Alternative methods for testing treatment effects on the basis of multiple outcomes: Simulation and case study
Article first published online: 3 MAY 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 30, Issue 16, pages 1917–1932, 20 July 2011
How to Cite
Yoon, F. B., Fitzmaurice, G. M., Lipsitz, S. R., Horton, N. J., Laird, N. M. and Normand, S.-L. T. (2011), Alternative methods for testing treatment effects on the basis of multiple outcomes: Simulation and case study. Statist. Med., 30: 1917–1932. doi: 10.1002/sim.4262
- Issue published online: 15 JUN 2011
- Article first published online: 3 MAY 2011
- Manuscript Accepted: 16 MAR 2011
- Manuscript Received: 12 JUL 2010
- joint tests;
- multiple outcomes;
- missing data;
In clinical trials multiple outcomes are often used to assess treatment interventions. This paper presents an evaluation of likelihood-based methods for jointly testing treatment effects in clinical trials with multiple continuous outcomes. Specifically, we compare the power of joint tests of treatment effects obtained from joint models for the multiple outcomes with univariate tests based on modeling the outcomes separately. We also consider the power and bias of tests when data are missing, a common feature of many trials, especially in psychiatry. Our results suggest that joint tests capitalize on the correlation of multiple outcomes and are more powerful than standard univariate methods, especially when outcomes are missing completely at random. When outcomes are missing at random, test procedures based on correctly specified joint models are unbiased, while standard univariate procedures are not. Results of a simulation study are reported, and the methods are illustrated in an example from the Clinical Antipsychotic Trials of Intervention Effectiveness for schizophrenia. Copyright © 2011 John Wiley & Sons, Ltd.