[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127700-en":3,"doc-seo-127700-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127700,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment - Published Version","Recent advances in causal inference and machine learning (ML) enable estimation of individualized treatment effects (ITEs), supporting patient-level understanding of treatment effectiveness variation by observed covariates. A scoping review assesses ML methods for estimating ITEs for use in health technology assessment, building a taxonomy around key observational-data challenges. Findings highlight strong performance in settings with baseline confounding and simple outcomes, while time-varying or unobserved confounding and uncertainty quantification remain limited for time-to-event settings. Results inform practical suitability and development needs.","This is a repository copy of Machine Learning Methods to Estimate Individualised Treatment Effects for Use in Health Technology Assessment.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/212661/](https://eprints.whiterose.ac.uk/212661/)  \nVersion: Published Version  \nArticle:  \nZhang, Yingying, Kreif, Noemi, S. Gc, Vijay et al. (1 more author) (2024) Machine Learning Methods to Estimate Individualised Treatment Effects for Use in Health Technology Assessment. Medical Decision Making. ISSN 1552-681X  \n[https://doi.org/10.1177/0272989X241263356](https://doi.org/10.1177/0272989X241263356)  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \n[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/)  \nMachine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment  \nYingying Zhang, Noemi Kreif, Vijay S. GC, and Andrea Manca  \nMedical Decision Making 1–14  \n􀀂 The Author(s) 2024  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/0272989X241263356](DOI: 10.1177/0272989X241263356)[ ](DOI: 10.1177/0272989X241263356)[journals.sagepub.com/home/mdm](journals.sagepub.com/home/mdm)  \nBackground. Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients’ observed covariates. ITEs can be used to stratify health policy decisions according to individual characteristics and potentially achieve greater population health. Little is known about the appropriateness of available ML methods for use in health technology assessment. Methods. In this scoping review, we evaluate ML methods available for estimating ITEs, aiming to help practitioners assess their suitability in health technology assessment. We present a taxonomy of ML approaches, categorized by key challenges in health technology assessment using observational data, including handling time-varying confounding and time-to event data and quantifying uncertainty. Results. We found a wide range of algorithms for simpler settings with baseline confounding and continuous or binary outcomes. Not many ML algorithms can handle time-varying or unobserved confounding, and at the time of writing, no ML algorithm was capable of estimating ITEs for time-to-event outcomes while accounting for time-varying confounding. Many of the ML algorithms that estimate ITEs in longitudinal settings do not formally quantify uncertainty around the point estimates. Limitations. This scoping review may not cover all relevant ML methods and algorithms as they are continuously evolving. Conclusions. Existing ML methods available for ITE estimation are limited in handling important challenges posed by observational data when used for cost-effectiveness analysis, such as time-to-event outcomes, time-varying and hidden confounding, or the need to estimate sampling uncertainty around the estimates. Implications. ML methods are promising but need further development before they can be used to estimate ITEs for health technology assessments.  \nHighlights  \n􀀂 Estimating individualized treatment effects (ITEs) using observational data ","cbCainkY2IbCEX5U","https://ap.wps.com/l/cbCainkY2IbCEX5U","pdf",610814,1,15,"English","en",105,"# Background\n# Methods\n## Taxonomy and evaluation focus\n# Results\n# Limitations\n# Conclusions\n# Implications\n# Highlights\n# Keywords","[{\"question\":\"What problem does the review address in health technology assessment?\",\"answer\":\"It evaluates how appropriate available machine learning methods are for estimating individualized treatment effects for use in health technology assessment, especially with observational data challenges.\"},{\"question\":\"What are the main limitations of current ML methods for ITE estimation identified in the review?\",\"answer\":\"Many algorithms handle baseline confounding but struggle with time-varying or unobserved confounding, and few can estimate ITEs for time-to-event outcomes while accounting for time-varying confounding or quantifying uncertainty.\"},{\"question\":\"How can individualized treatment effects support decision-making in health technology assessment?\",\"answer\":\"ITEs can help stratify policy decisions based on individual characteristics, potentially enabling more personalized treatment and information about effectiveness and cost-effectiveness.\"}]","Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment - 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