A Computationally Efficient Approach for Modeling Complex and Big Survival Data

Abstract

Modern data collection techniques have resulted in an increasing number of big clustered time-to-event data sets, wherein patients are often observed from a large number of healthcare providers. Semiparametric frailty models are a flexible and powerful tool for modeling clustered time-to-event data. In this manuscript, we first provide a computationally efficient approach based on a minimization-maximization algorithm to fit semiparametric frailty models in large-scale settings. We then extend the proposed method to incorporate complex data structures such as time-varying effects, for which many existing methods fail because of lack of computational power. The finite-sample properties and the utility of the proposed method are examined through an extensive simulation study and an analysis of the national kidney transplant data.