TY - JOUR
T1 - Framework for the treatment and reporting of missing data in observational studies
T2 - The Treatment And Reporting of Missing data in Observational Studies framework
AU - the STRATOS initiative
AU - Lee, Katherine J.
AU - Tilling, Kate M.
AU - Cornish, Rosie P.
AU - Little, Roderick J.A.
AU - Bell, Melanie L.
AU - Goetghebeur, Els
AU - Hogan, Joseph W.
AU - Carpenter, James R.
N1 - Funding Information:
Funding: This work was supported by the Australian National Health and Medical Research Council (career development fellowship 1127984 to KJL ). The U.K. Medical Research Council and Wellcome (Grant ref: 102215/2/13/2 ) and the University of Bristol provide core support for ALSPAC. KT and RC work in the Integrative Epidemiology Unit which receives funding from the U.K. Medical Research Council and the University of Bristol (MC_UU_00,011/3 and MC_UU_00011/6). JC is supported by the U.K. Medical Research Council, grants MC UU 12023/21 and MC UU 12023/29.
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - ALSPAC
KW - Missing data
KW - Multiple imputation
KW - Observational studies
KW - Reporting
KW - STRATOS initiative
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UR - http://www.scopus.com/inward/citedby.url?scp=85101192471&partnerID=8YFLogxK
U2 - 10.1016/j.jclinepi.2021.01.008
DO - 10.1016/j.jclinepi.2021.01.008
M3 - Article
C2 - 33539930
AN - SCOPUS:85101192471
VL - 134
SP - 79
EP - 88
JO - Journal of Chronic Diseases
JF - Journal of Chronic Diseases
SN - 0895-4356
ER -