Review of Rank-Based Procedures for Multicenter Clinical Trials

Abstract

This article reviews nonparametric alternatives to the mixed model normal theory analysis for the analyses of multicenter clinical trials. Under a mixed model, the traditional analysis is based on maximum likelihood theory under normal errors. This analysis, though, is not robust to outliers. Robust, rank-based, Wilcoxon-type procedures are reviewed for a multicenter clinical trial for the mixed model but without the assumption of normality. These procedures retain the high efficiency of Wilcoxon methods for simple location problems and are based on a fitting criterion which is robust to outliers in response space. A simple weighting scheme can be employed so that the procedures are robust to outliers in factor (design) space as well as response space. These rank-based analyses offer a complete analysis, including estimation of fixed effects and their standard errors, and tests of linear hypotheses. Both rank-based estimates of contrasts and individual treatment effects are reviewed. We illustrate the analyses using real data from a clinical trial.

On the efficacy of procedures to normalize Ex-Gaussian distributions

“…These authors also give useful recommendations as to how to analyze data when using general linear modeling approaches. Hence, a combination of the methods studied here with robust techniques could also be productive ( Rashid et al, 2013 , term this approach ‘side-by-side analyses’). Some researchers have taken these methods further.…”