Original Article
Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework

https://doi.org/10.1016/j.jclinepi.2021.01.008Get rights and content
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Highlights

  • Missing data are ubiquitous in medical research.

  • Guidance is available, but missing data are still often not handled appropriately.

  • We present a framework for handling and reporting analyses of incomplete data.

  • This framework encourages researchers to think systematically about missing data.

  • Adoption of this framework will increase the reproducibility of research findings.

  • This article provides a much needed framework for handling and reporting the analysis of incomplete data in observational studies.

  • The framework puts a strong emphasis on preplanning the statistical analysis and encourages transparency when reporting the results of a study.

  • Adoption of this framework will increase the confidence in and reproducibility of research findings.

Abstract

Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. Importantly, the lack of transparency around methodological decisions is threatening the validity and reproducibility of modern research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. An important consideration is whether a complete records’ analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits and whether a sensitivity analysis regarding the missingness mechanism is required; 2) Examine the data, checking the methods outlined in the analysis plan are appropriate, and conduct the preplanned analysis; and 3) Report the results, including a description of the missing data, details on how the missing data were addressed, and the results from all analyses, interpreted in light of the missing data and the clinical relevance. This framework seeks to support researchers in thinking systematically about missing data and transparently reporting the potential effect on the study results, therefore increasing the confidence in and reproducibility of research findings.

Keywords

Missing data
Multiple imputation
Observational studies
Reporting
ALSPAC
STRATOS initiative

Cited by (0)

Conflicts of interest: None declared.

Authorship: All authors made substantial contributions to the conception and design of the study. K.J.L. drafted the manuscript, and all authors reviewed the manuscript for important intellectual content and approve the final version to be submitted.