Department of Preventive Medicine, Division of Biostatistics, Northwestern University, Chicago, Illinois.
The Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts.
Stat Med. 2019 Sep 30;38(22):4218-4239. doi: 10.1002/sim.8282. Epub 2019 Jul 23.
In March of 2018, the National Kidney Foundation, in collaboration with the US Food and Drug Administration and the European Medicines Agency, sponsored a workshop in which surrogate endpoints other than currently established event-time endpoints for clinical trials in chronic kidney disease (CKD) were presented and discussed. One such endpoint is a slope-based parameter describing the rate of decline in the estimated glomerular filtration rate (eGFR) over time. There are a number of challenges that can complicate such slope-based analyses in CKD trials. These include the possibility of an early but short-term acute treatment effect on the slope, both within-subject and between-subject heteroscedasticity, and informative censoring resulting from patient dropout due to death or onset of end-stage kidney disease. To address these issues, we first consider a class of mixed-effects models for eGFR that are linear in the parameters describing the mean eGFR trajectory but which are intrinsically nonlinear when a power-of-mean variance structure is used to model within-subject heteroscedasticity. We then combine the model for eGFR with a model for time to dropout to form a class of shared parameter models which, under the right specification of shared random effects, can minimize bias due to informative censoring. The models and methods of analysis are described and illustrated using data from two CKD studies one of which was one of 56 studies made available to the workshop analytical team. Lastly, methodology and accompanying software for prospectively determining sample size/power estimates are presented.
2018 年 3 月，美国肾脏基金会与美国食品药品监督管理局和欧洲药品管理局合作，举办了一次研讨会，会上介绍和讨论了除目前用于慢性肾脏病（CKD）临床试验的事件时间终点以外的替代终点。这样的终点之一是描述随时间估计肾小球滤过率（eGFR）下降速度的基于斜率的参数。在 CKD 试验中，基于斜率的分析可能会遇到许多复杂的挑战。这些挑战包括斜率上可能存在短期的急性治疗效应，以及个体内和个体间的异方差性，以及由于死亡或终末期肾病而导致患者脱落造成的信息性删失。为了解决这些问题，我们首先考虑了一类 eGFR 的混合效应模型，这些模型在描述平均 eGFR 轨迹的参数方面是线性的，但当使用平均方差结构对个体内异方差性进行建模时，它们本质上是非线性的。然后，我们将 eGFR 模型与时间到脱落模型相结合，形成一类共享参数模型，在共享随机效应的正确规范下，可以最小化信息性删失引起的偏差。使用来自两个 CKD 研究的数据描述和说明了模型和分析方法，其中一个研究是研讨会分析小组提供的 56 个研究之一。最后，提出了前瞻性确定样本量/功效估计的方法和伴随的软件。
