A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data.

ABSTRACT: In the analysis of semi-competing risks data interest lies in estimation and inference with respect to a so-called non-terminal event, the observation of which is subject to a terminal event. Multi-state models are commonly used to analyse such data, with covariate effects on the transition/intensity functions typically specified via the Cox model and dependence between the non-terminal and terminal events specified, in part, by a unit-specific shared frailty term. To ensure identifiability, the frailties are typically assumed to arise from a parametric distribution, specifically a Gamma distribution with mean 1.0 and variance, say, σ². When the frailty distribution is misspecified, however, the resulting estimator is not guaranteed to be consistent, with the extent of asymptoti

SUBMITTER: Jiang F 

PROVIDER: S-EPMC5400113 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature