Efficiency Considerations for Group Sequential Designs with Adaptive Unblinded Sample Size Re-assessment

Lingyun Liu, Sam Hsiao and Cyrus R. Mehta ()
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Lingyun Liu: Cytel Corporation
Sam Hsiao: Cytel Corporation
Cyrus R. Mehta: Cytel Corporation

Statistics in Biosciences, 2018, vol. 10, issue 2, No 8, 405-419

Abstract: Abstract Clinical trials with adaptive sample size re-assessment, based on an analysis of the unblinded interim results (ubSSR), have gained in popularity due to uncertainty regarding the value of $$\delta $$ δ at which to power the trial at the start of the study. While the statistical methodology for controlling the type-1 error of such designs is well established, there remain concerns that conventional group sequential designs with no ubSSR can accomplish the same goals with greater efficiency. The precise manner in which this efficiency comparison can be objectified has been difficult to quantify, however. In this paper, we present a methodology for making this comparison in a standard, well-accepted manner by plotting the unconditional power curves of the two approaches while holding constant their expected sample size, at each value of $$\delta $$ δ in the range of interest. It is seen that under reasonable decision rules for increasing sample size (conservative promising zones, and no more than a 50% increase in sample size) there is little or no loss of efficiency for the adaptive designs in terms of unconditional power. The two approaches, however, have very different conditional power profiles. More generally, a methodology has been provided for comparing any design with ubSSR relative to a comparable group sequential design with no ubSSR, so one can determine whether the efficiency loss, if any, of the ubSSR design is offset by the advantages it confers for re-powering the study at the time of the interim analysis.

Keywords: Promising zone design; Adaptive design; Group sequential design; Power comparisons of adaptive versus non-adaptive; Conditional power (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s12561-017-9188-x

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