Department of Neurology, Division of Biostatistics, School of Medicine, Washington University, St. Louis, Missouri, USA.
Division of Biostatistics, Washington University, School of Medicine, St. Louis, Missouri, USA.
Alzheimers Dement. 2024 Aug;20(8):5421-5433. doi: 10.1002/alz.14035. Epub 2024 Jun 21.
Estimating treatment effects as time savings in disease progression may be more easily interpretable than assessing the absolute difference or a percentage reduction. In this study, we investigate the statistical considerations of the existing method for estimating time savings and propose alternative complementary methods.
We propose five alternative methods to estimate the time savings from different perspectives. These methods are applied to simulated clinical trial data that mimic or modify the Clinical Dementia Rating Sum of Boxes progression trajectories observed in the Clarity AD lecanemab trial.
Our study demonstrates that the proposed methods can generate more precise estimates by considering two crucial factors: (1) the absolute difference between treatment arms, and (2) the observed progression rate in the treatment arm.
Quantifying treatment effects as time savings in disease progression offers distinct advantages. To provide comprehensive estimations, it is important to use various methods.
We explore the statistical considerations of the current method for estimating time savings. We proposed alternative methods that provide time savings estimations based on the observed absolute differences. By using various methods, a more comprehensive estimation of time savings can be achieved.
相较于评估绝对差值或百分比降幅，将治疗效果估计为疾病进展时间节省可能更易于理解。本研究旨在探讨现有时间节省估计方法的统计考虑，并提出替代的补充方法。
我们提出了五种从不同角度估计时间节省的替代方法。这些方法应用于模拟临床试验数据，模拟或修改了在 Clarity AD 利斯单抗试验中观察到的临床痴呆评定总和盒进展轨迹。
我们的研究表明，所提出的方法通过考虑两个关键因素可以产生更精确的估计：（1）治疗组之间的绝对差异，以及（2）治疗组中观察到的进展速度。
将疾病进展中的治疗效果量化为时间节省具有明显优势。为提供全面的估计，使用各种方法非常重要。
我们探讨了当前估计时间节省的方法的统计考虑。我们提出了基于观察到的绝对差异的替代方法，提供了时间节省估计。通过使用各种方法，可以实现更全面的时间节省估计。
