Nonparametric testing methods based on relative effect in non-inferiority clinical trial with multiple experimental drugs

Conventional non-inferiority (NI) clinical trials often rely on large sample sizes and parametric methods with a single experimental drug. However, rare diseases pose challenges in obtaining large datasets, and data normality cannot always be assured. Nonparametric methods are a viable alternative, particularly when testing multiple experimental drugs with limited sample sizes. This paper reviews existing parametric and nonparametric NI testing methods and proposes a novel nonparametric NI method based on relative effect, a rank-based measure. Through simulations and real clinical trial data, we demonstrate that our proposed method outperforms existing ones in scenarios with small to moderate sample sizes and non-normal data. These findings highlight the potential of nonparametric approaches in NI clinical trials.