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Choosing an imbalance metric for covariate-constrained randomization in multiple-arm cluster-randomized trials
BACKGROUND: In cluster-randomized controlled trials (C-RCTs), covariate-constrained randomization (CCR) methods efficiently control imbalance in multiple baseline cluster-level variables, but the choice of imbalance metric to define the subset of “adequately balanced” possible allocation schemes for...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6537428/ https://www.ncbi.nlm.nih.gov/pubmed/31138319 http://dx.doi.org/10.1186/s13063-019-3324-5 |
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author | Ciolino, Jody D. Diebold, Alicia Jensen, Jessica K. Rouleau, Gerald W. Koloms, Kimberly K. Tandon, Darius |
author_facet | Ciolino, Jody D. Diebold, Alicia Jensen, Jessica K. Rouleau, Gerald W. Koloms, Kimberly K. Tandon, Darius |
author_sort | Ciolino, Jody D. |
collection | PubMed |
description | BACKGROUND: In cluster-randomized controlled trials (C-RCTs), covariate-constrained randomization (CCR) methods efficiently control imbalance in multiple baseline cluster-level variables, but the choice of imbalance metric to define the subset of “adequately balanced” possible allocation schemes for C-RCTs involving more than two arms and continuous variables is unclear. In an ongoing three-armed C-RCT, we chose the min(three Kruskal–Wallis [KW] test P values) > 0.30 as our metric. We use simulation studies to explore the performance of this and other metrics of baseline variable imbalance in CCR. METHODS: We simulated three continuous variables across three arms under varying allocation ratios and assumptions. We compared the performance of min(analysis of variance [ANOVA] P value) > 0.30, min(KW P value) > 0.30, multivariate analysis of variance (MANOVA) P value > 0.30, min(nine possible t test P values) > 0.30, and min(Wilcoxon rank-sum [WRS] P values) > 0.30. RESULTS: Pairwise comparison metrics (t test and WRS) tended to be the most conservative, providing the smallest subset of allocation schemes (10%–13%) meeting criteria for acceptable balance. Sensitivity of the min(t test P values) > 0.30 for detecting non-trivial imbalance was 100% for both hypothetical and resampled simulation scenarios. The KW criterion maintained higher sensitivity than both the MANOVA and ANOVA criteria (89% to over 99%) but was not as sensitive as pairwise criteria. CONCLUSIONS: Our criterion, the KW P value > 0.30, to signify “acceptable” balance was not the most conservative, but it appropriately identified imbalance in the majority of simulations. Since all are related, CCR algorithms involving any of these imbalance metrics for continuous baseline variables will ensure robust simultaneous control over multiple continuous baseline variables, but we recommend care in determining the threshold of “acceptable” levels of (im)balance. TRIAL REGISTRATION: This trial is registered on ClinicalTrials.gov (initial post: December 1, 2016; identifier: NCT02979444). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13063-019-3324-5) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6537428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65374282019-05-30 Choosing an imbalance metric for covariate-constrained randomization in multiple-arm cluster-randomized trials Ciolino, Jody D. Diebold, Alicia Jensen, Jessica K. Rouleau, Gerald W. Koloms, Kimberly K. Tandon, Darius Trials Methodology BACKGROUND: In cluster-randomized controlled trials (C-RCTs), covariate-constrained randomization (CCR) methods efficiently control imbalance in multiple baseline cluster-level variables, but the choice of imbalance metric to define the subset of “adequately balanced” possible allocation schemes for C-RCTs involving more than two arms and continuous variables is unclear. In an ongoing three-armed C-RCT, we chose the min(three Kruskal–Wallis [KW] test P values) > 0.30 as our metric. We use simulation studies to explore the performance of this and other metrics of baseline variable imbalance in CCR. METHODS: We simulated three continuous variables across three arms under varying allocation ratios and assumptions. We compared the performance of min(analysis of variance [ANOVA] P value) > 0.30, min(KW P value) > 0.30, multivariate analysis of variance (MANOVA) P value > 0.30, min(nine possible t test P values) > 0.30, and min(Wilcoxon rank-sum [WRS] P values) > 0.30. RESULTS: Pairwise comparison metrics (t test and WRS) tended to be the most conservative, providing the smallest subset of allocation schemes (10%–13%) meeting criteria for acceptable balance. Sensitivity of the min(t test P values) > 0.30 for detecting non-trivial imbalance was 100% for both hypothetical and resampled simulation scenarios. The KW criterion maintained higher sensitivity than both the MANOVA and ANOVA criteria (89% to over 99%) but was not as sensitive as pairwise criteria. CONCLUSIONS: Our criterion, the KW P value > 0.30, to signify “acceptable” balance was not the most conservative, but it appropriately identified imbalance in the majority of simulations. Since all are related, CCR algorithms involving any of these imbalance metrics for continuous baseline variables will ensure robust simultaneous control over multiple continuous baseline variables, but we recommend care in determining the threshold of “acceptable” levels of (im)balance. TRIAL REGISTRATION: This trial is registered on ClinicalTrials.gov (initial post: December 1, 2016; identifier: NCT02979444). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13063-019-3324-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-28 /pmc/articles/PMC6537428/ /pubmed/31138319 http://dx.doi.org/10.1186/s13063-019-3324-5 Text en © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Ciolino, Jody D. Diebold, Alicia Jensen, Jessica K. Rouleau, Gerald W. Koloms, Kimberly K. Tandon, Darius Choosing an imbalance metric for covariate-constrained randomization in multiple-arm cluster-randomized trials |
title | Choosing an imbalance metric for covariate-constrained randomization in multiple-arm cluster-randomized trials |
title_full | Choosing an imbalance metric for covariate-constrained randomization in multiple-arm cluster-randomized trials |
title_fullStr | Choosing an imbalance metric for covariate-constrained randomization in multiple-arm cluster-randomized trials |
title_full_unstemmed | Choosing an imbalance metric for covariate-constrained randomization in multiple-arm cluster-randomized trials |
title_short | Choosing an imbalance metric for covariate-constrained randomization in multiple-arm cluster-randomized trials |
title_sort | choosing an imbalance metric for covariate-constrained randomization in multiple-arm cluster-randomized trials |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6537428/ https://www.ncbi.nlm.nih.gov/pubmed/31138319 http://dx.doi.org/10.1186/s13063-019-3324-5 |
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