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Statistical analysis and handling of missing data in cluster randomized trials: a systematic review
BACKGROUND: Cluster randomized trials (CRTs) randomize participants in groups, rather than as individuals and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomization is not feasible. Two potential major pitfalls exist regar...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748550/ https://www.ncbi.nlm.nih.gov/pubmed/26862034 http://dx.doi.org/10.1186/s13063-016-1201-z |
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author | Fiero, Mallorie H. Huang, Shuang Oren, Eyal Bell, Melanie L. |
author_facet | Fiero, Mallorie H. Huang, Shuang Oren, Eyal Bell, Melanie L. |
author_sort | Fiero, Mallorie H. |
collection | PubMed |
description | BACKGROUND: Cluster randomized trials (CRTs) randomize participants in groups, rather than as individuals and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomization is not feasible. Two potential major pitfalls exist regarding CRTs, namely handling missing data and not accounting for clustering in the primary analysis. The aim of this review was to evaluate approaches for handling missing data and statistical analysis with respect to the primary outcome in CRTs. METHODS: We systematically searched for CRTs published between August 2013 and July 2014 using PubMed, Web of Science, and PsycINFO. For each trial, two independent reviewers assessed the extent of the missing data and method(s) used for handling missing data in the primary and sensitivity analyses. We evaluated the primary analysis and determined whether it was at the cluster or individual level. RESULTS: Of the 86 included CRTs, 80 (93 %) trials reported some missing outcome data. Of those reporting missing data, the median percent of individuals with a missing outcome was 19 % (range 0.5 to 90 %). The most common way to handle missing data in the primary analysis was complete case analysis (44, 55 %), whereas 18 (22 %) used mixed models, six (8 %) used single imputation, four (5 %) used unweighted generalized estimating equations, and two (2 %) used multiple imputation. Fourteen (16 %) trials reported a sensitivity analysis for missing data, but most assumed the same missing data mechanism as in the primary analysis. Overall, 67 (78 %) trials accounted for clustering in the primary analysis. CONCLUSIONS: High rates of missing outcome data are present in the majority of CRTs, yet handling missing data in practice remains suboptimal. Researchers and applied statisticians should carry out appropriate missing data methods, which are valid under plausible assumptions in order to increase statistical power in trials and reduce the possibility of bias. Sensitivity analysis should be performed, with weakened assumptions regarding the missing data mechanism to explore the robustness of results reported in the primary analysis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13063-016-1201-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4748550 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47485502016-02-11 Statistical analysis and handling of missing data in cluster randomized trials: a systematic review Fiero, Mallorie H. Huang, Shuang Oren, Eyal Bell, Melanie L. Trials Research BACKGROUND: Cluster randomized trials (CRTs) randomize participants in groups, rather than as individuals and are key tools used to assess interventions in health research where treatment contamination is likely or if individual randomization is not feasible. Two potential major pitfalls exist regarding CRTs, namely handling missing data and not accounting for clustering in the primary analysis. The aim of this review was to evaluate approaches for handling missing data and statistical analysis with respect to the primary outcome in CRTs. METHODS: We systematically searched for CRTs published between August 2013 and July 2014 using PubMed, Web of Science, and PsycINFO. For each trial, two independent reviewers assessed the extent of the missing data and method(s) used for handling missing data in the primary and sensitivity analyses. We evaluated the primary analysis and determined whether it was at the cluster or individual level. RESULTS: Of the 86 included CRTs, 80 (93 %) trials reported some missing outcome data. Of those reporting missing data, the median percent of individuals with a missing outcome was 19 % (range 0.5 to 90 %). The most common way to handle missing data in the primary analysis was complete case analysis (44, 55 %), whereas 18 (22 %) used mixed models, six (8 %) used single imputation, four (5 %) used unweighted generalized estimating equations, and two (2 %) used multiple imputation. Fourteen (16 %) trials reported a sensitivity analysis for missing data, but most assumed the same missing data mechanism as in the primary analysis. Overall, 67 (78 %) trials accounted for clustering in the primary analysis. CONCLUSIONS: High rates of missing outcome data are present in the majority of CRTs, yet handling missing data in practice remains suboptimal. Researchers and applied statisticians should carry out appropriate missing data methods, which are valid under plausible assumptions in order to increase statistical power in trials and reduce the possibility of bias. Sensitivity analysis should be performed, with weakened assumptions regarding the missing data mechanism to explore the robustness of results reported in the primary analysis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13063-016-1201-z) contains supplementary material, which is available to authorized users. BioMed Central 2016-02-09 /pmc/articles/PMC4748550/ /pubmed/26862034 http://dx.doi.org/10.1186/s13063-016-1201-z Text en © Fiero et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Fiero, Mallorie H. Huang, Shuang Oren, Eyal Bell, Melanie L. Statistical analysis and handling of missing data in cluster randomized trials: a systematic review |
title | Statistical analysis and handling of missing data in cluster randomized trials: a systematic review |
title_full | Statistical analysis and handling of missing data in cluster randomized trials: a systematic review |
title_fullStr | Statistical analysis and handling of missing data in cluster randomized trials: a systematic review |
title_full_unstemmed | Statistical analysis and handling of missing data in cluster randomized trials: a systematic review |
title_short | Statistical analysis and handling of missing data in cluster randomized trials: a systematic review |
title_sort | statistical analysis and handling of missing data in cluster randomized trials: a systematic review |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748550/ https://www.ncbi.nlm.nih.gov/pubmed/26862034 http://dx.doi.org/10.1186/s13063-016-1201-z |
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