A Comparison of Estimation Methods for Shared Gamma Frailty Models

This paper compares six different estimation methods for shared frailty models via a series of simulation studies. A shared frailty model is a survival model that incorporates random effects, where the frailties are common or shared among individuals within specific groups. Several 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 are a limited amount of research comparing the performance of these estimation methods. 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 estimation methods implemented in R packages. We will evaluate several key aspects, including the performance of parameter estimators, rate of convergence, and computational time. Through this systematic evaluation, our goal is to provide a comprehensive understanding of the advantages and limitations associated with each estimation method.