In comparative studies of progressive diseases, such as randomized controlled trials (RCTs), the mean Change From Baseline (CFB) of a continuous outcome at a pre-specified follow-up time across subjects in the target population is a standard estimand used to summarize the overall disease progression. Despite its simplicity in interpretation, the mean CFB may not efficiently capture important features of the trajectory of the mean outcome relevant to the evaluation of the treatment effect of an intervention. Additionally, the estimation of the mean CFB does not use all longitudinal data points. To address these limitations, we propose a class of estimands called Principal Progression Rate (PPR). The PPR is a weighted average of local or instantaneous slope of the trajectory of the population mean during the follow-up. The flexibility of the weight function allows the PPR to cover a broad class of intuitive estimands, including the mean CFB, the slope of ordinary least-square fit to the trajectory, and the area under the curve. We showed that properly chosen PPRs can enhance statistical power over the mean CFB by amplifying the signal of treatment effect and/or improving estimation precision. We evaluated different versions of PPRs and the performance of their estimators through numerical studies. A real dataset was analyzed to demonstrate the advantage of using alternative PPR over the mean CFB.

翻译：在渐进性疾病的比较研究（如随机对照试验）中，目标人群受试者在预设随访时间点连续结局指标的基线变化均值，是用于总结整体疾病进展的标准估计量。尽管其解释简单，但均值基线变化可能无法有效捕捉与干预措施治疗效果评估相关的结局均值轨迹的重要特征。此外，均值基线变化的估计并未利用所有纵向数据点。为应对这些局限，我们提出一类称为主进展率的估计量。主进展率是随访期间总体均值轨迹的局部或瞬时斜率的加权平均值。权重函数的灵活性使得主进展率能够涵盖一类广泛的直观估计量，包括均值基线变化、轨迹普通最小二乘拟合的斜率以及曲线下面积。我们证明，通过放大治疗效应信号和/或提高估计精度，恰当选择的主进展率能够较均值基线变化获得更高的统计功效。我们通过数值研究评估了不同版本的主进展率及其估计量的性能，并分析真实数据集以展示采用替代性主进展率相较于均值基线变化的优势。