Model Identifiability for Bivariate Failure Time Data with Competing Risks: Parametric Cause-specific Hazards and Non-parametric Frailty

TL;DR

This paper investigates the identifiability of models for bivariate survival data with competing risks, comparing non-parametric frailty and parametric cause-specific hazards to ensure robust and flexible modeling.

Contribution

It provides a detailed analysis of model identifiability for combined parametric and non-parametric approaches in multivariate survival analysis with competing risks.

Findings

Identifiability conditions established for various frailty models.

Comparison of non-parametric frailty with parametric cause-specific hazards.

Framework ensures robust modeling of bivariate survival data.

Abstract

One of the commonly used approaches to capture dependence in multivariate survival data is through the frailty variables. The identifiability issues should be carefully investigated while modeling multivariate survival with or without competing risks. The use of non-parametric frailty distribution(s) is sometimes preferred for its robustness and flexibility properties. In this paper, we consider modeling of bivariate survival data with competing risks through four different kinds of non-parametric frailty and parametric baseline cause-specific hazard functions to investigate the corresponding model identifiability. We make the common assumption of the frailty mean being equal to unity.