ArticleTrials2019
Choosing an imbalance metric for covariate-constrained randomization in multiple-arm cluster-randomized trials.
Article in Trials, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02979444 (Comparing the Effectiveness of Clinicians and Paraprofessionals to Reduce Disparities in Perinatal Depression), which is not on this map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Comparing the Effectiveness of Clinicians and Paraprofessionals to Reduce Disparities in Perinatal Depression
Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- Comparing the effectiveness of home visiting paraprofessionals and mental health professionals delivering a postpartum depression preventive intervention: a cluster-randomized non-inferiority clinical trial.Archives of women's mental health · 2021Trial
- Cluster minimal sufficient balance (CMSB): an efficient covariate balancing randomization method for cluster randomized trials.BMC medical research methodology · 2026Article
- Effects of a postpartum depression intervention: subgroup analyses from a cluster randomized trial.Frontiers in psychiatry · 2026Article
- Article
- Article
- Covariate-constrained randomization in cluster randomized 2x2 factorial trials: Application to a diabetes prevention study.Research square · 2024Article
- SHARING Choices: Design and rationale for a pragmatic trial of an advance care planning intervention for older adults with and without dementia in primary care.Contemporary clinical trials · 2022Article
- Constrained randomization and statistical inference for multi-arm parallel cluster randomized controlled trials.Statistics in medicine · 2022Article
- Using the half normal distribution to quantify covariate balance in cluster-randomized pragmatic trials.Trials · 2021Article
- Application of randomization techniques for balancing site covariates in the adult day service plus pragmatic cluster-randomized trial.Contemporary clinical trials communications · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundIn 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.
methodsWe 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.
resultsPairwise 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.
conclusionsOur 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 registrationThis trial is registered on ClinicalTrials.gov (initial post: December 1, 2016; identifier: NCT02979444 ).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.