kb/data/en.wikipedia.org/wiki/Adaptive_design_(medicine)-3.md

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Adaptive design (medicine)	4/4	https://en.wikipedia.org/wiki/Adaptive_design_(medicine)	reference	science, encyclopedia	2026-05-05T09:48:46.125163+00:00	kb-cron

For regulatory submission of Bayesian clinical trial design, there exist two Bayesian decision rules that are frequently used by trial sponsors. First, posterior probability approach is mainly used in decision-making to quantify the evidence to address the question, "Does the current data provide convincing evidence in favor of the alternative hypothesis?" The key quantity of the posterior probability approach is the posterior probability of the alternative hypothesis being true based on the data observed up to the point of analysis. Second, predictive probability approach is mainly used in decision-making is to answer the question at an interim analysis: "Is the trial likely to present compelling evidence in favor of the alternative hypothesis if we gather additional data, potentially up to the maximum sample size (or current sample size)?" The key quantity of the predictive probability approach is the posterior predictive probability of the trial success given the interim data. In most regulatory submissions, Bayesian trial designs are calibrated to possess good frequentist properties. In this spirit, and in adherence to regulatory practice, regulatory agencies typically recommend that sponsors provide the frequentist type I and II error rates for the sponsor's proposed Bayesian analysis plan. In other words, the Bayesian designs for the regulatory submission need to satisfy the type I and II error requirement in most cases in the frequentist sense. Some exception may happen in the context of external data borrowing where the type I error rate requirement can be relaxed to some degree depending on the confidence of the historical information.

== Statistical analysis == The problem of adaptive clinical trial design is more or less exactly the bandit problem as studied in the field of reinforcement learning.

== Added complexity == The logistics of managing traditional, non-adaptive design clinical trials may be complex. In adaptive design clinical trials, adapting the design as results arrive adds to the complexity of design, monitoring, drug supply, data capture and randomization. Furthermore, it should be stated in the trial's protocol exactly what kind of adaptation will be permitted. Publishing the trial protocol in advance increases the validity of the final results, as it makes clear that any adaptation that took place during the trial was planned, rather than ad hoc. According to PCAST "One approach is to focus studies on specific subsets of patients most likely to benefit, identified based on validated biomarkers. In some cases, using appropriate biomarkers can make it possible to dramatically decrease the sample size required to achieve statistical significance—for example, from 1500 to 50 patients." Adaptive designs have added statistical complexity compared to traditional clinical trial designs. For example, any multiple testing, either from looking at multiple treatment arms or from looking at a single treatment arm multiple times, must be accounted for. Another example is statistical bias, which can be more likely when using adaptive designs, and again must be accounted for. While an adaptive design may be an improvement over a non-adaptive design in some respects (for example, expected sample size), it is not always the case that an adaptive design is a better choice overall: in some cases, the added complexity of the adaptive design may not justify its benefits. An example of this is when the trial is based on a measurement that takes a long time to observe, as this would mean having an interim analysis when many participants have started treatment but cannot yet contribute to the interim results.

== Risks == Shorter trials may not reveal longer term risks, such as a cancer's return.

== Resources (external links) == "What are adaptive clinical trials?" (video). youtube.com. Medical Research Council Biostatistics Unit. 17 November 2022. Burnett, Thomas; Mozgunov, Pavel; Pallmann, Philip; Villar, Sofia S.; Wheeler, Graham M.; Jaki, Thomas (2020). "Adding flexibility to clinical trial designs: An example-based guide to the practical use of adaptive designs". BMC Medicine. 18 (1): 352. doi:10.1186/s12916-020-01808-2. PMC 7677786. PMID 33208155. Jennison, Christopher; Turnbull, Bruce (1999). Group Sequential Methods with Applications to Clinical Trials. Taylor & Francis. ISBN 0-8493-0316-8. Wason, James M. S.; Brocklehurst, Peter; Yap, Christina (2019). "When to keep it simple – adaptive designs are not always useful". BMC Medicine. 17 (1): 152. doi:10.1186/s12916-019-1391-9. PMC 6676635. PMID 31370839. Wheeler, Graham M.; Mander, Adrian P.; Bedding, Alun; Brock, Kristian; Cornelius, Victoria; Grieve, Andrew P.; Jaki, Thomas; Love, Sharon B.; Odondi, Lang'o; Weir, Christopher J.; Yap, Christina; Bond, Simon J. (2019). "How to design a dose-finding study using the continual reassessment method". BMC Medical Research Methodology. 19 (1): 18. doi:10.1186/s12874-018-0638-z. PMC 6339349. PMID 30658575. Grayling, Michael John; Wheeler, Graham Mark (2020). "A review of available software for adaptive clinical trial design". Clinical Trials. 17 (3): 323–331. doi:10.1177/1740774520906398. PMC 7736777. PMID 32063024. S2CID 189762427.

== See also ==

== References ==

== Sources == Kurtz, Esfahani, Scherer (July 2019). "Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction". Cell. 178 (3): 699–713.e19. doi:10.1016/j.cell.2019.06.011. PMC 7380118. PMID 31280963.{{cite journal}}: CS1 maint: multiple names: authors list (link) President's Council of Advisors on Science and Technology (September 2012). "Report To The President on Propelling Innovation in Drug Discovery, Development and Evaluation" (PDF). Executive Office of the President. Archived (PDF) from the original on 21 January 2017. Retrieved 4 January 2014. Brennan, Zachary (5 June 2013). "CROs Slowly Shifting to Adaptive Clinical Trial Designs". Outsourcing-pharma.com. Archived from the original on 27 August 2017. Retrieved 5 January 2014. Spiegelhalter, David (April 2010). "Bayesian methods in clinical trials: Has there been any progress?" (PDF). Archived from the original (PDF) on 6 January 2014. Carlin, Bradley P. (25 March 2009). "Bayesian Adaptive Methods for Clinical Trial Design and Analysis" (PDF).

== External links == Gottlieb K. (2016) The FDA adaptive trial design guidance in a nutshell - A review in Q&A format for decision makers. PeerJ Preprints 4:e1825v1 [1] Coffey, C. S.; Kairalla, J. A. (2008). "Adaptive clinical trials: Progress and challenges". Drugs in R&D. 9 (4): 229–242. doi:10.2165/00126839-200809040-00003. PMID 18588354. S2CID 11861515. Center for Drug Evaluation and Research (CDER); Center for Biologics Evaluation and Research (CBER) (February 2010). "Adaptive Design Clinical Trials for Drugs and Biologics" (PDF). Food and Drug Administration. Archived from the original (PDF) on 5 January 2014. Yi, Cheng; Yu, Shen. "Bayesian Adaptive Designs for Clinical Trials" (PDF). M. D. Anderson. Berry, Scott M.; Carlin, Bradley P.; Lee, J. Jack; Muller, Peter (20 July 2010). Bayesian Adaptive Methods for Clinical Trials. CRC Press. ISBN 978-1-4398-2551-8. Berry on BAMCT on YouTube Press, W. H. (2009). "Bandit solutions provide unified ethical models for randomized clinical trials and comparative effectiveness research". Proceedings of the National Academy of Sciences. 106 (52): 22387–92. doi:10.1073/pnas.0912378106. PMC 2793317. PMID 20018711.