A discrete approximation method for modeling interval‐censored multistate data

• Published in: Statistics in medicine Vol. 43; no. 12; pp. 2452 - 2471
• Main Authors: You, Lu, Liu, Xiang, Krischer, Jeffrey
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 30.05.2024; Wiley Subscription Services, Inc
• Subjects: Algorithms; Approximation; Computer Simulation; data augmentation; Data Interpretation, Statistical; Heart Transplantation - statistics & numerical data; Humans; interval censoring; Likelihood Functions; Longitudinal Studies; Models, Statistical; multistate model; Nonparametric statistics; proportional hazards model; Proportional Hazards Models; time‐to‐event data; time‐to‐event data; data augmentation; interval censoring; proportional hazards model; multistate model
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.10079

Many longitudinal studies are designed to monitor participants for major events related to the progression of diseases. Data arising from such longitudinal studies are usually subject to interval censoring since the events are only known to occur between two monitoring visits. In this work, we propose a new method to handle interval‐censored multistate data within a proportional hazards model framework where the hazard rate of events is modeled by a nonparametric function of time and the covariates affect the hazard rate proportionally. The main idea of this method is to simplify the likelihood functions of a discrete‐time multistate model through an approximation and the application of data augmentation techniques, where the assumed presence of censored information facilitates a simpler parameterization. Then the expectation‐maximization algorithm is used to estimate the parameters in the model. The performance of the proposed method is evaluated by numerical studies. Finally, the method is employed to analyze a dataset on tracking the advancement of coronary allograft vasculopathy following heart transplantation.