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The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data
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The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data
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Journal Article
The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data
2020
Overview
Background
Cluster randomized trials (CRTs) are a design used to test interventions where individual randomization is not appropriate. The mixed model for repeated measures (MMRM) is a popular choice for individually randomized trials with longitudinal continuous outcomes. This model’s appeal is due to avoidance of model misspecification and its unbiasedness for data missing completely at random or at random.
Methods
We extended the MMRM to cluster randomized trials by adding a random intercept for the cluster and undertook a simulation experiment to investigate statistical properties when data are missing at random. We simulated cluster randomized trial data where the outcome was continuous and measured at baseline and three post-intervention time points. We varied the number of clusters, the cluster size, the intra-cluster correlation, missingness and the data-generation models. We demonstrate the MMRM-CRT with an example of a cluster randomized trial on cardiovascular disease prevention among diabetics.
Results
When simulating a treatment effect at the final time point we found that estimates were unbiased when data were complete and when data were missing at random. Variance components were also largely unbiased. When simulating under the null, we found that type I error was largely nominal, although for a few specific cases it was as high as 0.081.
Conclusions
Although there have been assertions that this model is inappropriate when there are more than two repeated measures on subjects, we found evidence to the contrary. We conclude that the MMRM for CRTs is a good analytic choice for cluster randomized trials with a continuous outcome measured longitudinally.
Trial registration
ClinicalTrials.gov, ID:
NCT02804698
.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Analysis
/ Bias
/ Cardiovascular Diseases - epidemiology
/ Cardiovascular Diseases - prevention & control
/ Data Interpretation, Statistical
/ Diabetes Complications - epidemiology
/ Diabetes Complications - prevention & control
/ Diabetes Mellitus - rehabilitation
/ Diseases
/ Dropout
/ Exercise
/ Female
/ Humans
/ Male
/ Medicine
/ Randomized Controlled Trials as Topic
/ Statistics for Life Sciences
/ Time
