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000282347 1001_ $$0P:(DE-He78)bb6a7a70f976eb8df1769944bf913596$$aKopp-Schneider, Annette$$b0$$eFirst author$$udkfz
000282347 245__ $$aSimulating and reporting frequentist operating characteristics of clinical trials that borrow external information: Towards a fair comparison in case of one-arm and hybrid control two-arm trials.
000282347 260__ $$aNew York, NY$$bWiley$$c2024
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000282347 500__ $$a#EA:C060#LA:C060# / 2024 Jan-Feb;23(1):4-19
000282347 520__ $$aBorrowing information from historical or external data to inform inference in a current trial is an expanding field in the era of precision medicine, where trials are often performed in small patient cohorts for practical or ethical reasons. Even though methods proposed for borrowing from external data are mainly based on Bayesian approaches that incorporate external information into the prior for the current analysis, frequentist operating characteristics of the analysis strategy are often of interest. In particular, type I error rate and power at a prespecified point alternative are the focus. We propose a procedure to investigate and report the frequentist operating characteristics in this context. The approach evaluates type I error rate of the test with borrowing from external data and calibrates the test without borrowing to this type I error rate. On this basis, a fair comparison of power between the test with and without borrowing is achieved. We show that no power gains are possible in one-sided one-arm and two-arm hybrid control trials with normal endpoint, a finding proven in general before. We prove that in one-arm fixed-borrowing situations, unconditional power (i.e., when external data is random) is reduced. The Empirical Bayes power prior approach that dynamically borrows information according to the similarity of current and external data avoids the exorbitant type I error inflation occurring with fixed borrowing. In the hybrid control two-arm trial we observe power reductions as compared to the test calibrated to borrowing that increase when considering unconditional power.
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000282347 650_7 $$2Other$$aBayesian dynamic borrowing of information
000282347 650_7 $$2Other$$aexternal information
000282347 650_7 $$2Other$$afrequentist operating characteristics
000282347 650_7 $$2Other$$apower gain
000282347 650_7 $$2Other$$atype I error inflation
000282347 7001_ $$0P:(DE-He78)1042737c83ba70ec508bdd99f0096864$$aWiesenfarth, Manuel$$b1$$udkfz
000282347 7001_ $$aHeld, Leonhard$$b2
000282347 7001_ $$0P:(DE-He78)b5d9469407737829d5348adb615655c6$$aCalderazzo, Silvia$$b3$$eLast author$$udkfz
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