ArticleJournal of the American Statistical Association2019
Inverse probability weighted estimation of risk under representative interventions in observational studies.
Article in Journal of the American Statistical Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
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- Emulation of a Target Trial of Antihypertensive Medications on Weight Change.Journal of general internal medicine · 2026Observational
- Long-term memory effects of an incremental blood pressure intervention in a mortal cohort.Biometrics · 2026Article
- The effect of long-term adherence to physical activity recommendations in midlife on plasma proteins associated with frailty in the Atherosclerosis Risk in Communities (ARIC) study.European journal of epidemiology · 2026Article
- Comparative Weight Change With Initiation and Adherence to Common Medications for Type 2 Diabetes.Obesity (Silver Spring, Md.) · 2025Article
- Defining and Estimating Outcomes Directly Averted by a Vaccination Program when Rollout Occurs Over Time.ArXiv · 2025Article
- Demystifying Clone-Censor-Weighting to Studying Treatment Initiation Windows: An Example Using Publicly Available Synthetic Medicare Claims Data.Pharmacoepidemiology and drug safety · 2025Article
- Inverse probability weighting to estimate impacts of hypothetical occupational limits on radon exposure to reduce lung cancer.American journal of epidemiology · 2025Article
- Causal Inference for Continuous Multiple Time Point Interventions.Statistics in medicine · 2024Article
- A Review of Causal Inference Methods for Estimating the Effects of Exposure Change When Incident Exposure Is Unobservable.Current epidemiology reports · 2024Article
- Medication-Induced Weight Change Across Common Antidepressant Treatments : A Target Trial Emulation Study.Annals of internal medicine · 2024Observational
- Article
- Pancreatic Cancer Multidisciplinary Clinic is Associated with Improved Treatment and Elimination of Socioeconomic Disparities.Annals of surgical oncology · 2024Article
- Causal Effects of Stochastic PrEP Interventions on HIV Incidence Among Men Who Have Sex With Men.American journal of epidemiology · 2024Article
- Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models.Statistics in medicine · 2023Observational
- Intervention treatment distributions that depend on the observed treatment process and model double robustness in causal survival analysis.Statistical methods in medical research · 2023Article
- Use of a Doubly Robust Machine-Learning-Based Approach to Evaluate Body Mass Index as a Modifier of the Association Between Fruit and Vegetable Intake and Preeclampsia.American journal of epidemiology · 2022Article
- Early life exposure to greenness and executive function and behavior: An application of inverse probability weighting of marginal structural models.Environmental pollution (Barking, Essex : 1987) · 2021Article
- Separating Algorithms From Questions and Causal Inference With Unmeasured Exposures: An Application to Birth Cohort Studies of Early Body Mass Index Rebound.American journal of epidemiology · 2021Article
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Authors and funding
4 authors.
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Abstract
Researchers are often interested in using observational data to estimate the effect on a health outcome of maintaining a continuous treatment within a pre-specified range over time; e.g. "always exercise at least 30 minutes per day". There may be many precise interventions that could achieve this range. In this paper we consider representative interventions. These are special cases of random dynamic interventions; interventions under which treatment at each time is assigned according to a random draw from a distribution that may depend on a subject's measured past. Estimators of risk under representative interventions on a time-varying treatment have previously been described based on g-estimation of structural nested cumulative failure time models. In this paper, we consider an alternative approach based on inverse probability weighting (IPW) of marginal structural models. In particular, we show that the risk under a representative intervention on a time-varying continuous treatment can be consistently estimated via computationally simple IPW methods traditionally used for deterministic static (i.e. "nonrandom" and "nondynamic") interventions for binary treatments. We present an application of IPW in this setting to estimate the 28-year risk of coronary heart disease under various representative interventions on lifestyle behaviors in the Nurses Health Study.
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