Comparing Missing Data Strategies for Generalized Pairwise Comparisons in Randomized Clinical Trials: A Simulation Study.

Abstract

Generalized pairwise comparisons are increasingly being used in randomized clinical trials. This study evaluates how missing data strategies impact the operating characteristics of endpoints combining overall survival and a longitudinal outcome. A simulation study was conducted to evaluate the impact of censoring and missing longitudinal data on the Type I error, power, and bias of a hierarchical composite endpoint. Seven missing data strategies were identified from the literature, including assigning ties to missing patient pairs, comparing patients at their last common visit, and applying multiple imputation. Conditional longitudinal and survival data were generated based on the natural history of amyotrophic lateral sclerosis. Simulation scenarios varied in censoring rates, extent of missing longitudinal data, differential attrition between treatment arms, and the dependency of missingness on disease severity. All methods were unbiased and maintained nominal Type I error when censoring and missingness were balanced across treatment arms. Under imbalance, however, Type I error increased-reaching up to 0.378 for some strategies. The last common visit strategy was the only approach that consistently preserved nominal error rates (0.025 ± 0.002). Regarding statistical power, all methods exhibited a loss of precision and increased bias under the alternative hypothesis as missingness increased. Multiple imputation partially recovered power and reduced bias but inflated the Type I error rate in scenarios with differential attrition. As generalized pairwise comparisons are increasingly used in pivotal clinical trials-and therefore in regulatory contexts-our findings provide practical guidance for selecting a robust primary analysis strategy and minimizing bias arising from incomplete data.