ArticleStatistics in medicine2018
Using marginal structural models to adjust for treatment drop-in when developing clinical prediction models.
Article in Statistics in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study.BMJ (Clinical research ed.) · 2023Pooled it
- Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Dynamic Predictions and Predictimands for Salvage Therapy in Recurrent Prostate Cancer Using Joint Models.Statistics in medicine · 2026Article
- Development and validation of PRECISE-X model: predicting first severe exacerbation in COPD.Thorax · 2026Article
- Predicting disease progression under a "treatment-naive" counterfactual scenario in a competing risks framework.Frontiers in epidemiology · 2026Article
- Clinical implementation of an AI-based prediction model for decision support for patients undergoing colorectal cancer surgery.Nature medicine · 2025Article
- Counterfactual prediction from machine learning models: transportability and joint analysis for model development and evaluation using multi-source data.Diagnostic and prognostic research · 2025Article
- Estimating and Evaluating Counterfactual Prediction Models.Statistics in medicine · 2025Article
- Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model.The Lancet. Digital health · 2025Article
- Application of Dragonnet and Conformal Inference for Estimating Individualized Treatment Effects for Personalized Stroke Prevention: Retrospective Cohort Study.JMIR cardio · 2025Article
- Risk-Based Decision Making: Estimands for Sequential Prediction Under Interventions.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Tutorial: dos and don'ts in clinical prediction research for venous thromboembolism.Research and practice in thrombosis and haemostasis · 2024Article
- Making predictions under interventions: a case study from the PREDICT-CVD cohort in New Zealand primary care.Frontiers in epidemiology · 2024Article
- Assessing the transportability of clinical prediction models for cognitive impairment using causal models.BMC medical research methodology · 2023Article
- Should Cognitive Screening Tests Be Corrected for Age and Education? Insights From a Causal Perspective.American journal of epidemiology · 2023Article
- Predicting counterfactual risks under hypothetical treatment strategies: an application to HIV.European journal of epidemiology · 2022Article
- Article
- Prediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment.American journal of epidemiology · 2021Article
- Invited Commentary: Treatment Drop-in-Making the Case for Causal Prediction.American journal of epidemiology · 2021Article
- Management of superficial venous thrombosis based on individual risk profiles: protocol for the development and validation of three prognostic prediction models in large primary care cohorts.Diagnostic and prognostic research · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Clinical prediction models (CPMs) can inform decision making about treatment initiation, which requires predicted risks assuming no treatment is given. However, this is challenging since CPMs are usually derived using data sets where patients received treatment, often initiated postbaseline as "treatment drop-ins." This study proposes the use of marginal structural models (MSMs) to adjust for treatment drop-in. We illustrate the use of MSMs in the CPM framework through simulation studies that represent randomized controlled trials and real-world observational data and the example of statin initiation for cardiovascular disease prevention. The simulations include a binary treatment and a covariate, each recorded at two timepoints and having a prognostic effect on a binary outcome. The bias in predicted risk was examined in a model ignoring treatment, a model fitted on treatment-naïve patients (at baseline), a model including baseline treatment, and the MSM. In all simulation scenarios, all models except the MSM underestimated the risk of outcome given absence of treatment. These results were supported in the statin initiation example, which showed that ignoring statin initiation postbaseline resulted in models that significantly underestimated the risk of a cardiovascular disease event occurring within 10 years. Consequently, CPMs that do not acknowledge treatment drop-in can lead to underallocation of treatment. In conclusion, when developing CPMs to predict treatment-naïve risk, researchers should consider using MSMs to adjust for treatment drop-in, and also seek to exploit the ability of MSMs to allow estimation of individual treatment effects.
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