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Abstract:
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Repeated measures of outcome, such as quality of life measurements, are important in clinical trials when assessing therapeutic benefits. Designing studies with repeated measures requires accurate specifications of the variances and correlations in order to select an appropriate sample size. Underspecifying the variances leads to a sample size that is inadequate to detect a meaningful scientific difference, while overspecifying the variances results in an unnecessary large sample size. Both lead to waste of resources and place study participants in unwarranted risk. We extend the internal pilot design, which allows for sample size re-estimation, to repeated measures, and we derive approximate distributions of the final sample size and the Un?ivariate Approach to Repeated Measures test statistic. Extensive simulations examine the impact of misspecification of the covariance matrix, and demonstrate the accuracy of the approximations in controlling the Type I error rate and achieving the target power. The proposed methods are applied to a clinical trial assessing early initiation of antiretroviral therapy for young adults living with HIV.
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