Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA.
Stat Methods Med Res. 2019 Oct-Nov;28(10-11):3392-3403. doi: 10.1177/0962280218802300. Epub 2018 Oct 11.
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.
肌萎缩侧索硬化症（ALS）引起的损伤是多维度的（例如球部、精细运动、粗大运动）和进行性的。其多维性质排除了单一的结果来衡量疾病进展。ALS 的临床试验使用多个纵向结局来评估治疗对整体改善的效果。例如死亡或退出等终末事件可能会停止随访过程。此外，到达终末事件的时间可能依赖于多维纵向测量。在本文中，我们开发了一个联合模型，该模型由一个多维潜在特征线性混合模型（MLTLMM）和一个具有分段常数基线风险的比例风险模型组成，用于事件时间数据。共享随机效应用于将两个模型联系起来。通过在 Stan 语言中实现的马尔可夫链蒙特卡罗模拟，使用贝叶斯框架进行模型推断。我们的模型通过模拟研究进行评估，并应用于头孢曲松研究，这是一项评估头孢曲松对 ALS 患者影响的临床试验。
