A review of h-likelihood for survival analysis

Statistical models with unobservable random variables such as random-effect models have been recently studied for analyzing data of complex types (e.g. longitudinal and time-to-event data) in various areas. The hierarchical likelihood [h-likelihood; Lee and Nelder (Journal of the Royal Statistical Society 58, 619-678, 1996)] provides a unified framework for the inference of such models with unobservable random variables. In this paper, we review the h-likelihood framework for survival analysis. We also demonstrate how to analyze survival data via web-based software Albatross Analytics which was recently developed based on the h-likelihood procedure. Furthermore, we discuss recent extensions of the h-likelihood.