An application of minimization for ensuring balanced study arms in a group-randomized COVID-19 educational intervention trial
Background: Our institution carried out a multi-center, group-randomized controlled trial to evaluate the effectiveness of two community-based interventions on promoting the uptake of COVID-19 testing and vaccination in three regions with high levels of health disparities in Texas. We selected Censu...
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Elsevier
2025-04-01
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| Series: | Contemporary Clinical Trials Communications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2451865425000122 |
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| author | Xu Zhang Mohammad H. Rahbar Amirali Tahanan Cici Bauer Marcia C. de Oliveira Otto Alanna C. Morrison Belinda Reininger Maria E. Fernandez |
| author_facet | Xu Zhang Mohammad H. Rahbar Amirali Tahanan Cici Bauer Marcia C. de Oliveira Otto Alanna C. Morrison Belinda Reininger Maria E. Fernandez |
| author_sort | Xu Zhang |
| collection | DOAJ |
| description | Background: Our institution carried out a multi-center, group-randomized controlled trial to evaluate the effectiveness of two community-based interventions on promoting the uptake of COVID-19 testing and vaccination in three regions with high levels of health disparities in Texas. We selected Census Block Groups (CBGs) with high disparity for randomization. In each study region, selected CBGs were randomized into two intervention groups and one control group. An important goal was to ensure balanced distributions of two continuous covariates, the disparity index and population size, across study arms. In this paper, we describe a novel minimization method used to ensure balanced study arms. Methods: We employed a minimization method to balance distributions of disparity index and population size among the selected CBGs across three study groups. First, we used the means and standard deviations at the baseline to standardize covariates. Second, we used the maximum of pairwise Manhattan distances as the imbalance score. When randomizing a set of CBGs, we computed the imbalance scores for all possible assignments and used unequal allocation probabilities to implement randomization. We conducted a simulation study to evaluate the performance of the imbalance score. Results: In both the simulation study and the actual randomization results of the trial, minimization yielded balanced groups on the marginal distributions of the disparity index and population size. In the trial, the study groups were highly homogenous regarding the joint distribution of disparity index and population size (p = 0.91). Conclusion: The results indicate that the maximum of pairwise Manhattan distances is a practically useful imbalance score. Using this imbalance score, the minimization procedure satisfactorily balances the distribution of continuous covariates among three study groups. |
| format | Article |
| id | doaj-art-9c2df078148247aea5b92bb4960bd331 |
| institution | Kabale University |
| issn | 2451-8654 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Contemporary Clinical Trials Communications |
| spelling | doaj-art-9c2df078148247aea5b92bb4960bd3312025-08-20T03:42:53ZengElsevierContemporary Clinical Trials Communications2451-86542025-04-014410143810.1016/j.conctc.2025.101438An application of minimization for ensuring balanced study arms in a group-randomized COVID-19 educational intervention trialXu Zhang0Mohammad H. Rahbar1Amirali Tahanan2Cici Bauer3Marcia C. de Oliveira Otto4Alanna C. Morrison5Belinda Reininger6Maria E. Fernandez7Biostatistics/Epidemiology/Research Design (BERD) Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX, United States; Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, United States; Corresponding author. Biostatistics/Epidemiology/Research Design (BERD) Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX, United States.Biostatistics/Epidemiology/Research Design (BERD) Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX, United States; Department of Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, United StatesBiostatistics/Epidemiology/Research Design (BERD) Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX, United StatesDepartment of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States; Center for Spatial-Temporal Modeling for Applications in Population Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United StatesDepartment of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United StatesDepartment of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United StatesDepartment of Epidemiology, Human Genetics and Environmental Science, School of Public Health, The University of Texas Health Science Center at Houston, Brownsville, TX, United StatesDepartment of Health Promotion and Behavior Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Brownsville, TX, United StatesBackground: Our institution carried out a multi-center, group-randomized controlled trial to evaluate the effectiveness of two community-based interventions on promoting the uptake of COVID-19 testing and vaccination in three regions with high levels of health disparities in Texas. We selected Census Block Groups (CBGs) with high disparity for randomization. In each study region, selected CBGs were randomized into two intervention groups and one control group. An important goal was to ensure balanced distributions of two continuous covariates, the disparity index and population size, across study arms. In this paper, we describe a novel minimization method used to ensure balanced study arms. Methods: We employed a minimization method to balance distributions of disparity index and population size among the selected CBGs across three study groups. First, we used the means and standard deviations at the baseline to standardize covariates. Second, we used the maximum of pairwise Manhattan distances as the imbalance score. When randomizing a set of CBGs, we computed the imbalance scores for all possible assignments and used unequal allocation probabilities to implement randomization. We conducted a simulation study to evaluate the performance of the imbalance score. Results: In both the simulation study and the actual randomization results of the trial, minimization yielded balanced groups on the marginal distributions of the disparity index and population size. In the trial, the study groups were highly homogenous regarding the joint distribution of disparity index and population size (p = 0.91). Conclusion: The results indicate that the maximum of pairwise Manhattan distances is a practically useful imbalance score. Using this imbalance score, the minimization procedure satisfactorily balances the distribution of continuous covariates among three study groups.http://www.sciencedirect.com/science/article/pii/S2451865425000122Adaptive randomizationMinimizationImbalance score |
| spellingShingle | Xu Zhang Mohammad H. Rahbar Amirali Tahanan Cici Bauer Marcia C. de Oliveira Otto Alanna C. Morrison Belinda Reininger Maria E. Fernandez An application of minimization for ensuring balanced study arms in a group-randomized COVID-19 educational intervention trial Contemporary Clinical Trials Communications Adaptive randomization Minimization Imbalance score |
| title | An application of minimization for ensuring balanced study arms in a group-randomized COVID-19 educational intervention trial |
| title_full | An application of minimization for ensuring balanced study arms in a group-randomized COVID-19 educational intervention trial |
| title_fullStr | An application of minimization for ensuring balanced study arms in a group-randomized COVID-19 educational intervention trial |
| title_full_unstemmed | An application of minimization for ensuring balanced study arms in a group-randomized COVID-19 educational intervention trial |
| title_short | An application of minimization for ensuring balanced study arms in a group-randomized COVID-19 educational intervention trial |
| title_sort | application of minimization for ensuring balanced study arms in a group randomized covid 19 educational intervention trial |
| topic | Adaptive randomization Minimization Imbalance score |
| url | http://www.sciencedirect.com/science/article/pii/S2451865425000122 |
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