Proportional hazards model for competing risks data with missing cause of failure

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...

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

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

Estimation in a competing risks proportional hazards model under length-biased sampling with censoring.

What population does the sample represent? The answer to this question is of crucial importance when estimating a survivor function in duration studies. As is well-known, in a stationary population, survival data obtained from a cross-sectional sample taken from the population at time t(0) represents not the target density f (t) but its length-biased version proportional to t f (t), for t > 0. ...