American Statistical Association
New York City
Metropolitan Area Chapter

Mailman School of Public Health
Columbia University
Department of Biostatistics Colloquium



A PSEUDO-LIKELIHOOD METHOD
FOR INCORPORATING MISSING COVARIATE DATA
IN STRATIFIED CASE-CONTROL STUDIES

by

Prof. Jinbo Chen
Department of Biostatistics
The University of Pennsylvania


Abstract

This work aimed to update the breast cancer risk prediction model of Gail et al. (1989). We first analyzed data from the Breast Cancer Detection Demonstration Project (BCDDP) to build a multivariate breast cancer relative risk model. Standard breast cancer risk predictors such as family history and age at menarche were collected on all subjects in an age-stratified case-control study nested within the BCDDP, but a new risk predictor, mammographic density (MD), were only available for about half the cases and controls. We developed a pseudo-likelihood method to accommodate missing MD measurements in the logistic regression analysis of both standard risk predictors and MD. Our method adapted a maximum likelihood method of Scott and Wild (1997) to incorporate a novel scheme for maximizing the contribution of complete covariates to the precision of inference. We showed that this procedure was substantially more efficient than a previously proposed weighted-likelihood method. We applied this method to the analysis of BCDDP data and obtained an updated relative risk model. We showed that this updated model, when combined with another model obtained from a different source of data in the BCDDP, led to a breast cancer absolute risk prediction model that had a higher discriminatory power than the original model of Gail et al. (1989).


Date: Thursday, December 18, 2008
Time: 4:00 - 5:00 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York

RESERVATIONS ARE NOT REQUIRED

Refreshments will be served at 3:30 P.M. in the
Biostatistics Conference Room (R627).


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