ArticleAmerican journal of epidemiology2017
A Comparison of Agent-Based Models and the Parametric G-Formula for Causal Inference.
Article in American journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Simulation Models to Track and Address the Opioid Crisis.Epidemiologic reviews · 2022Pooled it
- Disseminated Effects in Agent-Based Models: A Potential Outcomes Framework and Application to Inform Preexposure Prophylaxis Coverage Levels for HIV Prevention.American journal of epidemiology · 2021Trial
- Advancing systems thinking in implementation science: An epidemiologic perspective.Annals of epidemiology · 2026Article
- Challenges in estimating effects of hypothetical interventions on resources patterned by structural racism: an example in a rural North Carolina Medicaid population.American journal of epidemiology · 2026Article
- Article
- Incorporating social determinants of health into agent-based models of HIV transmission: methodological challenges and future directions.Frontiers in epidemiology · 2025Review
- Evaluation of Rhode Island's Early Geographic COVID-19 Vaccine Prioritization Policy.American journal of public health · 2024Article
- Article
- Causal Effects of Stochastic PrEP Interventions on HIV Incidence Among Men Who Have Sex With Men.American journal of epidemiology · 2024Article
- Modelling HIV/AIDS epidemiological complexity: A scoping review of Agent-Based Models and their application.PloS one · 2024Article
- Some principles for using epidemiologic study results to parameterize transmission models.medRxiv : the preprint server for health sciences · 2023Article
- Impact of Alcohol Consumption on Multiple Sclerosis Using Model-based Standardization and Misclassification Adjustment Via Probabilistic Bias Analysis.Archives of Iranian medicine · 2023Article
- Applications of agent-based modeling in trauma research.Psychological trauma : theory, research, practice and policy · 2023Article
- A case study and proposal for publishing directed acyclic graphs: The effectiveness of the quadrivalent human papillomavirus vaccine in perinatally HIV Infected girls.Journal of clinical epidemiology · 2022Article
- Dynamical Modeling as a Tool for Inferring Causation.American journal of epidemiology · 2022Article
- Causal evidence in health decision making: methodological approaches of causal inference and health decision science.German medical science : GMS e-journal · 2022Review
- Occupational Segregation And Hypertension Inequity: The Implication Of The Inverse Hazard Law Among Healthcare Workers.Journal of economics, race, and policy · 2022Article
- Countering the rise of syphilis: A role for doxycycline post-exposure prophylaxis?International journal of STD & AIDS · 2022Article
- G-Computation and Agent-Based Modeling for Social Epidemiology: Can Population Interventions Prevent Posttraumatic Stress Disorder?American journal of epidemiology · 2022Article
- Modeling the Impact of Social Determinants of Health on HIV.AIDS and behavior · 2021Article
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5 authors.
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Abstract
Decision-making requires choosing from treatments on the basis of correctly estimated outcome distributions under each treatment. In the absence of randomized trials, 2 possible approaches are the parametric g-formula and agent-based models (ABMs). The g-formula has been used exclusively to estimate effects in the population from which data were collected, whereas ABMs are commonly used to estimate effects in multiple populations, necessitating stronger assumptions. Here, we describe potential biases that arise when ABM assumptions do not hold. To do so, we estimated 12-month mortality risk in simulated populations differing in prevalence of an unknown common cause of mortality and a time-varying confounder. The ABM and g-formula correctly estimated mortality and causal effects when all inputs were from the target population. However, whenever any inputs came from another population, the ABM gave biased estimates of mortality-and often of causal effects even when the true effect was null. In the absence of unmeasured confounding and model misspecification, both methods produce valid causal inferences for a given population when all inputs are from that population. However, ABMs may result in bias when extrapolated to populations that differ on the distribution of unmeasured outcome determinants, even when the causal network linking variables is identical.
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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.