Department of Family & Preventive Medicine, Division of Biostatistics & Bioinformatics, University of California, San Diego, La Jolla, CA 92093-0949, USA.
J Nutr Health Aging. 2012 Apr;16(4):360-4. doi: 10.1007/s12603-012-0047-7.
Randomized clinical trials of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) typically assess intervention efficacy with measures of cognitive or functional assessments repeated every six months for one to two years. The Mixed Model of Repeated Measures (MMRM), which assumes an "unstructured mean" by treating time as categorical, is attractive because it makes no assumptions about the shape of the mean trajectory of the outcome over time. However, categorical time models may be over-parameterized and inefficient in detecting treatment effects relative to continuous time models of, say, the linear trend of the outcome over time. Mixed effects models can also be extended to model quadratic time effects, although it is questionable whether the duration and interval of observations in AD and MCI studies is sufficient to support such models. Furthermore, it is unknown which of these models are most robust to missing data, which plagues AD and MCI studies. We review the literature and compare estimates of treatment effects from four potential models fit to data from five AD Cooperative Study (ADCS) trials in MCI and AD.
阿尔茨海默病（AD）和轻度认知障碍（MCI）的随机临床试验通常使用认知或功能评估来评估干预效果，这些评估每六个月重复一次，持续一到两年。混合重复测量模型（MMRM）通过将时间视为分类变量来假设“非结构化均值”，因为它对随时间变化的结果的均值轨迹形状没有任何假设，所以很有吸引力。然而，相对于随时间变化的结果的线性趋势等连续时间模型，分类时间模型可能会过度参数化并且对治疗效果的检测效率较低。混合效应模型也可以扩展到对二次时间效应进行建模，尽管对于 AD 和 MCI 研究来说，观察的持续时间和间隔是否足以支持这种模型还存在疑问。此外，尚不清楚在存在大量缺失数据的情况下，哪种模型最稳健，而 AD 和 MCI 研究中就存在大量缺失数据。我们回顾了文献，并比较了适用于来自 MCI 和 AD 的五个 AD 合作研究（ADCS）试验数据的四个潜在模型的治疗效果估计值。
