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Abstract Details

Activity Number: 562
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #305472
Title: Weighted Estimators for Additive Risk Models with Missing Covariates
Author(s): Lihong Qi*+ and Lu Wang and Yueheng An and Yichuan Zhao
Companies: University of California at Davis and University of California at Davis and Georgia State University and Georgia State University
Address: Department of Public Health Sciences, Davis, CA, , USA
Keywords: Additive risk model ; Kernel smoother ; Missing covariate data ; Nonparametric method ; Weighted estimating equation

In contrast to the proportional hazards models, the additive risk model specifies that the conditional hazard function given a set of covariates is the sum of, rather than the product of, an arbitrary baseline hazard function and a regression function of covariates. In medical studies including clinical trials, covariate data are often observed incompletely. In some situations, certain covariates are observed for all study subjects and other covariate data are collected only for a subset. Inconsistent and inefficient estimates can be generated by naively discarding subjects with incomplete data. In this talk, I will present both simple weighted and kernel-assisted fully augmented weighted estimators that use partially incomplete data nonparametrically. The resulting weighted estimators are consistent and asymptotically normal. Moreover, they are more efficient than the simple weighted estimator with the inverse of true selection probability as weight. The proposed weighted estimators allow the missing-data mechanism to depend on outcome variables and observed covariates, and they are applicable to various cohort sampling procedures.

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