Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model.
Abstract
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.
Type
Journal articleSubject
Amyotrophic lateral sclerosisMarkov chain Monte Carlo
informative dropout
longitudinal data
mixed model
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https://hdl.handle.net/10161/19134Published Version (Please cite this version)
10.1177/0962280218802300Publication Info
Wang, Jue; & Luo, Sheng (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.
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Show full item recordScholars@Duke
Sheng Luo
Professor of Biostatistics & Bioinformatics

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