Multistage nonparametric tests for treatment comparisons in clinical trials with multiple primary endpoints

Peng Huang, Ming T. Tan

Research output: Contribution to journalArticle

Abstract

Many clinical trials, e.g., neurodegenerative disease trials, are conducted to test whether a new treatment could slow or modify disease progression. Multiple primary endpoints are often used since it is difficult to find a single clinical endpoint that summarizes the treatment effect, e.g., the neuroprotective effect. There are three major challenges in the design and analysis of such trials: (1) the presence of nuisance effect regardless whether the desired neuroprotective effect exists; (2) primary endpoints are of mixed type; (3) the need for interim analysis stopping rule for multiple primary endpoints. We propose a simple nonparametric multistage adaptive (group sequential) test to overcome these difficulties. Statistically, this test is another solution to the multivariate nonparametric Behrens-Fisher problem. We provide both large and small sample properties of the proposed test. The methodology is illustrated using data from two randomized clinical trials.

Original languageEnglish (US)
Pages (from-to)343-354
Number of pages12
JournalStatistics and its Interface
Volume9
Issue number3
DOIs
StatePublished - Jan 1 2016

Keywords

  • Adaptive group sequential test
  • Behrens- Fisher problem
  • Brownian motion
  • Rank-based test

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics

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