Incorporating historical two-arm data in clinical trials with binary outcome: A practical approach.
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TL;DR: A frequentist framework is proposed that allows incorporation of historical data from both arms of two-armed trials with binary outcome, while simultaneously controlling the type I error rate, and how the required sample size can be reduced.
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Abstract: The feasibility of a new clinical trial may be increased by incorporating historical data of previous trials. In the particular case where only data from a single historical trial are available, there exists no clear recommendation in the literature regarding the most favorable approach. A main problem of the incorporation of historical data is the possible inflation of the type I error rate. A way to control this type of error is the so-called power prior approach. This Bayesian method does not "borrow" the full historical information but uses a parameter 0 ≤ δ ≤ 1 to determine the amount of borrowed data. Based on the methodology of the power prior, we propose a frequentist framework that allows incorporation of historical data from both arms of two-armed trials with binary outcome, while simultaneously controlling the type I error rate. It is shown that for any specific trial scenario a value δ > 0 can be determined such that the type I error rate falls below the prespecified significance level. The magnitude of this value of δ depends on the characteristics of the data observed in the historical trial. Conditionally on these characteristics, an increase in power as compared to a trial without borrowing may result. Similarly, we propose methods how the required sample size can be reduced. The results are discussed and compared to those obtained in a Bayesian framework. Application is illustrated by a clinical trial example.
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Citations
Prior elicitation for Gaussian spatial process: An application to TMS brain mapping
TL;DR: In this paper , the authors extend the power and commensurate prior distributions to a Gaussian spatial process, which enables the elicitation of prior knowledge from historical geostatistical data for Bayesian analysis.
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Comments on "Incorporating historical two-arm data in clinical trials with binary outcome: A practical approach" by Manuel Feißt, Johannes Krisam and Meinhard Kieser.
TL;DR: The results seem to contradict the findings by Kopp-Schneider et al, who showed that power gains from historical information are not possible when type I error rate should be controlled whenever a uniformly most powerful (UMP) test exists.
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TL;DR: In this paper , a probability-based power prior is proposed to determine the amount of information to be borrowed based on the agreement between the historical and current data, to make it applicable for either a single or multiple historical trials available.
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TL;DR: In this article , an exploratory platform trial design comparing combination therapies to the respective monotherapies and standard-of-care is presented, where the authors define a set of error rates and operating characteristics and then use these to compare the design parameters under a range of simulation assumptions.
References
Power gains by using external information in clinical trials are typically not possible when requiring strict type I error control.
TL;DR: It is shown that if prior information is conditioned upon and a uniformly most powerful test exists, strict control of type I error implies that no power gain is possible under any mechanism of incorporation of prior information, including dynamic borrowing.
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•Journal Article
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TL;DR: A Bayesian solution is provided by using simulation to approximately reconstruct the likelihood of the external summary and allowing the parameters in the model to vary under the different conditions by using fake-data simulations.
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