H-likelihood approach for joint modeling of longitudinal outcomes and time-to-event data

Abstract

In longitudinal studies, a subject may have different types of outcomes that could be correlated. For example, a response variable of interest would be measured repeatedly over time on the same subject and at the same time, an event time representing a single event or competing-risks event is also observed. In this paper, we propose a joint modeling framework that accounts for the inherent association between such multiple outcomes via frailties (unobserved random effects). Among outcomes, at least one outcome is an event time that has a type of a single event or competing-risks event. For inference we use the hierarchical likelihood (h-likelihood) that provides an unified efficient fitting procedure for the joint models. Numerical studies are provided to show the performance of the proposed method and two data examples are shown.

Keywords: Competing-risks data; Frailty model; H-likelihood; Joint model; Random effects.