症状前阿尔茨海默病中疾病进展时间与认知连续测量指标的相对效率
The relative efficiency of time-to-progression and continuous measures of cognition in presymptomatic Alzheimer's disease.
作者信息
Li Dan, Iddi Samuel, Aisen Paul S, Thompson Wesley K, Donohue Michael C
机构信息
Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, CA, USA.
Department of Statistics, University of Ghana, Legon-Accra, Ghana.
出版信息
Alzheimers Dement (N Y). 2019 Jul 18;5:308-318. doi: 10.1016/j.trci.2019.04.004. eCollection 2019.
INTRODUCTION
Clinical trials on preclinical Alzheimer's disease are challenging because of the slow rate of disease progression. We use a simulation study to demonstrate that models of repeated cognitive assessments detect treatment effects more efficiently than models of time to progression.
METHODS
Multivariate continuous data are simulated from a Bayesian joint mixed-effects model fit to data from the Alzheimer's Disease Neuroimaging Initiative. Simulated progression events are algorithmically derived from the continuous assessments using a random forest model fit to the same data.
RESULTS
We find that power is approximately doubled with models of repeated continuous outcomes compared with the time-to-progression analysis. The simulations also demonstrate that a plausible informative missing data pattern can induce a bias that inflates treatment effects, yet 5% type I error is maintained.
DISCUSSION
Given the relative inefficiency of time to progression, it should be avoided as a primary analysis approach in clinical trials of preclinical Alzheimer's disease.
引言
由于疾病进展缓慢，临床前阿尔茨海默病的临床试验具有挑战性。我们通过一项模拟研究表明，重复认知评估模型比疾病进展时间模型能更有效地检测治疗效果。
方法
多变量连续数据由一个贝叶斯联合混合效应模型模拟得出，该模型拟合了阿尔茨海默病神经影像倡议的数据。使用拟合相同数据的随机森林模型，从连续评估中算法性地得出模拟进展事件。
结果
我们发现，与疾病进展时间分析相比，重复连续结果模型的检验效能大约提高了一倍。模拟还表明，一种看似合理的信息性缺失数据模式会导致偏差，使治疗效果膨胀，但仍能维持5%的I型错误率。
讨论
鉴于疾病进展时间相对效率较低，在临床前阿尔茨海默病的临床试验中应避免将其作为主要分析方法。
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