ArticleEpidemiology (Cambridge, Mass.)2018
Exploring the Subtleties of Inverse Probability Weighting and Marginal Structural Models.
Article in Epidemiology (Cambridge, Mass.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- Estimating marginal structural model parameters for time-fixed, binary actions with g-computation and estimating equations.American journal of epidemiology · 2025Article
- Evaluation of the Effectiveness of Buprenorphine-Naloxone on Opioid Overdose and Death among Insured Patients with Opioid Use Disorder in the United States.Pharmacoepidemiology · 2022Article
- Causal effects of cingulate morphology on executive functions in healthy young adults.Human brain mapping · 2022Article
- Contribution of obesity and cardiometabolic risk factors in developing cardiovascular disease: a population-based cohort study.Scientific reports · 2022Article
- Metformin use and cirrhotic decompensation in patients with type 2 diabetes and liver cirrhosis.British journal of clinical pharmacology · 2022Article
- Estimating the effects of Mexico to U.S. migration on elevated depressive symptoms: evidence from pooled cross-national cohorts.Annals of epidemiology · 2021Article
- G-computation for policy-relevant effects of interventions on time-to-event outcomes.International journal of epidemiology · 2021Article
- Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips.Journal of epidemiology · 2020Article
- Article
- Comparison of Parametric and Nonparametric Estimators for the Association Between Incident Prepregnancy Obesity and Stillbirth in a Population-Based Cohort Study.American journal of epidemiology · 2019Article
- Causal criteria: time has come for a revision.European journal of epidemiology · 2019Article
- Optimal treatment for women with a persisting pregnancy of unknown location, a randomized controlled trial: The ACT-or-NOT trial.Contemporary clinical trials · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Since being introduced to epidemiology in 2000, marginal structural models have become a commonly used method for causal inference in a wide range of epidemiologic settings. In this brief report, we aim to explore three subtleties of marginal structural models. First, we distinguish marginal structural models from the inverse probability weighting estimator, and we emphasize that marginal structural models are not only for longitudinal exposures. Second, we explore the meaning of the word "marginal" in "marginal structural model." Finally, we show that the specification of a marginal structural model can have important implications for the interpretation of its parameters. Each of these concepts have important implications for the use and understanding of marginal structural models, and thus providing detailed explanations of them may lead to better practices for the field of epidemiology.
Indexed as
Identifiers
What OpenQuestion holds
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.