Frailty Model with Change Point for Survival Analysis

TL;DR

This paper introduces a new frailty model with change points for survival analysis, incorporating random effects into the Cox model to better account for heterogeneity among clusters, with an efficient estimation algorithm and practical validation.

Contribution

The paper presents a novel frailty model with change points and an extended EM algorithm for parameter estimation, improving accuracy in heterogeneous survival data analysis.

Findings

Higher accuracy than models without random effects

Effective estimation of change points and random effects

Model performs well in simulation and real clinical trial data

Abstract

We propose a novel frailty model with change points applying random effects to a Cox proportional hazard model to adjust the heterogeneity between clusters. Because the frailty model includes random effects, the parameters are estimated using the expectation-maximization (EM) algorithm. Additionally, our model needs to estimate change points; we thus propose a new algorithm extending the conventional estimation algorithm to the frailty model with change points to solve the problem. We show a practical example to demonstrate how to estimate the change point and random effect. Our proposed model can be easily analyzed using the existing R package. We conducted simulation studies with three scenarios to confirm the performance of our proposed model. We re-analyzed data of two clinical trials to show the difference in analysis results with and without random effect. In conclusion, we confirmed that the frailty model with change points has a higher accuracy than the model without the random effect. Our proposed model is useful when heterogeneity needs to be taken into account. Additionally, the absence of heterogeneity did not affect the estimation of the regression coefficient parameters.