Semiparametric analysis of competing risks data with missing causes of failure and covariate measurement error

Competing risks data with missing causes of failure are common in biomedical studies. Often, competing risks data may arise with the covariates that are measured with error. In this work, we consider a semiparametric linear transformation model to deal with the combined problem of competing risks data with missing causes of failure and the covariate measurement error. We consider a set of estimating equations to obtain the estimators of the parameters involved in this linear transformation model. To handle the missing causes of failure, we employ the Inverse Probability Weight (IPW) approach, and a flexible Simulation Extrapolation (SIMEX) method is adopted as the covariate measurement error correction technique. We study the asymptotic properties of the estimators and assess the finite sample properties of the estimators by a Monte Carlo simulation study. The proposed method is illustrated using real data.