论文标题
重复度量的进展模型：估计进行性疾病的新治疗效果
Progression models for repeated measures: Estimating novel treatment effects in progressive diseases
论文作者
论文摘要
分析临床试验结果时，重复测量的混合模型（MMRMS）无处不在。但是，在这些模型中，固定效应结构的线性在很大程度上限制了其用于估计治疗效果的用途，这些治疗效果被定义为对结果量表的效应的线性组合。在某些情况下，治疗效果的替代量化可能更合适。例如，在进行性疾病中，人们可能想估计药物是否具有累积作用，导致随着时间的推移疗效的增加，或者它是否减慢了疾病的发展。本文介绍了一类非线性混合效应模型，称为重复测量（PMRMS）的进程模型，这些模型基于MMRMS的分类时间参数化的连续时间扩展，可以估计新型治疗效果的类型，包括疾病时间进展的减慢或延迟疾病的延迟。与传统的治疗效果估计值相比，该单位与结果量表的量相匹配（例如2点在认知量表上受益），基于时间的治疗效果可以提供更好的解释性和临床意义（例如，认知能力下降的延迟6个月的延迟）。 PMRM类包括常规使用的MMRM和相关模型，用于纵向数据分析，以及先前提出的疾病进展模型的变体作为特殊情况。使用阿尔茨海默氏病的临床试验和具有不同类型的人工模拟治疗效果的临床试验中的模拟和历史数据来说明PMRM框架的潜力。与常规模型相比，表明PMRM可以提供大幅增加的功率来检测疾病改良的治疗效果，而在治疗持续时间内收益增加。
Mixed Models for Repeated Measures (MMRMs) are ubiquitous when analyzing outcomes of clinical trials. However, the linearity of the fixed-effect structure in these models largely restrict their use to estimating treatment effects that are defined as linear combinations of effects on the outcome scale. In some situations, alternative quantifications of treatment effects may be more appropriate. In progressive diseases, for example, one may want to estimate if a drug has cumulative effects resulting in increasing efficacy over time or whether it slows the time progression of disease. This paper introduces a class of nonlinear mixed-effects models called Progression Models for Repeated Measures (PMRMs) that, based on a continuous-time extension of the categorical-time parametrization of MMRMs, enables estimation of novel types of treatment effects, including measures of slowing or delay of the time progression of disease. Compared to conventional estimates of treatment effects where the unit matches that of the outcome scale (e.g. 2 points benefit on a cognitive scale), the time-based treatment effects can offer better interpretability and clinical meaningfulness (e.g. 6 months delay in progression of cognitive decline). The PMRM class includes conventionally used MMRMs and related models for longitudinal data analysis, as well as variants of previously proposed disease progression models as special cases. The potential of the PMRM framework is illustrated using both simulated and historical data from clinical trials in Alzheimer's disease with different types of artificially simulated treatment effects. Compared to conventional models it is shown that PMRMs can offer substantially increased power to detect disease-modifying treatment effects where the benefit is increasing with treatment duration.
