Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model

• Published in: Statistical methods in medical research Vol. 28; no. 10-11; p. 3392
• Main Authors: Wang, Jue, Luo, Sheng
• Format: Journal Article
• Language: English
• Published: England 01.11.2019
• Subjects: Amyotrophic Lateral Sclerosis - drug therapy; Amyotrophic Lateral Sclerosis - mortality; Bayes Theorem; Ceftriaxone - therapeutic use; Disease Progression; Humans; Linear Models; Markov Chains; Monte Carlo Method; Proportional Hazards Models; Survival Analysis; Amyotrophic lateral sclerosis; mixed model; Markov chain Monte Carlo; informative dropout; longitudinal data
• ISSN: 1477-0334
• DOI: 10.1177/0962280218802300

Impairment caused by Amyotrophic lateral sclerosis (ALS) is multidimensional (e.g. bulbar, fine motor, gross motor) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of ALS use multiple longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we develop a joint model consisting of a multidimensional latent trait linear mixed model (MLTLMM) for the multiple longitudinal outcomes, and a proportional hazards model with piecewise constant baseline hazard for the event time data. Shared random effects are used to link together two models. The model inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation implemented in Stan language. Our proposed model is evaluated by simulation studies and is applied to the Ceftriaxone study, a motivating clinical trial assessing the effect of ceftriaxone on ALS patients.