ReviewAmerican journal of epidemiology2021
Common Methods for Handling Missing Data in Marginal Structural Models: What Works and Why.
Review in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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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.
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Who cites it
24 citing papers in PubMed, 37 citations in OpenAlex.
- Maternal postnatal depression and sensitive parenting: Considering developmental timing with a causally informative approach.Developmental psychology · 2026Article
- Mind the Gap: Addressing Missing Person Time When Estimating Outcome Incidence in Longitudinal Data.Epidemiology (Cambridge, Mass.) · 2026Article
- Validation of IHC4 + C for male breast cancer.Virchows Archiv : an international journal of pathology · 2026Article
- Time-Dynamic AI Models to Predict Quality of Life in Patients With Breast Cancer: Development and Validation Study Using the EORTC BALANCE Cohort.Journal of medical Internet research · 2026Article
- Article
- Interpersonal conflicts, social media use and depression associated with protests.Nature medicine · 2026Article
- Renin-Angiotensin System Inhibitors in Patients With Nonproteinuric Chronic Kidney Disease and Kidney Outcomes: Findings From the CRIC Study.Kidney medicine · 2026Article
- Digital Pharmacist-Physician Collaborative Care Management of Hypertension for Medicare Patients.Journal of general internal medicine · 2025Observational
- Comparative Effectiveness and Durability of Biologics Through 24 Months for Patients with Moderate-to-Severe Psoriasis: Results from the International, Observational Psoriasis Study of Health Outcomes (PSoHO).Dermatology and therapy · 2025Article
- Causal estimation of time-varying treatments in observational studies: a scoping review of methods, applications, and missing data practices.BMC medical research methodology · 2025Article
- Effectiveness of levonorgestrel implant and depot medroxyprogesterone acetate injectable for women with HIV on efavirenz.AIDS (London, England) · 2025Article
- Tying the Knot for Parents: How Adult Children's Marital Choices Impact Parental Depression in China.Innovation in aging · 2025Article
- Comprehensive strain assessment and mortality after acute myocardial infarction: a retrospective observational study based on the Essen Coronary Artery Disease registry.Heart (British Cardiac Society) · 2024Observational
- Allopurinol, Febuxostat, and Nonuse of Xanthine Oxidoreductase Inhibitor Treatment in Patients Receiving Hemodialysis: A Longitudinal Analysis.Kidney medicine · 2024Article
- Cardiac Effects of Renin-Angiotensin System Inhibitors in Nonproteinuric CKD.Hypertension (Dallas, Tex. : 1979) · 2024Article
- Observational pilot study on the influence of an app-based self-management program on the quality of life of women with endometriosis.Archives of gynecology and obstetrics · 2024Observational
- Moving Beyond Medical Statistics: A Systematic Review on Missing Data Handling in Electronic Health Records.Health data science · 2024Review
- Methods in causal inference. Part 3: measurement error and external validity threats.Evolutionary human sciences · 2024Article
- Addressing missing data in the estimation of time-varying treatments in comparative effectiveness research.Statistics in medicine · 2023Article
- Glucocorticoid exposure and the risk of serious infections in rheumatoid arthritis: a marginal structural model application.Rheumatology (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Marginal structural models (MSMs) are commonly used to estimate causal intervention effects in longitudinal nonrandomized studies. A common challenge when using MSMs to analyze observational studies is incomplete confounder data, where a poorly informed analysis method will lead to biased estimates of intervention effects. Despite a number of approaches described in the literature for handling missing data in MSMs, there is little guidance on what works in practice and why. We reviewed existing missing-data methods for MSMs and discussed the plausibility of their underlying assumptions. We also performed realistic simulations to quantify the bias of 5 methods used in practice: complete-case analysis, last observation carried forward, the missingness pattern approach, multiple imputation, and inverse-probability-of-missingness weighting. We considered 3 mechanisms for nonmonotone missing data encountered in research based on electronic health record data. Further illustration of the strengths and limitations of these analysis methods is provided through an application using a cohort of persons with sleep apnea: the research database of the French Observatoire Sommeil de la Fédération de Pneumologie. We recommend careful consideration of 1) the reasons for missingness, 2) whether missingness modifies the existing relationships among observed data, and 3) the scientific context and data source, to inform the choice of the appropriate method(s) for handling partially observed confounders in MSMs.
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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.