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Considerations on covariates and endpoints in multi‐arm multi‐stage clinical trials selecting all promising treatments
Author(s) -
Jaki T.,
Magirr D.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5669
Subject(s) - covariate , interim , orthogonality , computer science , confidence interval , statistics , set (abstract data type) , interim analysis , clinical trial , econometrics , medicine , mathematics , machine learning , geometry , archaeology , history , programming language
In early stages of drug development, there is often uncertainty about the most promising among a set of different treatments. To ensure the best use of resources in such situations, it is important to decide which, if any, of the treatments should be taken forward for further testing. In later development, it has been shown that evaluating more than one dose increases the chance of success substantially. In this work, we discuss how multi‐arm multi‐stage trials can be designed such that all promising treatments are kept in the study at the interim analyses. We first investigate the impact of deviating from the planned design and show how confidence intervals can be constructed before we consider the impact of important covariates. We show that under orthogonality, the inclusion of covariates has no effect on familywise error rate control in the strong sense. We further show that the derived methodology can be used to investigate non‐normal endpoints. Copyright © 2012 John Wiley & Sons, Ltd.