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.