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

Autor: Lee KJ; Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Australia. Electronic address: Katherine.lee@mcri.edu.au., Tilling KM; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK., Cornish RP; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK., Little RJA; Department of Statistics, University of Michigan, MI, USA., Bell ML; Department of Epidemiology and Biostatistics, University of Arizona, AZ, USA., Goetghebeur E; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium., Hogan JW; Department of Biostatistics, Brown University, RI, USA., Carpenter JR; MRC Clinical Trials Unit, London School of Hygiene and Tropical Medicine, London, UK.
Jazyk: angličtina
Zdroj: Journal of clinical epidemiology [J Clin Epidemiol] 2021 Jun; Vol. 134, pp. 79-88. Date of Electronic Publication: 2021 Feb 02.
DOI: 10.1016/j.jclinepi.2021.01.008
Abstrakt: 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.
(Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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