The Utility of Multistate Models: A Flexible Framework for Time-to-Event Data.

Abstract

Purpose of review: Survival analyses are common and essential in medical research. Most readers are familiar with Kaplan-Meier curves and Cox models; however, very few are familiar with multistate models. Although multistate models were introduced in 1965, they only recently receive more attention in the medical research community. The current review introduces common terminologies and quantities that can be estimated from multistate models. Examples from published literature are used to illustrate the utility of multistate models.

Recent findings: A figure of states and transitions is a useful depiction of a multistate model. Clinically meaningful quantities that can be estimated from a multistate model include the probability in a state at a given time, the average time in a state, and the expected number of visits to a state; all of which describe the absolute risks of an event. Relative risk can also be estimated using multistate hazard models.

Summary: Multistate models provide a more general and flexible framework that extends beyond the Kaplan-Meier estimator and Cox models. Multistate models allow simultaneous analyses of multiple disease pathways to provide insights into the natural history of complex diseases. We strongly encourage the use of multistate models when analyzing time-to-event data.

Supplementary information: The online version contains supplementary material available at 10.1007/s40471-022-00291-y.