Multistate models with nested frailty for lifetime analysis: Application to bone marrow transplantation recovery patients

Abstract

Multistate models allow individuals to move through a series of states over time, making it possible to estimate the transition probabilities and intensities between states and the effect of covariates associated with each transition. Multistate models are useful for studying the evolution of individuals over time, particularly in clustered data, where individuals are grouped together and may share common risk factors. Frailties can be used in multistate models to capture the heterogeneity between groups at risk for different types of transitions, as well as the dependency structure between transitions of individuals in the same group. Frailties are unobserved random variables that are assumed to be independent between different types of transitions but shared by groups of individuals. In this paper, we propose a multistate model with frailties to capture the heterogeneity between groups at risk for different types of transitions, as well as the dependency structure between transitions of individuals in the same group. We assume that the frailties are independent between different types of transitions but shared by groups of individuals. We discuss both parametric and semiparametric estimation of the model parameters, and we evaluate the performance of the model in simulation studies. Finally, we apply the model to a dataset of bone marrow transplantation recovery patients.