Quantile Regression for Competing Risks Data with Missing Cause of Failure

• Activity Number: 562
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Yanqing Sun*+ and Huixia Judy Wang and Peter B Gilbert
• Companies: The University of North Carolina at Charlotte and North Carolina State University and University of Washington/Fred Hutchinson Cancer Research Center
• Keywords: Augmented inverse probability weighted ; Auxiliary variables ; Double robustness ; Local functional linearity ; Logistic regression ; Mashi trial

Abstract

This paper considers the generalized linear quantile regression for competing risks data when the failure type may be missing. Two estimation procedures for the egression coefficients, including an inverse probability weighted complete-case estimator and an augmented inverse probability weighted estimator, are discussed under the assumption that the failure type is missing at random. The proposed estimation procedures utilize supplemental auxiliary variables for predicting the missing failure type and for informing its distribution. The asymptotic properties of the two estimators are derived and their asymptotic efficiencies are compared. We show that the augmented estimator is more efficient and possesses a double obustness property against misspecification of either the model for missingness or for the failure type. The finite sample performance of the proposed estimation procedures are evaluated through a simulation study. The methods are applied to analyze the `Mashi' trial data for investigating the effect of formula- versus breast-feeding plus extended infant zidovudine prophylaxis on HIV-related death of infants born to HIV-infected mothers in Botswana.