A two stage conditional power adaptive design adjusting for treatment by covariate interaction

Abstract During the design and planning phase of clinical trials, researchers often assume that no covariate by treatment interaction exists. This assumption has led to many trials being underpowered to detect such interactions and perhaps inaccurate interpretation of treatment effects. We propose a...

Full description

Saved in:
Bibliographic Details
Published in:Contemporary clinical trials 2008-05, Vol.29 (3), p.428-438
Main Authors: Ayanlowo, A.O, Redden, D.T
Format: Article
Language:English
Subjects:
Adaptive design
Biological and medical sciences
Biometry
Cardiovascular
Clinical trial. Drug monitoring
Clinical Trials as Topic - methods
Conditional power
Covariate by treatment interaction
General pharmacology
Hematology, Oncology and Palliative Medicine
Medical sciences
Models, Statistical
Pharmacology. Drug treatments
Probability
Research Design
Sample Size
Treatment effect
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract During the design and planning phase of clinical trials, researchers often assume that no covariate by treatment interaction exists. This assumption has led to many trials being underpowered to detect such interactions and perhaps inaccurate interpretation of treatment effects. We propose a two-stage adaptive design that incorporates the likely existence of a treatment by covariate interaction into the design and implementation of the clinical trial. The information in stage 1 is used to test for the presence of the covariate by treatment interaction. A statistically significant interaction influences how the second stage of the trial will be implemented, thereby aiding in the full understanding and consequently, an accurate interpretation of the treatment effect. We examine the statistical properties of the proposed design using a binary outcome under different types of covariate by treatment interactions and treatment allocation schemes. A conditional power approach is used to prevent inflation of the overall trial type I error rate while maintaining adequate statistical power conditional on the statistically significant interaction.
ISSN:1551-7144
1559-2030
DOI:10.1016/j.cct.2007.10.003