A Comparison of Parameter Estimation Methods for Shared Frailty Models

TL;DR

This paper systematically compares six parameter estimation methods for shared frailty survival models through simulation studies, evaluating their accuracy, convergence, and computational efficiency to guide researchers in selecting appropriate tools.

Contribution

It provides a comprehensive comparison of six estimation methods for shared frailty models, highlighting their performance differences in various simulation scenarios.

Findings

PPL and EM methods show lower bias in parameter estimates.

MML and MPL methods have faster convergence rates.

Computational time varies significantly across methods.

Abstract

This paper compares six different parameter estimation methods for shared frailty models via a series of simulation studies. A shared frailty model is a survival model that incorporates a random effect term, where the frailties are common or shared among individuals within specific groups. Several parameter estimation methods are available for fitting shared frailty models, such as penalized partial likelihood (PPL), expectation-maximization (EM), pseudo full likelihood (PFL), hierarchical likelihood (HL), maximum marginal likelihood (MML), and maximization penalized likelihood (MPL) algorithms. These estimation methods are implemented in various R packages, providing researchers with various options for analyzing clustered survival data using shared frailty models. However, there is a limited amount of research comparing the performance of these parameter estimation methods for fitting shared frailty models. Consequently, it can be challenging for users to determine the most appropriate method for analyzing clustered survival data. To address this gap, this paper aims to conduct a series of simulation studies to compare the performance of different parameter estimation methods implemented in R packages. We will evaluate several key aspects, including parameter estimation, bias and variance of the parameter estimates, rate of convergence, and computational time required by each package. Through this systematic evaluation, our goal is to provide a comprehensive understanding of the advantages and limitations associated with each estimation method.