Use of multi-state models for time-to-event data

• Main Authors: Mahtab Rouhifard, Mehdi Yaseri
• Format: Article
• Language: English
• Published: Tehran University of Medical Sciences 2018-07-01
• Series: Journal of Biostatistics and Epidemiology
• Subjects: Markov chains; Survival analysis; Risk factors; Proportional hazards models; Disease progression
• Online Access: https://jbe.tums.ac.ir/index.php/jbe/article/view/181

Background & Aim: In medical sciences, the outcome is the time until the occurrence of an event of interest. A multi-state model (MSM) is used to model a process where subjects’ transition takes place from one state to the next. For instance, a standard survival curve can be thought of as a simple MSM with two states (alive and dead) and the transition between these two-state models is a method used to analyze time to event data. The most important aspect of this model is that it considers intermediate events and models the effect of covariates on each transition intensity. Some diseases like cancer, human immunodeficiency virus (HIV), etc. have several stages. In the present study, these models were reviewed using cardiac allograft vasculopathy (CAV) data focusing on different approaches. Methods & Materials: The data of 576 CAVs were collected. A time dependent simple Cox regression model (CRM) was fitted and a three-state illness-death model was considered for the MSMs. Results: In the simple CRM, only the individuals with the age of > 50 were significant, however for Cox Markov model (CMM) and Cox semi-Markov model (CSMM), the donor’s age > 40, sex, and the individuals with the age of > 50 were other significant covariates. Conclusion: The CMM and CSMM showed more accurate results about risk factors compared to the simple CRM.