Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Stat Methods Med Res. 2019 May;28(5):1439-1456. doi: 10.1177/0962280218759565. Epub 2018 Mar 20.
Randomized trials with patient-reported outcomes are commonly plagued by missing data. The analysis of such trials relies on untestable assumptions about the missing data mechanism. To address this issue, it has been recommended that the sensitivity of the trial results to assumptions should be a mandatory reporting requirement. In this paper, we discuss a recently developed methodology (Scharfstein et al., Biometrics, 2018) for conducting sensitivity analysis of randomized trials in which outcomes are scheduled to be measured at fixed points in time after randomization and some subjects prematurely withdraw from study participation. The methodology is explicated in the context of a placebo-controlled randomized trial designed to evaluate a treatment for bipolar disorder. We present a comprehensive data analysis and a simulation study to evaluate the performance of the method. A software package entitled SAMON (R and SAS versions) that implements our methods is available at www.missingdatamatters.org .
随机对照试验中常因数据缺失而受到困扰。此类试验的分析依赖于对缺失数据机制的未经检验的假设。为了解决这个问题，已经建议将试验结果对假设的敏感性作为强制性报告要求。在本文中，我们讨论了一种最近开发的方法（Scharfstein 等人，Biometrics，2018），用于进行随机试验的敏感性分析，其中结局在随机分组后预定在固定时间点进行测量，并且一些受试者提前退出研究。该方法在一项安慰剂对照随机试验的背景下进行了阐述，该试验旨在评估一种双相情感障碍的治疗方法。我们提供了全面的数据分析和模拟研究，以评估该方法的性能。一个名为 SAMON（R 和 SAS 版本）的软件包可用于实施我们的方法，可在 www.missingdatamatters.org 获得。
