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In addition to scientific questions, clinical trialists often explore or require other design features, such as increasing the power while controlling the type I error rate, minimizing unnecessary exposure to inferior treatments, and comparing multiple treatments in one clinical trial. We propose implementing adaptive seamless design (ASD) with response adaptive randomization (RAR) to satisfy various clinical trials’ design objectives. However, the combination of ASD and RAR poses a challenge in controlling the type I error rate. In this paper, we investigated how to utilize the advantages of the two adaptive methods and control the type I error rate. We offered the theoretical foundation for this procedure. Numerical studies demonstrated that our methods could achieve efficient and ethical objectives while controlling the type I error rate.
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Causal estimators for incorporating external controls in randomized trials with longitudinal outcomes
Abstract Incorporating external data, such as external controls, holds the promise of improving the efficiency of traditional randomized controlled trials especially when treating rare diseases or diseases with unmet needs. To this end, we propose novel weighting estimators grounded in the causal inference framework. As an alternative framework, Bayesian methods are also discussed. From trial design perspective, operating characteristics including Type I error and power are particularly important and are assessed in our realistic simulation studies representing a variety of practical scenarios. Our proposed weighting estimators achieve significant power gain, while maintaining Type I error close to the nominal value of 0.05. An empirical application of the methods is demonstrated through a Phase III clinical trial in rare disease.
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- Award ID(s):
- 1934568
- PAR ID:
- 10555963
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of the Royal Statistical Society Series A: Statistics in Society
- ISSN:
- 0964-1998
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/jrsssa/qnae075
