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Handling missing data in RCTs; a review of the top medical journals
BACKGROUND: Missing outcome data is a threat to the validity of treatment effect estimates in randomized controlled trials. We aimed to evaluate the extent, handling, and sensitivity analysis of missing data and intention-to-treat (ITT) analysis of randomized controlled trials (RCTs) in top tier med...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4247714/ https://www.ncbi.nlm.nih.gov/pubmed/25407057 http://dx.doi.org/10.1186/1471-2288-14-118 |
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author | Bell, Melanie L Fiero, Mallorie Horton, Nicholas J Hsu, Chiu-Hsieh |
author_facet | Bell, Melanie L Fiero, Mallorie Horton, Nicholas J Hsu, Chiu-Hsieh |
author_sort | Bell, Melanie L |
collection | PubMed |
description | BACKGROUND: Missing outcome data is a threat to the validity of treatment effect estimates in randomized controlled trials. We aimed to evaluate the extent, handling, and sensitivity analysis of missing data and intention-to-treat (ITT) analysis of randomized controlled trials (RCTs) in top tier medical journals, and compare our findings with previous reviews related to missing data and ITT in RCTs. METHODS: Review of RCTs published between July and December 2013 in the BMJ, JAMA, Lancet, and New England Journal of Medicine, excluding cluster randomized trials and trials whose primary outcome was survival. RESULTS: Of the 77 identified eligible articles, 73 (95%) reported some missing outcome data. The median percentage of participants with a missing outcome was 9% (range 0 – 70%). The most commonly used method to handle missing data in the primary analysis was complete case analysis (33, 45%), while 20 (27%) performed simple imputation, 15 (19%) used model based methods, and 6 (8%) used multiple imputation. 27 (35%) trials with missing data reported a sensitivity analysis. However, most did not alter the assumptions of missing data from the primary analysis. Reports of ITT or modified ITT were found in 52 (85%) trials, with 21 (40%) of them including all randomized participants. A comparison to a review of trials reported in 2001 showed that missing data rates and approaches are similar, but the use of the term ITT has increased, as has the report of sensitivity analysis. CONCLUSIONS: Missing outcome data continues to be a common problem in RCTs. Definitions of the ITT approach remain inconsistent across trials. A large gap is apparent between statistical methods research related to missing data and use of these methods in application settings, including RCTs in top medical journals. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2288-14-118) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4247714 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42477142014-11-30 Handling missing data in RCTs; a review of the top medical journals Bell, Melanie L Fiero, Mallorie Horton, Nicholas J Hsu, Chiu-Hsieh BMC Med Res Methodol Research Article BACKGROUND: Missing outcome data is a threat to the validity of treatment effect estimates in randomized controlled trials. We aimed to evaluate the extent, handling, and sensitivity analysis of missing data and intention-to-treat (ITT) analysis of randomized controlled trials (RCTs) in top tier medical journals, and compare our findings with previous reviews related to missing data and ITT in RCTs. METHODS: Review of RCTs published between July and December 2013 in the BMJ, JAMA, Lancet, and New England Journal of Medicine, excluding cluster randomized trials and trials whose primary outcome was survival. RESULTS: Of the 77 identified eligible articles, 73 (95%) reported some missing outcome data. The median percentage of participants with a missing outcome was 9% (range 0 – 70%). The most commonly used method to handle missing data in the primary analysis was complete case analysis (33, 45%), while 20 (27%) performed simple imputation, 15 (19%) used model based methods, and 6 (8%) used multiple imputation. 27 (35%) trials with missing data reported a sensitivity analysis. However, most did not alter the assumptions of missing data from the primary analysis. Reports of ITT or modified ITT were found in 52 (85%) trials, with 21 (40%) of them including all randomized participants. A comparison to a review of trials reported in 2001 showed that missing data rates and approaches are similar, but the use of the term ITT has increased, as has the report of sensitivity analysis. CONCLUSIONS: Missing outcome data continues to be a common problem in RCTs. Definitions of the ITT approach remain inconsistent across trials. A large gap is apparent between statistical methods research related to missing data and use of these methods in application settings, including RCTs in top medical journals. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2288-14-118) contains supplementary material, which is available to authorized users. BioMed Central 2014-11-19 /pmc/articles/PMC4247714/ /pubmed/25407057 http://dx.doi.org/10.1186/1471-2288-14-118 Text en © Bell et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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 Article Bell, Melanie L Fiero, Mallorie Horton, Nicholas J Hsu, Chiu-Hsieh Handling missing data in RCTs; a review of the top medical journals |
title | Handling missing data in RCTs; a review of the top medical journals |
title_full | Handling missing data in RCTs; a review of the top medical journals |
title_fullStr | Handling missing data in RCTs; a review of the top medical journals |
title_full_unstemmed | Handling missing data in RCTs; a review of the top medical journals |
title_short | Handling missing data in RCTs; a review of the top medical journals |
title_sort | handling missing data in rcts; a review of the top medical journals |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4247714/ https://www.ncbi.nlm.nih.gov/pubmed/25407057 http://dx.doi.org/10.1186/1471-2288-14-118 |
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