Mixture mean residual life model for competing risks data with mismeasured covariates

Abstract

This paper proposes a mixture regression model for competing risks data, where the logistic regression model is specified for the marginal probabilities of the failure types and the mean residual lifetime (MRL) model is assumed for the failure time given the failure of interest. The estimating equations (EEs) are derived to infer the logistic regression and MRL model separately. We further consider the situation where the covariates are subject to measurement error. The presence of measurement error imposes extra challenges for the analysis of complex time-to-event data. By using the above EEs as the correction-amenable original estimating functions, we propose a corrected score estimation, which does not require specifying the distributions for unobserved error-prone covariates. The proposed estimators are shown to be consistent and asymptotically normally distributed. The performance of the method is investigated by intensive simulation studies and two real examples are presented to illustrate the proposed methods.

Keywords: Competing risks data; estimating equation; inverse probability censoring weight; mean residual life model; measurement errors.