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 appe...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-02-01
|
| Series: | Trials |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13063-020-4114-9 |
| _version_ | 1818661737992290304 |
|---|---|
| author | Melanie L. Bell Brooke A. Rabe |
| author_facet | Melanie L. Bell Brooke A. Rabe |
| author_sort | Melanie L. Bell |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T04:49:49Z |
| format | Article |
| id | doaj.art-38ba22016e4943908a3052580e16ec6c |
| institution | Directory Open Access Journal |
| issn | 1745-6215 |
| language | English |
| last_indexed | 2024-12-17T04:49:49Z |
| publishDate | 2020-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Trials |
| spelling | doaj.art-38ba22016e4943908a3052580e16ec6c2022-12-21T22:02:56ZengBMCTrials1745-62152020-02-0121111010.1186/s13063-020-4114-9The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous dataMelanie L. Bell0Brooke A. Rabe1Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of ArizonaDepartment of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of ArizonaAbstract 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.https://doi.org/10.1186/s13063-020-4114-9Missing dataDropoutVariance componentsIntention-to-treatCluster trialsGroup randomized trials |
| spellingShingle | Melanie L. Bell Brooke A. Rabe The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data Trials Missing data Dropout Variance components Intention-to-treat Cluster trials Group randomized trials |
| title | The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data |
| title_full | The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data |
| title_fullStr | The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data |
| title_full_unstemmed | The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data |
| title_short | The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data |
| title_sort | mixed model for repeated measures for cluster randomized trials a simulation study investigating bias and type i error with missing continuous data |
| topic | Missing data Dropout Variance components Intention-to-treat Cluster trials Group randomized trials |
| url | https://doi.org/10.1186/s13063-020-4114-9 |
| work_keys_str_mv | AT melanielbell themixedmodelforrepeatedmeasuresforclusterrandomizedtrialsasimulationstudyinvestigatingbiasandtypeierrorwithmissingcontinuousdata AT brookearabe themixedmodelforrepeatedmeasuresforclusterrandomizedtrialsasimulationstudyinvestigatingbiasandtypeierrorwithmissingcontinuousdata AT melanielbell mixedmodelforrepeatedmeasuresforclusterrandomizedtrialsasimulationstudyinvestigatingbiasandtypeierrorwithmissingcontinuousdata AT brookearabe mixedmodelforrepeatedmeasuresforclusterrandomizedtrialsasimulationstudyinvestigatingbiasandtypeierrorwithmissingcontinuousdata |