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

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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.





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Wang, Jue, and Sheng Luo (2018). Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model. Statistical methods in medical research. p. 962280218802300. 10.1177/0962280218802300 Retrieved from

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Sheng Luo

Professor of Biostatistics & Bioinformatics

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