A two stage conditional power adaptive design adjusting for treatment by covariate interaction.
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.
Department of Biostatistics, University of Alabama at Birmingham, AL 35294-0022, USA. aayanlowo@mail.dopm.uab.edu
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D001699:Biometry; D002986:Clinical Trials as Topic / Q000379:methods*; D015233:Models, Statistical; D011336:Probability; D012107:Research Design; D018401:Sample Size