Progression models for repeated measures: Estimating novel treatment effects in progressive diseases
重复测量进展模型：估计进行性疾病的新型治疗效应AI 解读 ↓
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重复测量进展模型：估计进行性疾病的新型治疗效应
- 研究问题
- 对于随时间进展的疾病，如何超越结局尺度上的线性治疗效应，估计疗效累积、疾病进展减慢或时间延迟等效应？
- 方法与思路
- 作者将重复测量混合模型的分类时间参数化扩展至连续时间，提出非线性混合效应 PMRM 框架以估计时间尺度治疗效应。
- 材料陈述的贡献
- 摘要称，框架包含常规模型和部分既有进展模型；在人工设定疗效随疗程增加的示例中，可提高检测疾病修饰效应的功效。
- 阅读关注点
- 应核对时间延迟和减速效应的参数定义、可识别性、轨迹约束，并检查历史数据中人工模拟治疗效应的具体构造。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；历史数据示例含人工模拟效应，不能据此认定真实药物有效或普遍功效更高。
原文摘要
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.