Abstract
Clinical trial design requires assumptions. Prior data often serve as the basis for these assumptions. However, prior data may be limited or an inaccurate indication of future data. This may result in trials that are over-/under-powered. Interim analyses provide opportunities to evaluate the accuracy of the design assumptions and potentially make design adjustments if the assumptions are markedly inaccurate. We discuss sample size recalculation based on the observed intervention’s effects during interim analyses with a focus on the control of statistical error rates.
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Hamasaki, T., Asakura, K., Evans, S.R., Ochiai, T. (2016). Sample Size Recalculation in Clinical Trials with Two Co-primary Endpoints. In: Group-Sequential Clinical Trials with Multiple Co-Objectives. SpringerBriefs in Statistics(). Springer, Tokyo. https://doi.org/10.1007/978-4-431-55900-9_3
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DOI: https://doi.org/10.1007/978-4-431-55900-9_3
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Online ISBN: 978-4-431-55900-9
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