ArticleBMC medical research methodology2017
G-computation of average treatment effects on the treated and the untreated.
Article in BMC medical research methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 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
38 citing papers in PubMed.
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- Impacts of Industrial Crop Production by Smallholders on Household Food Security in Ghana: Evidence From the Oil Palm Sector.Plant-environment interactions (Hoboken, N.J.) · 2026Article
- Effects of a post-acute care management program on subsequent acute care utilization for hospitalized patients discharged to skilled nursing facilities.BMC health services research · 2026Article
- Structured psychiatric care and psychosocial support during placebo participation: association with violent and domestic-violence offending in the ReINVEST trial.Frontiers in psychiatry · 2026Article
- Investigating the role of chronic stress and perceived discrimination in racial disparities in incident dementia among U.S. older adults.Social science & medicine (1982) · 2025Article
- Unbound bilirubin and risk of severe neurodevelopmental impairment in extremely low birthweight newborns.Pediatric research · 2025Article
- Toward New Directions in Human Biology: A Roadmap for Anthropological Causal Inference With Observational Data.American journal of human biology : the official journal of the Human Biology Council · 2025Article
- Association between diabetes and heart failure after coronary artery bypass grafting: Danish register-based cohort study.Clinical research in cardiology : official journal of the German Cardiac Society · 2025Article
- Quantification of racial and ethnic disparities in alcohol-related problems in light of different methodological approaches.Addiction (Abingdon, England) · 2025Article
- Assessing racial disparities in healthcare expenditure using generalized propensity score weighting.BMC medical research methodology · 2025Article
- Matching plus regression adjustment for the estimation of the average treatment effect on survival outcomes: a case study with mosunetuzumab in relapsed/refractory follicular lymphoma.BMC medical research methodology · 2025Article
- Article
- A Review of Causal Inference Methods for Estimating the Effects of Exposure Change When Incident Exposure Is Unobservable.Current epidemiology reports · 2024Article
- Simulating the impact of greenspace exposure on metabolic biomarkers in a diverse population living in San Diego, California: A g-computation application.Environmental epidemiology (Philadelphia, Pa.) · 2024Article
- Comparing the performance of two-stage residual inclusion methods when using physician's prescribing preference as an instrumental variable: unmeasured confounding and noncollapsibility.Journal of comparative effectiveness research · 2024Article
- Comparing Perioperative Complications of Off-Clamp versus On-Clamp Partial Nephrectomy for Renal Cancer Using a Novel Energy Balancing Weights Method.Life (Basel, Switzerland) · 2024Article
- Model-based standardization using multiple imputation.BMC medical research methodology · 2024Article
- [Relationship Between Hearing Loss and Cognitive Function in Elderly Chinese People: A Study Based on Propensity Score Matching].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Article
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
backgroundAverage treatment effects on the treated (ATT) and the untreated (ATU) are useful when there is interest in: the evaluation of the effects of treatments or interventions on those who received them, the presence of treatment heterogeneity, or the projection of potential outcomes in a target (sub-) population. In this paper we illustrate the steps for estimating ATT and ATU using g-computation implemented via Monte Carlo simulation.
methodsTo obtain marginal effect estimates for ATT and ATU we used a three-step approach: fitting a model for the outcome, generating potential outcome variables for ATT and ATU separately, and regressing each potential outcome variable on treatment intervention.
resultsThe estimates for ATT, ATU and average treatment effect (ATE) were of similar magnitude, with ATE being in between ATT and ATU as expected. In our illustrative example, the effect (risk difference [RD]) of a higher education on angina among the participants who indeed have at least a high school education (ATT) was -0.019 (95% CI: -0.040, -0.007) and that among those who have less than a high school education in India (ATU) was -0.012 (95% CI: -0.036, 0.010).
conclusionsThe g-computation algorithm is a powerful way of estimating standardized estimates like the ATT and ATU. Its use should be encouraged in modern epidemiologic teaching and practice.
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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.