ArticleJournal of epidemiology2020
Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips.
Article in Journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Mortality Among Patients Undergoing Blood Transfusion in Relation to Donor Sex and Parity: A Natural Experiment.JAMA internal medicine · 2022Trial
- Loneliness patterns across time and subsequent risk of psychotic experiences, depression, anxiety, and diminished well-being in adolescents.Journal of child psychology and psychiatry, and allied disciplines · 2026Article
- An operational target trial emulation framework for causal inference using electronic health record data.NPJ digital medicine · 2026Review
- Depression may not be an independent risk factor for mortality in patients with cardiovascular disease: data from NHANES 2011-2018.Open heart · 2026Article
- Article
- Context Is Medicine: Integrating the Exposome into Neurorehabilitation.Brain sciences · 2025Article
- Difference-in-Differences for Health Policy and Practice: A Review of Modern Methods.Statistics in medicine · 2025Review
- A reappraisal of association between ventilator-associated events and mortality among critically ill patients using marginal structural model: multicenter observational study.Intensive care medicine · 2025Observational
- Unraveling Causality: Innovations in Epidemiologic Methods.JMA journal · 2025Article
- New Special Article Series: "Methodological Tutorial Series for Epidemiological Studies".Journal of epidemiology · 2025Article
- Scaling Interventions to Manage Chronic Disease: Innovative Methods at the Intersection of Health Policy Research and Implementation Science.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Changes in Glomerular Filtration Rate After Switching From Tenofovir Disoproxil Fumarate to Tenofovir Alafenamide Fumarate for Human Immunodeficiency Virus Preexposure Prophylaxis.Open forum infectious diseases · 2024Article
- Impact of type of anticoagulant on clinical outcomes in cancer patients who had atrial fibrillation.Scientific reports · 2023Article
- Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: A use case studying depression as a risk factor for Alzheimer's disease.Journal of biomedical informatics · 2023Article
- Impact of early heparin therapy on outcomes in patients with solid malignancy associated sepsis: a marginal structural model causal analyse.Frontiers in pharmacology · 2023Article
- Effectiveness of early heparin therapy on outcomes in critically ill patients with sepsis-induced coagulopathy.Frontiers in pharmacology · 2023Article
- Breastfeeding, pregnancy, medicines, neurodevelopment, and population databases: the information desert.International breastfeeding journal · 2022Review
- De-escalating adjuvant durvalumab treatment duration in stage III non-small cell lung cancer.European journal of cancer (Oxford, England : 1990) · 2022Article
- Article
- Estimating the causal effect of transient anemia status on renal and cardiovascular outcomes in community-dwelling patients in Japan at the beginning of impaired renal function using marginal structural modeling.Clinical and experimental nephrology · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epidemiologists are increasingly encountering complex longitudinal data, in which exposures and their confounders vary during follow-up. When a prior exposure affects the confounders of the subsequent exposures, estimating the effects of the time-varying exposures requires special statistical techniques, possibly with structural (ie, counterfactual) models for targeted effects, even if all confounders are accurately measured. Among the methods used to estimate such effects, which can be cast as a marginal structural model in a straightforward way, one popular approach is inverse probability weighting. Despite the seemingly intuitive theory and easy-to-implement software, misunderstandings (or "pitfalls") remain. For example, one may mistakenly equate marginal structural models with inverse probability weighting, failing to distinguish a marginal structural model encoding the causal parameters of interest from a nuisance model for exposure probability, and thereby failing to separate the problems of variable selection and model specification for these distinct models. Assuming the causal parameters of interest are identified given the study design and measurements, we provide a step-by-step illustration of generalized computation of standardization (called the g-formula) and inverse probability weighting, as well as the specification of marginal structural models, particularly for time-varying exposures. We use a novel hypothetical example, which allows us access to typically hidden potential outcomes. This illustration provides steppingstones (or "tips") to understand more concretely the estimation of the effects of complex time-varying exposures.
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