Exploring Heterogeneity in Cost-Effectiveness Using Machine Learning Methods: A Case Study Using the FIRST-ABC Trial

*Discipline of Economics, University of Galway, Galway, Ireland

†Department of Mathematics, An-Najah National University, Nablus, Palestine, London, UK

‡Department of Health Services Research & Policy, London School of Hygiene and Tropical Medicine, London, UK

§Section of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK

∥Paediatric Intensive Care Unit, St Mary’s Hospital, London, UK

The FIRST-ABC Step-Down trial was funded by the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme (project No.: 17/94/28). The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the HTA programme, NIHR, NHS or the Department of Health.

The research was supported by funding from the University of Galway Hardiman PhD scholarship scheme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The authors declare no conflict of interest.

Supplemental Digital Content is available for this article. Direct URL citations are provided in the HTML and PDF versions of this article on the journal’s website, www.lww-medicalcare.com.

Correspondence to: Stephen O’Neill, PhD, Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK. E-mail: [email protected].

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