Fitting Frailty Models via Linear Mixed Models using Model Transformation

Abstract

Frailty models are widely used to model clustered survival data. Classical ways to fit frailty models are likelihood based. We propose an alternative approach in which the original problem of 'fitting a frailty model' is reformulated into the problem of 'fitting a linear mixed model' using model transformation. Based on a simulation study, we show that the proposed method provides a good and simple alternative for fitting frailty models for data sets with a sufficiently large number of clusters and moderate to large sample sizes within covariate level subgroups in the clusters. We illustrate the proposed method using data from 27 randomized trials in advanced colorectal cancer.