All Times EDT
Keywords: rare disease, hemophilia, multiple imputation; Rubin’s rule; Wilcoxon signed-rank test; inference; coverage probability; health-related quality of life endpoints
In a rare disease clinical trial setting we often encounter regulatory requirements to employ analysis methods to account for the small sample size as well as for missing values. Non-parametric analyses and Multiple Imputation (MI) are widely accepted as measures to address each concern. However, there is a paucity of literature on valid analyses techniques that tackle both concerns. In this article we present such analyses combining MI and the one-sample testing procedure using Wilcoxon signed-rank test and the accompanying estimation of Hodges-Lehman location parameter that can be applied to a hemophilia clinical trial. We review asymptotic behavior of the Wilcoxon signed-rank sum statistics that can be extended to multiple imputed data sets via Rubin’s rule. We propose ways of deriving the accompanying statistical inference for multiple imputed data sets. The type I error rate, power, and coverage probability of these proposals are evaluated for general robustness assessment via simulations under various scenarios. Our proposal extends to cases with substantial proportion of tied observations as encountered in Health-related Quality of Life endpoints which steadily gain importance in patient-centric drug development.