ArticleStatistics in medicine2021
Estimating optimal dynamic treatment strategies under resource constraints using dynamic marginal structural models.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Blood pressure thresholds for the administration of balanced crystalloids and the effect on intra-operative hypotension: A proof-of-concept causal inference analysis featuring idealised dynamic treatment regimens.European journal of anaesthesiology · 2026Observational
- Cardiovascular risk thresholds for intensifying primary care encounter frequency for patients with type 2 diabetes mellitus: a target trial emulation.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2025Article
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Authors and funding
4 authors.
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Abstract
Methods for estimating optimal treatment strategies typically assume unlimited access to resources. However, when a health system has resource constraints, such as limited funds, access to medication, or monitoring capabilities, medical decisions must account for competition between individuals in resource usage. The problem of incorporating resource constraints into optimal treatment strategies has been solved for point exposures (1), that is, treatment strategies entailing a decision at just one time point. However, attempts to directly generalize the point exposure solution to dynamic time-varying treatment strategies run into complications. We sidestep these complications by targeting the optimal strategy within a clinically defined subclass. Our approach is to employ dynamic marginal structural models to estimate (counterfactual) resource usage under the class of candidate treatment strategies and solve a constrained optimization problem to choose the optimal strategy for which expected resource usage is within acceptable limits. We apply this method to determine the optimal dynamic monitoring strategy for people living with HIV when resource limits on monitoring exist using observational data from the HIV-CAUSAL Collaboration.
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