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Activity Number: 86
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #318842 View Presentation
Title: Dimension Reduction and Estimation in Secondary Analysis of Case-Control Studies
Author(s): Liang Liang* and Yanyuan Ma and Raymond Carroll
Companies: Texas A&M University and University of South Carolina and Texas A&M University
Keywords: Biased samples ; Case-control study ; Heteroscedastic error ; Secondary analysis ; Semiparametric estimation ; Single index model

Studying the relationship between covariates based on retrospective data is the main purpose of secondary analysis, an area of increasing interest. We examine the secondary analysis problem when multiple covariates are available, while only a regression mean model is specified. Despite the completely parametric modeling of the regression mean function, the case-control nature of the data requires special treatment and semiparametric efficient estimation generates various nonparametric estimation problems with multivariate covariates. We devise a dimension reduction approach that fits with the specified primary and secondary models in the original problem setting, and use reweighting to adjust for the case-control nature of the data, even when the disease rate in the source population is unknown. The resulting estimator is both locally efficient and robust against the misspecification of the regression error distribution, which can be heteroscedastic as well as non-Gaussian. We demonstrate the advantage of our method over several existing methods both analytically and numerically.

Authors who are presenting talks have a * after their name.

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