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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

Overview of attention for article published in BMC Medical Research Methodology, July 2016
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Title
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
Published in
BMC Medical Research Methodology, July 2016
DOI 10.1186/s12874-016-0182-7
Pubmed ID
Authors

Mirjam Moerbeek, Sander van Schie

Abstract

The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects. The consequences of covariance imbalance in unadjusted and adjusted linear mixed models are investigated by means of a simulation study. The factors in this study are the degree of imbalance, the covariate effect size, the cluster size and the intraclass correlation coefficient. The covariate is binary and measured at the cluster level; the outcome is continuous and measured at the individual level. The results show covariate imbalance results in negligible parameter bias and small standard error bias in adjusted linear mixed models. Ignoring the possibility of covariate imbalance while calculating the sample size at the cluster level may result in a loss in power of at most 25 % in the adjusted linear mixed model. The results are more severe for the unadjusted linear mixed model: parameter biases up to 100 % and standard error biases up to 200 % may be observed. Power levels based on the unadjusted linear mixed model are often too low. The consequences are most severe for large clusters and/or small intraclass correlation coefficients since then the required number of clusters to achieve a desired power level is smallest. The possibility of covariate imbalance should be taken into account while calculating the sample size of a cluster randomized trial. Otherwise more sophisticated methods to randomize clusters to treatments should be used, such as stratification or balance algorithms. All relevant covariates should be carefully identified, be actually measured and included in the statistical model to avoid severe levels of parameter and standard error bias and insufficient power levels.

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The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 26%
Student > Doctoral Student 5 19%
Student > Ph. D. Student 3 11%
Student > Master 3 11%
Lecturer 2 7%
Other 3 11%
Unknown 4 15%
Readers by discipline Count As %
Medicine and Dentistry 4 15%
Design 4 15%
Mathematics 4 15%
Sports and Recreations 3 11%
Nursing and Health Professions 2 7%
Other 5 19%
Unknown 5 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 September 2016.
All research outputs
#13,124,161
of 22,880,691 outputs
Outputs from BMC Medical Research Methodology
#1,223
of 2,022 outputs
Outputs of similar age
#183,483
of 354,317 outputs
Outputs of similar age from BMC Medical Research Methodology
#23
of 38 outputs
Altmetric has tracked 22,880,691 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,022 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.