JSM 2005 Online Program

Abstract #304190

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Activity Number:	223
Type:	Topic Contributed
Date/Time:	Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Biometrics Section
Abstract - #304190
Title:	Multiple Imputation and Semiparametric Estimators for the Regression Coefficients in the Linear Transformation Competing Risks Model with Missing Cause of Failure
Author(s):	Guozhi Gao*+ and Anastasios A. Tsiatis
Companies:	North Carolina State University and North Carolina State University
Address:	509 Tartan Circle, Raleigh, NC, 27606,
Keywords:	semi-parametric ; multiple imputation ; inverse probability weighted ; linear transformation models ; competing risk ; doubly robust
Abstract:	We consider the problem of estimating the regression coefficients in a competing risks model where the relationship between the cause-specific hazard for the cause of interest and covariates is described using linear transformation models and when cause of failure is missing at random for a subset of individuals. We begin by fully exploiting the ``Missing at Random" assumption, based on which we naturally posit parametric models for the probability of missing cause of failure and cause of failure. We first derive multiple imputation estimators for the regression coefficients that are asymptotically normal when the model for the probability of cause of failure is correctly specified. We then derive augmented inverse probability weighted complete-case estimators for the regression coefficients that are doubly-robust, in the sense that the regression coefficient estimators are consistent and asymptotically normal when either the model for the probability of missingness or the model for the probability of cause of failure are correctly specified.

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