Proper analysis in clinical trials: how to report and adjust for missing outcome data
Abstract
Missing data is a problem that occurs widely in medical research, and indeed is difficult to avoid. The aim of this commentary is to deal with its implications in clinical trials. In analysing the results of a randomised controlled trial, it is important to ensure that the main benefit of randomisation has not been compromised: namely, that the treatment arms (experimental and control) remain comparable in everything except the interventions being compared.1 Missing outcome data is a serious problem because of its ability to bias the findings of a clinical trial. The bias affects the direction of the effect observed, but data loss can also affect the precision, making the results unreliable. A good definition of missing data is given by Little, ‘values that are not available and that would be meaningful for analysis if they were observed’.2 As our interest is in trials, we concentrate on missing outcome data, although methods have been developed to handle missing covariates as well. The bias because of missing data may depend on the reason why data are missing. We therefore have to consider the mechanism of how data comes to be lost.
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We will consider using multiple imputation procedures and other sensitivity analyses as an alternative to intent to treat analyses as needed.
“We will consider using multiple imputation procedures and other sensitivity analyses as an alternative to intent to treat analyses as needed [75].”
From Improving symptom management for survivors of young adult cancer: rationale and study protocol for a pilot randomized controlled trial (2024) · cites this paper as [75]
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Williams, L., Barnes, K., Ball, L., Ross, L., Sladdin, I., & Mitchell, L. (2019) · Healthcare · doi:10.3390/healthcare7010020 · Cited by 95 · see citing works
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“We will consider using multiple imputation procedures and other sensitivity analyses as an alternative to intent to treat analyses as needed [75].”
Method
- Authors
- Joshi, M., Royuela, A., & Zamora, J.
- Affiliations
- Queen Mary University of London (GB)
- Instituto Ramón y Cajal de Investigación Sanitaria (ES)
- Corresponding author
- Joshi, M
- ORCID
- Venue
- BJOG: An International Journal of Obstetrics & Gynaecology
- Publisher
- Wiley
- Published
- 2013-06-11
- Volume
- 120
- Issue
- 8
- Pages
- 915-919
- ISSN
- 0306-5456 (ISSN-L)
- 1470-0328
- 1471-0528
- OpenAlex
- W1966945631
- Cited by
- 10
- References
- 14
- Citations by year
- 2024: 2 · 2022: 1 · 2021: 1 · 2020: 1 · 2019: 2 · 2015: 2 · 2014: 1
- FWCI
- 0.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- BJOG An International Journal of Obstetrics & Gynaecology (publishedVersion)
- PubMed (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- missing data, missing outcome data, clinical trials, randomized controlled trials, bias, data loss, missing data mechanisms
- MeSH terms
- Clinical Trials as Topic, Humans, Research Design, Statistics as Topic, Bias, Treatment Outcome
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