Exploration of the variation of treatment effect over time in randomized clinical trials with low event rates is limited by lack of power. A meta-analysis on individual patient data from such trials can partly solve the problem, but brings other computational difficulties. Using an example in hypert
POWER CONSIDERATIONS FOR CLINICAL TRIALS USING MULTIVARIATE TIME-TO-EVENT DATA
✍ Scribed by MICHAEL D. HUGHES
- Publisher
- John Wiley and Sons
- Year
- 1997
- Tongue
- English
- Weight
- 295 KB
- Volume
- 16
- Category
- Article
- ISSN
- 0277-6715
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✦ Synopsis
Many clinical trials involve the collection of data on the times to occurrence of different types of events, such as different fungal infections in AIDS research, or of recurrences of the same type, such as successive fits in epilepsy research. The multivariate proportional hazards model allows for analysis of this data and software for doing this is now widely available. In this paper, the approximate power of a clinical trial that aims to use such data for comparing two treatments is derived. Special attention is given to the bivariate case, both to show that the approximation works well and to illustrate how various design parameters affect the power of a trial. As with any multivariate data in clinical trials, there are many conceptual issues that should be considered during trial design; the paper closes with a discussion of some of these.
📜 SIMILAR VOLUMES
The principal response criteria for many clinical trials involve time-to-event variables. Usual methods of analysis for this type of response criterion include product-limit estimators of cumulative survival for the treatment groups, (stratified) logrank tests to compare treatments, and proportional