Abstract–The COVID-19 pandemic has impacted ongoing clinical trials. We consider particular impacts on noninferiority clinical trials, which aim to show that an investigational treatment is not markedly worse than an existing active control with known benefit. Because interpretation of noninferiority trials requires cross-trial validation involving untestable assumptions, it is vital that they be run to very high standards. The COVID-19 pandemic has introduced an unexpected impact on clinical trials, with subjects possibly missing treatment or assessments due to unforeseen intercurrent events. The resulting data must be carefully considered to ensure proper statistical inference. Missing data can often, but not always, be considered missing completely at random (MCAR). We discuss ways to ensure validity of the analyses through study conduct and data analysis, with focus on the hypothetical strategy for constructing estimands. We assess various analytic strategies of analyzing longitudinal binary data with dropouts where outcomes may be MCAR or missing at random (MAR). Simulations show that certain multiple imputation strategies control the Type I error rate and provide additional power over analysis of observed data when data are MCAR or MAR, with weaker assumptions about the missing data mechanism.

摘要-COVID-19大流行影响了正在进行的临床试验。我们考虑了对非劣效性临床试验的特殊影响，其目的是表明研究治疗方法并不比具有已知益处的现有主动对照明显差。由于对非劣效性试验的解释需要涉及不可测假设的交叉试验验证，因此至关重要的是将其运行至很高的标准。COVID-19大流行对临床试验产生了意想不到的影响，由于意外的并发事件，受试者可能会错过治疗或评估。必须仔细考虑所得数据，以确保适当的统计推断。丢失数据经常（但并非总是）被视为随机完全丢失（MCAR）。我们讨论了通过研究行为和数据分析来确保分析有效性的方法，重点是构建估计量的假设策略。我们评估了各种分析策略，这些分析策略可用于分析纵向二进制数据，并可能导致结果为MCAR或随机丢失（MAR）。仿真表明，当数据为MCAR或MAR时，某些多重插补策略可控制I型错误率，并为分析观测数据提供额外的支持，而对丢失数据机制的假设则较弱。