Semiparametric inference of competing risks data with additive hazards and missing cause of failure under MCAR or MAR assumptions

Semiparametric inference of competing risks data with additive hazards and missing cause of failure under MCAR or MAR assumptions

Analysis of Competing Risks Data with Missing Cause of Failure under Additive Hazards Model

Competing risks data arise when study subjects may experience several different types of failure. It is common that the cause of failure is missing due to various reasons. Analysis of competing risks data with missing cause of failure has received considerable attention recently (Goetghebeur and Ryan (1995), Lu and Tsiatis (2001), Gao and Tsiatis (2005), among others). In this article, we study...

Quantile Regression for Competing Risks Data with Missing Cause of Failure.

This paper considers generalized linear quantile regression for competing risks data when the failure type may be missing. Two estimation procedures for the regression co-efficients, 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...

Two-level lognormal frailty model and competing risks model with missing cause of failure

Approved: Thesis Supervisor Title and Department

Semiparametric analysis of mixture regression models with competing risks data.

In the analysis of competing risks data, cumulative incidence function is a useful summary of the overall crude risk for a failure type of interest. Mixture regression modeling has served as a natural approach to performing covariate analysis based on this quantity. However, existing mixture regression methods with competing risks data either impose parametric assumptions on the conditional ris...