Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury.


Kreif, N; Grieve, R; Díaz, I; Harrison, D; (2015) Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury. Health economics, 24 (9). pp. 1213-28. ISSN 1057-9230 DOI: 10.1002/hec.3189

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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. © 2015 The Authors. Health Economics Published by John Wiley & Sons Ltd.<br/>

Item Type: Article
Faculty and Department: Faculty of Public Health and Policy > Dept of Health Services Research and Policy
Research Centre: Centre for Statistical Methodology
PubMed ID: 26059721
Web of Science ID: 359070000012
URI: http://researchonline.lshtm.ac.uk/id/eprint/2219522

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