Review of Rank-Based Procedures for Multicenter Clinical Trials

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...

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Bibliographic Details
Published in:Journal of biopharmaceutical statistics 2013-11, Vol.23 (6), p.1207-1227
Main Authors: Rashid, M. Mushfiqur, McKean, Joseph W., Kloke, John D.
Format: Article
Language:English
Subjects:
Analysis of Variance
Antihypertensive Agents - therapeutic use
Blood Pressure - drug effects
Clinical trials
Covariates
Criteria
Data Interpretation, Statistical
Design engineering
Error analysis
Estimates
Fittings
Humans
Hypertension - diagnosis
Hypertension - drug therapy
Hypertension - physiopathology
Likelihood Functions
Mathematical models
Mixed models
Models, Statistical
Multicenter Studies as Topic - statistics & numerical data
Nonparametric
Randomized Controlled Trials as Topic - statistics & numerical data
Rank regression scores
Repeated measures
Robust
Sample Size
Standard error
Statistics
Statistics, Nonparametric
Treatment Outcome
Variance components
Variances
Wilcoxon procedures
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Summary: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.
ISSN:1054-3406
1520-5711
DOI:10.1080/10543406.2013.834919