ArticleJournal of clinical epidemiology2021
Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework.
Article in Journal of clinical epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 208 papers, 9 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
208 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Diagnostic Prediction Models for Depression in Patients With Breast Cancer: A Systematic Review.Journal of clinical nursing · 2026Pooled it
- Predictive Performance of Artificial Intelligence Algorithms for Gestational Diabetes Mellitus in Pregnant Women: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Reporting and handling of missing data in published studies of co-morbid hypertension and diabetes among people living with HIV/AIDS: a systematic review.BMC medical research methodology · 2025Pooled it
- A systematic review of mediation analysis frameworks in studies examining the determinants of cardiometabolic outcomes in people living with HIV.BMC medical research methodology · 2025Pooled it
- Handling Missing Data in Health Economics and Outcomes Research (HEOR): A Systematic Review and Practical Recommendations.PharmacoEconomics · 2023Pooled it
- Causal Model Building in the Context of Cardiac Rehabilitation: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- Predicting COVID-19 prognosis in the ICU remained challenging: external validation in a multinational regional cohort.Journal of clinical epidemiology · 2022Pooled it
- How to assess applicability and methodological quality of comparative studies of operative interventions in orthopedic trauma surgery.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2022Pooled it
- Potential Determinants of Cardio-Metabolic Risk among Aboriginal and Torres Strait Islander Children and Adolescents: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
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148 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.