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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.

ABSTRACT: 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.

SUBMITTER: Bell ML

PROVIDER: S-EPMC7006144 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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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.

Bell Melanie L ML Rabe Brooke A BA

Trials 20200207 1

<h4>Background</h4>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.<h4>Methods</h4>We extended the MMRM to cluster randomized trials by adding a ra ...[more]

PMID: 32033617

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Project description:BACKGROUND:A stepped wedge cluster randomised trial (SWCRT) is a multicentred study which allows an intervention to be rolled out at sites in a random order. Once the intervention is initiated at a site, all participants within that site remain exposed to the intervention for the remainder of the study. The time since the start of the study ("calendar time") may affect outcome measures through underlying time trends or periodicity. The time since the intervention was introduced to a site ("exposure time") may also affect outcomes cumulatively for successful interventions, possibly in addition to a step change when the intervention began. METHODS:Motivated by a SWCRT of self-monitoring for bipolar disorder, we conducted a simulation study to compare model formulations to analyse data from a SWCRT under 36 different scenarios in which time was related to the outcome (improvement in mood score). The aim was to find a model specification that would produce reliable estimates of intervention effects under different scenarios. Nine different formulations of a linear mixed effects model were fitted to these datasets. These models varied in the specification of calendar and exposure times. RESULTS:Modelling the effects of the intervention was best accomplished by including terms for both calendar time and exposure time. Treating time as categorical (a separate parameter for each measurement time-step) achieved the best coverage probabilities and low bias, but at a cost of wider confidence intervals compared to simpler models for those scenarios which were sufficiently modelled by fewer parameters. Treating time as continuous and including a quadratic time term performed similarly well, with slightly larger variations in coverage probability, but narrower confidence intervals and in some cases lower bias. The impact of misspecifying the covariance structure was comparatively small. CONCLUSIONS:We recommend that unless there is a priori information to indicate the form of the relationship between time and outcomes, data from SWCRTs should be analysed with a linear mixed effects model that includes separate categorical terms for calendar time and exposure time. Prespecified sensitivity analyses should consider the different formulations of these time effects in the model, to assess their impact on estimates of intervention effects.

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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.

Publications

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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