ArticleAmerican journal of epidemiology2019
Marginal Structural Models for Risk or Prevalence Ratios for a Point Exposure Using a Disease Risk Score.
Article in American journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comparison of disease risk score methods to study treatment effect heterogeneity: a simulation study.American journal of epidemiology · 2026Article
- Inverse Probability Weighting to Estimate Exposure Effects on the Burden of Recurrent Outcomes in the Presence of Competing Events.American journal of epidemiology · 2023Article
- On Variance of the Treatment Effect in the Treated When Estimated by Inverse Probability Weighting.American journal of epidemiology · 2022Article
- Bespoke Instruments: A new tool for addressing unmeasured confounders.American journal of epidemiology · 2022Article
- Standardizing Discrete-Time Hazard Ratios With a Disease Risk Score.American journal of epidemiology · 2020Article
- Cardiovascular disease risk among transgender women living with HIV in the United States.PloS one · 2020Article
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Authors and funding
4 authors.
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Abstract
The disease risk score is a summary score that can be used to control for confounding with a potentially large set of covariates. While less widely used than the exposure propensity score, the disease risk score approach might be useful for novel or unusual exposures, when treatment indications or exposure patterns are rapidly changing, or when more is known about the nature of how covariates cause disease than is known about factors influencing propensity for the exposure of interest. Focusing on the simple case of a binary point exposure, we describe a marginal structural model for estimation of risk (or prevalence) ratios. The proposed model incorporates the disease risk score as an offset in a regression model, and it yields an estimate of a standardized risk ratio where the target population is the exposed group. Simulations are used to illustrate the approach, and an empirical example is provided. Confounder control based on the proposed method might be a useful alternative to approaches based on the exposure propensity score, or as a complement to them.
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