Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model.
Repository Usage Stats
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.
SubjectAmyotrophic lateral sclerosis
Markov chain Monte Carlo
Published Version (Please cite this version)10.1177/0962280218802300
Publication InfoLuo, Sheng; & Wang, Jue (2018). Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model. Statistical methods in medical research. pp. 962280218802300. 10.1177/0962280218802300. Retrieved from https://hdl.handle.net/10161/19134.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
More InfoShow full item record