ArticleBMC medical research methodology2016
Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic.
Article in BMC medical research methodology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Efficacy and durability of multifactorial intervention on mortality and MACEs: a randomized clinical trial in type-2 diabetic kidney disease.Cardiovascular diabetology · 2021Trial
- A Cluster Randomized Controlled Trial Testing the Impact of Function and Behavior Focused Care for Nursing Home Residents With Dementia.Journal of the American Medical Directors Association · 2021Trial
- Trial
- Trial
- Increasing COVID-19 testing and vaccination among Spanish speakers in the USA: protocol for the development and evaluation of theBMJ open · 2022Article
- Testing Function Focused Care for Acute Care using the Evidence Integration Triangle: Protocol description.Research in nursing & health · 2022Article
- Interim data monitoring in cluster randomised trials: Practical issues and a case study.Clinical trials (London, England) · 2021Article
- ImbalanceClinical epidemiology · 2018Article
- Review of Recent Methodological Developments in Group-Randomized Trials: Part 2-Analysis.American journal of public health · 2017Review
- How large are the consequences of covariate imbalance in cluster randomized trials: a simulation study with a continuous outcome and a binary covariate at the cluster level.BMC medical research methodology · 2016Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundDespite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required.
methodsWe developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs.
resultsThe proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40% of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection.
conclusionThe proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs.
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