JSM 2004 - Toronto

Abstract #301065

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Activity Number: 339
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #301065
Title: The Importance of Balance on Covariates in Clinical Trials: When Covariate-adjusted Analyses are Not Enough
Author(s): Brenda L. Gaydos*+
Companies: Eli Lilly and Company
Address: Lilly Corporate Center DC2233, Indianapolis, IN, 46285,
Keywords: ANCOVA ; stratification ; adaptive randomization
Abstract:

Stratification or adaptive randomization to insure balance on important covariates can add to the complexity and costs of a clinical trial. In addition, it has been argued that gains to inference above those achieved through covariate-adjusted analyses are minimal or nonexistent in many cases. Therefore, researchers often opt for simple randomization. It is crucial, however, not to underestimate the risk of imbalance on inference. In some cases, covariate-adjusted analyses can mislead investigators. The resulting p value will not be predictive of the risk in concluding drug effectiveness. Development of a promising new treatment may be stopped or development of an ineffective one continued inappropriately. The limitations of covariate-adjusted analyses in the presence of baseline imbalance for the normal model will be discussed. Examples of clinical trials will be provided where baseline imbalance was present but not statistically significant, yet the data was uninterpretable. Guidelines will be provided to help understand the risks of simple randomization when designing a trial, and the impact of imbalance on analyses when reporting results.

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