Arnab Maity
A
Powerful Test for SNP Effects
on Multivariate Binary Outcomes using Kernel Machine Regression
Abstract
Evaluating multiple binary outcomes is common in genetic studies of complex diseases. These outcomes are often correlated because they are collected from the same individual and they may share common marker effects. In this paper, we propose a procedure to test for effect of a SNP-set on multiple, possibly correlated, binary responses. We develop a score-based test using a nonparametric modeling framework that jointly models the global effect of the marker set. We account for the nonlinear effects and potentially complicated interaction between markers using reproducing kernels. Our testing procedure only requires estimation under the null hypothesis and we use multivariate generalized estimating equations (GEEs) to estimate the model components to account for the correlation among the outcomes. We evaluate finite sample performance of our test via simulation study and demonstrated our methods using the CATIE antibody study data.
Laura Boehm, Brian Reich, Montse Fuentes, Francesca Dominici
Spatial variable selection methods for investigating acute\\ health effects of fine particulate matter components
Abstract
Previous research has suggested a connection between ambient particulate matter (PM) exposure and acute health effects, but the effect size varies across the United States. Variability in the effect may partially be due to differing community level exposure and health characteristics, but also due to the chemical composition of PM which is known to vary greatly by location and over time. The objective of this paper is to identify particularly harmful components of this chemical mixture. Because of the large number of potentially highly correlated components, we must incorporate some regularization into a statistical model. We assume that at each location the regression coefficients come from a mixture model, with the flavor of stochastic search variable selection, but utilize a copula to share information about variable inclusion and effect magnitude across locations. The model differs from current spatial variable selection techniques by accommodating both local and global variable selection. The model is used to study the association between fine PM components, measured at 115 counties nationally over the period 2000-2008, and cardiovascular emergency room admissions among Medicare patients.Paul Bernhardt, Huixia Judy Wang, Daowen Zhang
Statistical
Modeling with Covariates Subject to Detection Limits
Biomedical
datasets frequently contain variables which are subject to censoring. Though censoring is commonly associated with
time-to-event data, censored data also arise due to detection limits. We develop an “improper” multiple imputation method for
analyzing datasets with multiple predictors censored due to detection limits
within the context of generalized linear models and survival models. We also propose a maximum likelihood
procedure for jointly modeling binary data and longitudinal covariates subject
to detection limits. This presentation
will serve as an overview of these multiple imputation and maximum likelihood
procedures as applied to the GenIMS dataset.
Tuesday, February 5
3:30pm - 4:30pm
1108 SAS Hall