Semiparametric Pseudo-Maximum-Likelihood Estimation Exploiting Gene-Environment Independence for Population-Based Case-Control Studies with Complex Sampling

• Activity Number: 559
• Type: Invited
• Date/Time: Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Barry I. Graubard and Yan Li*+
• Companies: National Cancer Institute and The University of Texas at Arlington
• Keywords: Stratified multistage cluster sampling ; Selection probability ; Sample weights ; Taylor linearization ; Hardy-Weinberg Equilibrium

Abstract

Advances in human genetics have led to epidemiologic investigations not only of the effects of genes alone, but also of gene-environment interactions (G-E). A widely accepted design strategy in the study of how G-E relate to disease risks is the population-based case-control study (PBCCS). For simple random samples semiparametric methods for testing G-E have been developed by Chatterjee and Carroll (CC) in 2005. The use of complex sampling in PBCCS is becoming common. Two complexities, weighting and intracluster correlations of observations, are induced by the sampling. We develop pseudo semiparametric maximum-likelihood estimators (pseudo-SPMLE) that extend the CC method to the PBCCS with complex sampling. We study the finite sample performance of the pseudo-SPMLE using simulations and illustrate the pseudo-SPMLE with a case-control study of kidney cancer conducted in Detroit.