Multiple imputation for competing risks survival data via pseudo-observations

Pseudo-observations for competing risks with covariate dependent censoring.

Regression analysis for competing risks data can be based on generalized estimating equations. For the case with right censored data, pseudo-values were proposed to solve the estimating equations. In this article we investigate robustness of the pseudo-values against violation of the assumption that the probability of not being lost to follow-up (un-censored) is independent of the covariates. M...

Simulating competing risks data in survival analysis.

Competing risks analysis considers time-to-first-event ('survival time') and the event type ('cause'), possibly subject to right-censoring. The cause-, i.e. event-specific hazards, completely determine the competing risk process, but simulation studies often fall back on the much criticized latent failure time model. Cause-specific hazard-driven simulation appears to be the exception; if done, ...

Survival estimation and testing via multiple imputation

Multiple imputation is a technique for handling data sets with missing values. The method 0lls in each missing value several times, creating many augmented data sets. Each augmented data set is analyzed separately and the results combined to give a 0nal result consisting of an estimate and a measure of uncertainty. In this paper we consider nonparametric multiple-imputation methods to handle mi...

multiple imputation in survival models: applied on breast cancer data

Classification Trees for Survival Data with Competing Risks