American Statistical Association
Logistic regression has been the standard method of analysis for case-control studies in epidemiology. The classical justifications (Prentice-Pyke, 1979, Biometrika) for the efficiency of the estimates of odds ratios from such prospective analysis, despite the retrospective case-control sampling of subjects, depend on the nonparametric treatment of the distribution of the covariates in the underlying population. In genetic epidemiologic studies, however, parametric- or semi-parametric assumptions, such as Hardy-Weinberg-Equilibrium (HWE), linkage equilibrium (independence) between physically distant loci and independence of genetic and environmental factors, can lead to alternative methods for analysis of case-control studies with major efficiency advantage over the standard analysis. Unlike logistic regression, however, these alternative methods may produce biased inference when the underlying distributional assumptions are violated. In this talk, I will describe two techniques, one based on shrinkage estimation methodology, and the other based on conditional inference on minimal sufficient statistics, which can dramatically reduce the bias of the alternative methods when the underlying model assumptions are violated. Both simulation studies and real data examples are used to illustrate the trade-off between bias and efficiency achieved by the proposed methods. Theoretical issues will be discussed regarding how such estimators can achieve efficiency beyond known semi-parametric efficiency bounds. It is concluded that the novel methods could be potentially very useful for discovery and characterization of genetic associations and gene-environment interactions from case-control studies.
Dr. Nilanjan Chatterjee is the Chief and a Senior Investigator of the Biostatistics Branch of the Division of Cancer Epidemiology and Genetics (DECG), National Cancer Institute (NCI). He received his Ph.D. in Statistics from the University of Washington, Seattle in 1999. His research focuses on statistical methods for modern genetic and molecular epidemiologic studies. His statistical areas of research, which cut across the different scientific disciplines, include regression analysis under complex sampling designs (e.g., case-control and two-phase sampling), missing data, multivariate survival analysis and semiparametric inferences. He also actively collaborates in design and analysis of a variety of major cancer epidemiologic studies at NCI. His recent honors include NIH Merit Award (2003), DCEG Outstanding Mentoring award (2005), election as a Fellow of the American Statistical Association (2008) and being named as the 2010 recipient of the Mortimer Spiegelman Award from the American Public Health Association for outstanding contribution to Biostatistics.
|Date:||Thursday, October 14, 2010|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York