Adaptive trial designs: a review of barriers and opportunities
Open accessAbstract
Adaptive designs allow planned modifications based on data accumulating within a study. The promise of greater flexibility and efficiency stimulates increasing interest in adaptive designs from clinical, academic, and regulatory parties. When adaptive designs are used properly, efficiencies can include a smaller sample size, a more efficient treatment development process, and an increased chance of correctly answering the clinical question of interest. However, improper adaptations can lead to biased studies. A broad definition of adaptive designs allows for countless variations, which creates confusion as to the statistical validity and practical feasibility of many designs. Determining properties of a particular adaptive design requires careful consideration of the scientific context and statistical assumptions. We first review several adaptive designs that garner the most current interest. We focus on the design principles and research issues that lead to particular designs being appealing or unappealing in particular applications. We separately discuss exploratory and confirmatory stage designs in order to account for the differences in regulatory concerns. We include adaptive seamless designs, which combine stages in a unified approach. We also highlight a number of applied areas, such as comparative effectiveness research, that would benefit from the use of adaptive designs. Finally, we describe a number of current barriers and provide initial suggestions for overcoming them in order to promote wider use of appropriate adaptive designs. Given the breadth of the coverage all mathematical and most implementation details are omitted for the sake of brevity. However, the interested reader will find that we provide current references to focused reviews and original theoretical sources which lead to details of the current state of the art in theory and practice.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Prespecified interim analyses can be used for other trial adaptations such as sample size re-estimation, alteration of the proportion of participants allocated to each study group, and changes to eligibility criteria.
“Aside from informing stopping guidelines, prespecified interim analyses can be used for other trial adaptations such as sample size re-estimation, alteration of the proportion of participants allocated to each study group, and changes to eligibility criteria.460”
From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [460]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 74 · Cited by 249
Research works printed but not linked: 2
- Kairalla JA Coffey CS Muller KE Achieving the benefits of both an internal pilot and interim analysis in large and small samples JSM Proceedings 2010 5239 5252
- Scientific Advances in Adaptive Clinical Trial Designs Workshop Planning Committee Scientific Advances in Adaptive Clinical Trial Designs Workshop Summary 2010 www.palladianpartners.com/adaptivedesigns/summary
- Authors
- Kairalla, J. A., Coffey, C. S., Thomann, M. A., & Muller, K. E.
- Affiliations
- University of Florida (US)
- University of Iowa (US)
- University of Florida Health (US)
- Corresponding author
- Kairalla, John A
- Venue
- Trials
- Published
- 2012-08-23
- Volume
- 13
- Issue
- 1
- Pages
- 145-145
- ISSN
- 1745-6215 (ISSN-L)
- PMC
- PMC3519822
- OpenAlex
- W2171364060
- Cited by
- 249
- References
- 74
- Citations by year
- 2026: 9 · 2025: 9 · 2024: 5 · 2023: 10 · 2022: 8 · 2021: 12 · 2020: 79 · 2019: 10 · 2018: 16 · 2017: 21 · 2016: 15 · 2015: 23 · 2014: 19 · 2013: 13
- FWCI
- 2.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Trials (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Florida
- China Scholarship Council
- National Institutes of Health (/ NIDDK R01, UL1TR000064, NHLBI R01, 5r01de020832-04)
- National Institute on Alcohol Abuse and Alcoholism (R01AA016549)
- National Institute on Aging (R01AG039495)
- National Heart, Lung, and Blood Institute (R01HL091005, HL091843, R01HL091843, R01-HL091843-04)
- National Institute on Drug Abuse (R01DA031017, DA031017)
- National Institute of Nursing Research
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK072398)
- National Institute of Neurological Disorders and Stroke (U01NS077108, U01NS038529, U01NS077352)
- National Institute of Dental and Craniofacial Research (R01DE020832-01A1, U54DE019261, R01-DE020832)
- National Center for Research Resources (UL1RR029890)
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Keywords
- adaptive trial design, clinical trials, sample size reduction, statistical validity, regulatory concerns, comparative effectiveness research, barriers to adoption
- MeSH terms
- Clinical Trials as Topic, Humans, Random Allocation, Research Design, Sample Size
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review