Adaptive designs with correlated test statistics.

Full Abstract

In clinical trials, the collected observations such as clustered data or repeated measurements are often correlated. As a consequence, test statistics in a multistage design are correlated. Adaptive designs were originally developed for independent test statistics. We present a general framework for two-stage adaptive designs with correlated test statistics. We show that the significance level for the Bauer-Köhne design is inflated for positively correlated test statistics from a bivariate normal distribution. The decision boundary for the second stage can be modified so that type one error is controlled. This general concept is expandable to other adaptive designs. In order to use these designs, the correlation between test statistics has to be estimated. For a known covariance matrix, we show how correlation can be determined within the framework of linear mixed models. A sample size reassessment rule is proposed and evaluated for an unknown covariance matrix by simulation. As Wald test statistics in linear mixed models have independent increments, we use this property to create valid test procedures. We compare these procedures with the proposed design in our simulations. Copyright (c) 2009 John Wiley & Sons, Ltd.

Author information

Author/s: Götte, Heiko (H); Hommel, Gerhard (G); Faldum, Andreas (A);

Affiliation: Institute of Medical Biostatistics, Epidemiology and Informatics, University of Mainz, 55131 Mainz, Germany. goette(-atsign-)imbei.uni-mainz.de

Journal and publication information

Publication Type: Journal Article

Journal: Statistics in medicine (Stat Med), published in England. (Language: eng)

Reference: 2009-May; vol 28 (issue 10) : pp 1429-44

Dates: Created 2009/04/13; Completed 2009/07/01; Revised 2009/07/17;

PMID: 19226563, status: MEDLINE (last retrieval date: 7/25/2009, IMS Date: 25 Jul 2009 00:00:00)

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