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Italiano D, Campbell B, Hill MD, Johns HT, Churilov L. Adaptive Randomization Method to Prevent Extreme Instances of Group Size and Covariate Imbalance in Stroke Trials. Stroke 2024; 55:1962-1972. [PMID: 38920051 DOI: 10.1161/strokeaha.123.046269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/17/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND A recent review of randomization methods used in large multicenter clinical trials within the National Institutes of Health Stroke Trials Network identified preservation of treatment allocation randomness, achievement of the desired group size balance between treatment groups, achievement of baseline covariate balance, and ease of implementation in practice as critical properties required for optimal randomization designs. Common-scale minimal sufficient balance (CS-MSB) adaptive randomization effectively controls for covariate imbalance between treatment groups while preserving allocation randomness but does not balance group sizes. This study extends the CS-MSB adaptive randomization method to achieve both group size and covariate balance while preserving allocation randomness in hyperacute stroke trials. METHODS A full factorial in silico simulation study evaluated the performance of the proposed new CSSize-MSB adaptive randomization method in achieving group size balance, covariate balance, and allocation randomness compared with the original CS-MSB method. Data from 4 existing hyperacute stroke trials were used to investigate the performance of CSSize-MSB for a range of sample sizes and covariate numbers and types. A discrete-event simulation model created with AnyLogic was used to dynamically visualize the decision logic of the CSSize-MSB randomization process for communication with clinicians. RESULTS The proposed new CSSize-MSB algorithm uniformly outperformed the CS-MSB algorithm in controlling for group size imbalance while maintaining comparable levels of covariate balance and allocation randomness in hyperacute stroke trials. This improvement was consistent across a distribution of simulated trials with varying levels of imbalance but was increasingly pronounced for trials with extreme cases of imbalance. The results were consistent across a range of trial data sets of different sizes and covariate numbers and types. CONCLUSIONS The proposed adaptive CSSize-MSB algorithm successfully controls for group size imbalance in hyperacute stroke trials under various settings, and its logic can be readily explained to clinicians using dynamic visualization.
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Affiliation(s)
- Dominic Italiano
- Melbourne Medical School (D.I., H.T.J., L.C.), University of Melbourne, Parkville, Victoria, Australia
- Department of Medicine and Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital (B.C.), University of Melbourne, Parkville, Victoria, Australia
| | - Bruce Campbell
- Department of Medicine and Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital (B.C.), University of Melbourne, Parkville, Victoria, Australia
- Australian Stroke Alliance, Melbourne Brain Centre, Royal Melbourne Hospital, Victoria, Australia (D.I., B.C., H.T.J., L.C.)
| | - Michael D Hill
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Alberta, Canada (M.D.H.)
| | - Hannah T Johns
- Melbourne Medical School (D.I., H.T.J., L.C.), University of Melbourne, Parkville, Victoria, Australia
- Australian Stroke Alliance, Melbourne Brain Centre, Royal Melbourne Hospital, Victoria, Australia (D.I., B.C., H.T.J., L.C.)
| | - Leonid Churilov
- Melbourne Medical School (D.I., H.T.J., L.C.), University of Melbourne, Parkville, Victoria, Australia
- Australian Stroke Alliance, Melbourne Brain Centre, Royal Melbourne Hospital, Victoria, Australia (D.I., B.C., H.T.J., L.C.)
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Broderick JP, Mistry E. Evolution and Future of Stroke Trials. Stroke 2024; 55:1932-1939. [PMID: 38328974 PMCID: PMC11196204 DOI: 10.1161/strokeaha.123.044265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Affiliation(s)
- Joseph P. Broderick
- University of Cincinnati Gardner Neuroscience Institute, Department of Neurology and Rehabilitation Medicine, Cincinnati, Ohio, USA
| | - Eva Mistry
- University of Cincinnati Gardner Neuroscience Institute, Department of Neurology and Rehabilitation Medicine, Cincinnati, Ohio, USA
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Zhao W, Carter K, Sverdlov O, Scheffold A, Ryeznik Y, Cassarly C, Berger VW. Steady-state statistical properties and implementation of randomization designs with maximum tolerated imbalance restriction for two-arm equal allocation clinical trials. Stat Med 2024; 43:1194-1212. [PMID: 38243729 PMCID: PMC10925840 DOI: 10.1002/sim.10013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 11/01/2023] [Accepted: 01/04/2024] [Indexed: 01/21/2024]
Abstract
In recent decades, several randomization designs have been proposed in the literature as better alternatives to the traditional permuted block design (PBD), providing higher allocation randomness under the same restriction of the maximum tolerated imbalance (MTI). However, PBD remains the most frequently used method for randomizing subjects in clinical trials. This status quo may reflect an inadequate awareness and appreciation of the statistical properties of these randomization designs, and a lack of simple methods for their implementation. This manuscript presents the analytic results of statistical properties for five randomization designs with MTI restriction based on their steady-state probabilities of the treatment imbalance Markov chain and compares them to those of the PBD. A unified framework for randomization sequence generation and real-time on-demand treatment assignment is proposed for the straightforward implementation of randomization algorithms with explicit formulas of conditional allocation probabilities. Topics associated with the evaluation, selection, and implementation of randomization designs are discussed. It is concluded that for two-arm equal allocation trials, several randomization designs offer stronger protection against selection bias than the PBD does, and their implementation is not necessarily more difficult than the implementation of the PBD.
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Affiliation(s)
- Wenle Zhao
- Medical University of South Carolina, Charleston, SC, USA
| | - Kerstine Carter
- Boehringer-Ingelheim Pharmaceuticals Inc, Ridgefield, CT, USA
| | | | - Annika Scheffold
- Boehringer-Ingelheim Pharmaceuticals Inc, Ridgefield, CT, USA
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Yevgen Ryeznik
- Data Science & AI, R&D BioPharmaceuticals, AstraZeneca, Gothenburg, Sweden
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