Joint model for repeated measurements and competing risks data using flexible shared random effects

Area of Science:

• Biostatistics
• Clinical Trials
• Longitudinal Data Analysis

Background:

• Clinical trials generate complex data, including longitudinal measurements and time-to-event outcomes with competing risks.
• Existing models often struggle to simultaneously analyze these distinct data types effectively.

Purpose of the Study:

• To develop and evaluate a joint statistical model for analyzing longitudinal data alongside competing risks time-to-event data.
• To provide a robust framework for inferring relationships between covariates, longitudinal trajectories, and event occurrences.

Main Methods:

• A joint model combining a linear mixed-effects model for longitudinal data and a generalized exponential distribution for competing risks.
• A shared random effects structure links the longitudinal and survival processes.
• Parameter estimation via maximum likelihood using the Expectation-Maximization algorithm.

Main Results:

• The proposed joint model demonstrates feasibility and utility in analyzing complex clinical trial data.
• Simulation studies confirm the model's performance in parameter estimation and inference.
• Application to the SANAD trial data highlights practical applicability.

Conclusions:

• The developed joint model offers a powerful tool for simultaneously analyzing longitudinal and competing risks data in clinical research.
• This approach enhances the understanding of disease progression and treatment effects in the presence of multiple event types.