A multilevel model for hierarchical, repeated, and overdispersed time-to-event outcomes and its estimation strategies Hasselt University KU Leuven
The aim of this article is to propose a multilevel combined model for repeated, hierarchical, and overdispersed time-to-event outcomes, extending the so-called combined model proposed by Molenberghs et al. (2010), and using three different estimation strategies: full likelihood, pseudo-likelihood, and Bayesian estimation. For the first two estimation methods, we implemented the alternating imputation posterior (AIP) algorithm (Clayton and ...