ArticleMedical decision making : an international journal of the Society for Medical Decision Making2018
Using Observational Data to Calibrate Simulation Models.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Calibration of individual-based models to epidemiological data: A systematic review.PLoS computational biology · 2020Pooled it
- Incorporating social determinants of health into agent-based models of HIV transmission: methodological challenges and future directions.Frontiers in epidemiology · 2025Review
- Identification of optimum scopes of environmental drivers for schistosome-transmittingParasitology · 2024Article
- Projected Life Expectancy for Adolescents With HIV in the US.JAMA health forum · 2024Article
- Sociodemographic characteristics of women who died by suicide in India from 2014 to 2020: findings from surveillance data.The Lancet. Public health · 2023Article
- Causal evidence in health decision making: methodological approaches of causal inference and health decision science.German medical science : GMS e-journal · 2022Review
- Rapid-cycle systems modeling to support evidence-informed decision-making during system-wide implementation.Implementation science communications · 2021Article
- Emulating Target Trials to Improve Causal Inference From Agent-Based Models.American journal of epidemiology · 2021Article
- Complex systems models for causal inference in social epidemiology.Journal of epidemiology and community health · 2021Article
- The role of causal inference in health services research II: a framework for causal inference.International journal of public health · 2020Article
- The Challenges of Parameterizing Direct Effects in Individual-Level Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2020Article
- DAG-informed regression modelling, agent-based modelling and microsimulation modelling: a critical comparison of methods for causal inference.International journal of epidemiology · 2019Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundIndividual-level simulation models are valuable tools for comparing the impact of clinical or public health interventions on population health and cost outcomes over time. However, a key challenge is ensuring that outcome estimates correctly reflect real-world impacts. Calibration to targets obtained from randomized trials may be insufficient if trials do not exist for populations, time periods, or interventions of interest. Observational data can provide a wider range of calibration targets but requires methods to adjust for treatment-confounder feedback. We propose the use of the parametric g-formula to estimate calibration targets and present a case-study to demonstrate its application.
methodsWe used the parametric g-formula applied to data from the HIV-CAUSAL Collaboration to estimate calibration targets for 7-y risks of AIDS and/or death (AIDS/death), as defined by the Center for Disease Control and Prevention under 3 treatment initiation strategies. We compared these targets to projections from the Cost-effectiveness of Preventing AIDS Complications (CEPAC) model for treatment-naïve individuals presenting to care in the following year ranges: 1996 to 1999, 2000 to 2002, or 2003 onwards.
resultsThe parametric g-formula estimated a decreased risk of AIDS/death over time and with earlier treatment. The uncalibrated CEPAC model successfully reproduced targets obtained via the g-formula for baseline 1996 to 1999, but over-estimated calibration targets in contemporary populations and failed to reproduce time trends in AIDS/death risk. Calibration to g-formula targets improved CEPAC model fit for contemporary populations.
conclusionIndividual-level simulation models are developed based on best available information about disease processes in one or more populations of interest, but these processes can change over time or between populations. The parametric g-formula provides a method for using observational data to obtain valid calibration targets and enables updating of simulation model inputs when randomized trials are not available.
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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.