ArticleHealth economics2015
Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury.
Article in Health economics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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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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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.
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Who cites it
21 citing papers in PubMed.
- The Rise of Health Economics: Transforming the Landscape of Economic Research.Health economics · 2026Article
- Precision ecology for targeted conservation action.Nature ecology & evolution · 2025Review
- Methods for Estimating the Exposure-Response Curve to Inform the New Safety Standards for Fine Particulate Matter.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025Article
- Causal Inference for Continuous Multiple Time Point Interventions.Statistics in medicine · 2024Article
- Generalized Propensity Score Methods to Assess CKD-Associated Physiologic Factors and Risk of Kidney Failure in the Chronic Renal Insufficiency Cohort (CRIC) Study.American journal of epidemiology · 2024Article
- Targeted learning in observational studies with multi-valued treatments: An evaluation of antipsychotic drug treatment safety.Statistics in medicine · 2024Article
- Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets.Health economics review · 2023Article
- Cortisol and periodontitis: Prospective observational and Mendelian randomization studies.Frontiers in endocrinology · 2023Observational
- An examination of machine learning to map non-preference based patient reported outcome measures to health state utility values.Health economics · 2022Article
- AUS-TBI: The Australian Health Informatics Approach to Predict Outcomes and Monitor Intervention Efficacy after Moderate-to-Severe Traumatic Brain Injury.Neurotrauma reports · 2022Article
- Core concepts in pharmacoepidemiology: Violations of the positivity assumption in the causal analysis of observational data: Consequences and statistical approaches.Pharmacoepidemiology and drug safety · 2021Review
- Propensity score stratification methods for continuous treatments.Statistics in medicine · 2021Article
- Intersections of machine learning and epidemiological methods for health services research.International journal of epidemiology · 2021Article
- A flexible hierarchical framework for improving inference in area-referenced environmental health studies.Biometrical journal. Biometrische Zeitschrift · 2020Article
- Evaluation of the health impacts of the 1990 Clean Air Act Amendments using causal inference and machine learning.Journal of the American Statistical Association · 2020Article
- Joint and independent neurotoxic effects of early life exposures to a chemical mixture: A multi-pollutant approach combining ensemble learning and g-computation.Environmental epidemiology (Philadelphia, Pa.) · 2019Article
- Using an artificial neural network to predict traumatic brain injury.Journal of neurosurgery. Pediatrics · 2019Article
- Model-averaged confounder adjustment for estimating multivariate exposure effects with linear regression.Biometrics · 2018Article
- Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms.Frontiers in neurology · 2018Article
- The Association between Dietary Vitamin A and Carotenes and the Risk of Primary Liver Cancer: A Case-Control Study.Nutrients · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
For a continuous treatment, the generalised propensity score (GPS) is defined as the conditional density of the treatment, given covariates. GPS adjustment may be implemented by including it as a covariate in an outcome regression. Here, the unbiased estimation of the dose-response function assumes correct specification of both the GPS and the outcome-treatment relationship. This paper introduces a machine learning method, the 'Super Learner', to address model selection in this context. In the two-stage estimation approach proposed, the Super Learner selects a GPS and then a dose-response function conditional on the GPS, as the convex combination of candidate prediction algorithms. We compare this approach with parametric implementations of the GPS and to regression methods. We contrast the methods in the Risk Adjustment in Neurocritical care cohort study, in which we estimate the marginal effects of increasing transfer time from emergency departments to specialised neuroscience centres, for patients with acute traumatic brain injury. With parametric models for the outcome, we find that dose-response curves differ according to choice of specification. With the Super Learner approach to both regression and the GPS, we find that transfer time does not have a statistically significant marginal effect on the outcomes.
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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.