Exploring shared frailty models for cluster-specific risk estimation: A study on diabetes patients with a history of acute coronary syndrome

Abstract

This study proposes the use of semiparametric log-normal shared frailty models to analyze time-to-event data for individuals with similar features referred to as clusters. Shared frailty models are useful for modeling and estimating common risk in the lifetimes of individuals in these clusters. While various methods have been proposed for estimating shared frailty models, few studies have explored the use of the pseudo-full-likelihood method. In this study, the pseudo-full-likelihood and hierarchical likelihood approaches were used to construct and estimate parameter estimates and check for asymptotic properties via simulations. Log-normal semiparametric frailty model was used to obtain cluster-specific frailty based on the semiparametric log-normal shared frailty distribution. The results of both methods were compared, and prediction intervals for a random effect were obtained. To further investigate the existence of shared frailty in diabetes patients and a history of acute coronary syndrome (STEMI and NSTEMI), data from UK Biobank was used. The results suggest the presence of frailty within the clusters and indicate cluster time dependence in the study population. Overall, this study highlights the potential benefits of using the pseudo-full-likelihood method in shared frailty modeling and provides insights into the impact of observed variabilities on hazards within clusters.