Academic Journal
Covariate-adaptive randomization with dropouts.
| Title: | Covariate-adaptive randomization with dropouts. |
|---|---|
| Authors: | Sung MH; Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA, USA., Xiao Q; Department of Statistics, Shanghai Jiao Tong University, Shanghai, China., Shen Y; Department of Epidemiology and Biostatistics, University of Georgia, Athens, GA, USA. |
| Source: | Statistical methods in medical research [Stat Methods Med Res] 2026 Sep; Vol. 35 (9), pp. 2024-2044. Date of Electronic Publication: 2026 Sep 10. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | 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- |
| MeSH Terms: | Randomized Controlled Trials as Topic*/statistics & numerical data , Randomized Controlled Trials as Topic*/methods , Patient Dropouts*/statistics & numerical data, Humans ; Sample Size ; Models, Statistical ; Random Allocation ; Computer Simulation ; Research Design |
| Abstract: | While clinical trials are often regarded as the gold standard by medical researchers and regulatory agencies, the statistical properties of typical randomized controlled trials heavily depend on large sample sizes, which may not always be achievable due to budget constraints or other practical limitations. To address this issue, adaptive designs have gained increasing attention since the 1970s. These designs incorporate modifications in trial procedures or statistical methodologies, aiming to fulfill study objectives more efficiently using smaller sample sizes and shorter durations compared to traditional clinical trials. Specifically, covariate-adaptive designs are developed to reduce the risk of significant covariate imbalances among treatment groups, particularly in studies with limited sample sizes. Additionally, patient dropout during trials significantly impacts statistical inference regarding treatment effects and diminishes statistical power. Ignoring dropout events can result in undesirable participant allocation and substantial treatment imbalances. In this article, we propose novel strategies that explicitly incorporate dropout information into covariate-adaptive randomization. We thoroughly investigate the statistical properties of covariate-adaptive designs adjusted for dropouts. Extensive simulation studies highlight the advantages of the proposed approaches. Our research lays a robust foundation for future developments in covariate-adaptive randomization, specifically addressing dropout challenges. |
| 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: Balancing covariates; clinical trial; dropouts; minimization; randomization |
| Entry Date(s): | Date Created: 20260910 Date Completed: 20260910 Latest Revision: 20260910 |
| Update Code: | 20260910 |
| DOI: | 10.1177/09622802261475671 |
| PMID: | 42720055 |
| Database: | MEDLINE |
| ISSN: | 1477-0334 |
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| DOI: | 10.1177/09622802261475671 |