Academic Journal
Adaptive design strategies for ethical and efficient clinical trials using simulated annealing.
| Τίτλος: | Adaptive design strategies for ethical and efficient clinical trials using simulated annealing. |
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| Συγγραφείς: | Frieri R; Department of Statistical Sciences P. Fortunati, University of Bologna, Bologna, Italy., Mariani F; Department of Statistical Sciences P. Fortunati, University of Bologna, Bologna, Italy., Novelli M; Department of Statistical Sciences P. Fortunati, University of Bologna, Bologna, Italy. |
| Πηγή: | Statistical methods in medical research [Stat Methods Med Res] 2026 Oct; Vol. 35 (10), pp. 2200-2219. Date of Electronic Publication: 2026 Sep 14. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: SAGE Publications Country of Publication: England NLM ID: 9212457 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1477-0334 (Electronic) Linking ISSN: 09622802 NLM ISO Abbreviation: Stat Methods Med Res Subsets: MEDLINE |
| Imprint Name(s): | Publication: London : SAGE Publications Original Publication: Sevenoaks, Kent, UK : Edward Arnold, c1992- |
| Ιατρικοί όροι (MeSH): | Randomized Controlled Trials as Topic*/ethics , Clinical Trials as Topic*/ethics , Research Design*, Humans ; Bayes Theorem ; Algorithms ; Computer Simulation ; Models, Statistical ; Random Allocation |
| Περίληψη: | We introduce a flexible adaptive design strategy for treatment allocation in clinical trials based on a randomized simulated annealing algorithm. The proposed approach provides a unified and modular framework for implementing a broad range of adaptive randomization objectives, including covariate-adaptive (CA), response-adaptive (RA), covariate-adjusted response-adaptive (CARA), and hybrid RA+CA and CARA+CA designs. The procedure accommodates multiple treatments, different outcome types, and any baseline covariate information available. Its implementation is compatible with both parametric and nonparametric predictive models; in this work, we use Bayesian additive regression trees to capture complex covariate-response relationships without committing to a low-dimensional parametric specification. The allocation rule combines a simulated-annealing-based recommendation with a fallback randomization step used to ensure positive assignment probabilities. In a finite stratified setting, we establish strong consistency of the resulting stratified sample mean estimators. Extensive simulation studies with homogeneous and heterogeneous treatment effects show that our proposal achieves a favorable trade-off between ethical allocation and statistical efficiency. A redesign of the ACTG 175 clinical trial further illustrates the practical advantages and flexibility of the proposed framework. |
| Competing Interests: | Declaration of conflicting interestThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. |
| Contributed Indexing: | Keywords: Covariate-adaptive procedures; covariate-adjusted response-adaptive designs; inferential efficiency; precision medicine; treatment by covariate interactions |
| Entry Date(s): | Date Created: 20260915 Date Completed: 20261008 Latest Revision: 20261008 |
| Update Code: | 20261009 |
| DOI: | 10.1177/09622802261488058 |
| PMID: | 42736680 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1477-0334 |
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| DOI: | 10.1177/09622802261488058 |