JSM 2011 Online Program

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Abstract Details

Activity Number: 333
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #301206
Title: Robust Estimation for Homoscedastic Regression in the Secondary Analysis of Case-Control Data
Author(s): Jiawei Wei*+ and Raymond James Carroll and Ursula U. Muller and Ingrid Van Keilegom and Nilanjan Chatterjee
Companies: Texas A & M University and Texas A & M University and Texas A & M University and Université Catholique de Louvain and National Cancer Institute
Address: 3143 TAMU, Department of Statistics, College Station , TX, 77843-3143,
Keywords: Biased samples ; Homoscedastic regression ; Secondary data ; Secondary phenotypes ; Semiparametric inference ; Two-stage samples
Abstract:

Primary analysis of case-control studies focuses on the relationship between disease (D) and a set of covariates of interest (Y,X). A secondary application of the case-control study, often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated due to case-control sampling. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X.

In this paper, we take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different and novel approach in the case that the disease is rare. We show how to estimate the regression parameters in the rare disease case even if the assumed model for Y given X is incorrect, and thus the estimates are model-robust.


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