ArticleAmerican journal of epidemiology2022
The Role of the Natural Course in Causal Analysis.
Article in American journal of epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 17 citations in OpenAlex.
- Trial
- The effect of hypothetical interventions to limit weight gain on incident type 2 diabetes mellitus among people with HIV initiating antiretroviral therapy in the United States, 2012-2021.American journal of epidemiology · 2026Article
- Pooled Multinomial Logistic Regression for Parametric G-computation in the Presence of Competing Events.Epidemiology (Cambridge, Mass.) · 2026Article
- Estimating the effects of hypothetical loneliness interventions on memory function among middle-aged and older adults in the United States.American journal of epidemiology · 2026Article
- Effect Modification and Its Impact on Preventable and Attributable Fractions in the Potential Outcomes Framework.Journal of epidemiology · 2026Article
- Timing of urate-lowering therapy and risk of kidney failure and mortality in CKD: an application of the parametric G-formula.Clinical kidney journal · 2026Article
- Mind the Gap: Addressing Missing Person Time When Estimating Outcome Incidence in Longitudinal Data.Epidemiology (Cambridge, Mass.) · 2026Article
- A Framework for Thinking About the Potential Public Health Impact of Epidemiologic Research.Epidemiology (Cambridge, Mass.) · 2026Article
- Article
- Impact of interventions to prevent asbestos-related respiratory disease in an exposed worker registry using a simplified G-computation.American journal of epidemiology · 2026Article
- Article
- Effectiveness of sustained leisure-time physical activity strategies for obesity-related cancer prevention: an emulated target trial in a prospective US cohort.BMC medicine · 2025Article
- Empirical Sandwich Variance Estimator for Iterated Conditional Expectation g-Computation.Statistics in medicine · 2024Article
- Effects of an Intervention on Black Family Incomes on Self-Rated Health and Obesity in Black and White Adolescents and Young Adults.American journal of epidemiology · 2024Article
- Evaluating Model Specification When Using the Parametric G-Formula in the Presence of Censoring.American journal of epidemiology · 2023Article
- Estimating Causal Effects of Interventions on Early-life Environmental Exposures Using Observational Data.Current environmental health reports · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
The average causal effect compares counterfactual outcomes if everyone had been exposed versus if everyone had been unexposed, which can be an unrealistic contrast. Alternatively, we can target effects that compare counterfactual outcomes against the factual outcomes observed in the sample (i.e., we can compare against the natural course). Here, we demonstrate how the natural course can be estimated and used in causal analyses for model validation and effect estimation. Our example is an analysis assessing the impact of taking aspirin on pregnancy, 26 weeks after randomization, in the Effects of Aspirin in Gestation and Reproduction trial (United States, 2006-2012). To validate our models, we estimated the natural course using g-computation and then compared that against the observed incidence of pregnancy. We observed good agreement between the observed and model-based natural courses. We then estimated an effect that compared the natural course against the scenario in which participants assigned to aspirin always complied. If participants had always complied, there would have been 5.0 (95% confidence interval: 2.2, 7.8) more pregnancies per 100 women than was observed. It is good practice to estimate the natural course for model validation when using parametric models, but whether one should estimate a natural course contrast depends on the underlying research questions.
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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.