[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123615-en":3,"doc-seo-123615-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},123615,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Adaptive Identification of Populations with Treatment Benefit in Clinical Trials - Machine Learning Challenges and Solutions","The study investigates adaptive identification of patient subpopulations that benefit from a treatment within confirmatory clinical trials, relaxing classical design restrictions by incorporating adaptive and online experimentation ideas from modern machine learning. The work highlights key challenges: selecting subpopulations with any treatment benefit under limited budget rather than only the largest effect, and requiring effectiveness to hold on average across subpopulations. Based on these insights, it introduces AdaGGI and AdaGCPI, meta-algorithms for subpopulation construction, and evaluates them across simulation scenarios to reveal advantages and limitations across settings.","Adaptive Identification of Populations with Treatment Benefit in Clinical Trials:  \nMachine Learning Challenges and Solutions  \nAlicia Curth 1 Alihan Hüyük 1 Mihaela van der Schaar 1 2  \narXiv :2208 .05844v2 [ stat .ML] 5 Jun 2023  \nAbstract  \nWe study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial. This type of adaptive clinical trial has been thoroughly studied in biostatistics, but has been allowed only limited adaptivity so far. Here, we aim to relax classical restrictions on such designs and investigate how to incorporate ideas from the recent machine learning literature on adaptive and online experimentation to make trials more flexible and efficient. We find that the unique characteristics of the subpopulation selection problem  \n– most importantly that (i) one is usually interested in finding subpopulations with any treatment benefit (and not necessarily the single subgroup with largest effect) given a limited budget and that (ii) effectiveness only has to be demonstrated across the subpopulation on average – give rise to interesting challenges and new desiderata when designing algorithmic solutions. Building on these findings, we propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction. We empirically investigate their performance across a range of simulation scenarios and derive insights into their (dis)advantages across different settings.  \n1. Introduction  \nThe existence of treatment effect heterogeneity across subgroups of patients poses a challenge to both the success of clinical trials testing the effectiveness of treatments and the quality of treatment decisions in clinical practice when prescribing a drug that has been proven to be effective only for the average population [1–3] . Examples for such het-  \n1Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK 2The Alan Turing Institute. Correspondence to: Alicia Curth \u003C[amc253@cam.ac.uk](amc253@cam.ac.uk)>.  \nProceedings of the 40 th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023 . Copyright 2023 by the author(s) .  \nerogeneity are ubiquituous in practice and include differences in treatment responses in cancer patients with specific mutations [4], pyschiatric patients with different forms of depression [5] and stroke patients [6] . Motivated by this, the problem of discovering treatment effect heterogeneity using logged experimental or observational data has received much attention in the recent machine learning (ML) literature [7], resulting in the adaptation of many supervised ML methods for post-hoc effect estimation [8–12] . The active counterpart to this problem, i.e. designing experiments (clinical trials) to actively discover subpopulations that respond well to a treatment, has received only limited attention in the ML literature thus far but is the focus of this paper.  \nThe biostatistics literature on adaptive clinical trials, on the other hand, has proposed and extensively studied the use of so-called adaptive enrichment designs, which allow to change both enrolment criteria and the null hypothesis to be tested in a clinical trial based on interim data (see e.g. [1, 2] and Appendix A.1 for an overview) . In such designs, the degree of adaptivity and flexibility is usually quite limited as the ability to adapt features is commonly restricted to a few pre-specified interim analysis points and the number of subgroups is often very small (most often set to exactly two) .  \nIn this paper, we consider a new approach to designing such adaptive enrichment trials and investigate whether and how it is possible to make them more flexible and efficient by adapting tools that were originally developed to solve pure exploration 1 multi-armed bandits [13] and other adaptive experiments problems in the recent ML literature. We find that the problem of constructing subpopulations from subg","cbCaiaDMPLmzWJaz","https://ap.wps.com/l/cbCaiaDMPLmzWJaz","pdf",929967,1,20,"English","en",105,"# Abstract\n# Introduction\n## Treatment effect heterogeneity\n## Adaptive enrichment designs and limitations\n## ML lens and new algorithmic challenges\n## Contributions and paper focus","[{\"question\":\"What problem does the paper address in clinical trials?\",\"answer\":\"It studies how to adaptively identify patient subpopulations that benefit from a given treatment during a confirmatory clinical trial.\"},{\"question\":\"How does the paper differ from classical adaptive enrichment trial designs?\",\"answer\":\"It aims to relax classical restrictions, making designs more flexible and efficient by adapting ideas from machine learning research on adaptive and online experimentation.\"},{\"question\":\"What are AdaGGI and AdaGCPI?\",\"answer\":\"They are two meta-algorithms proposed for constructing beneficial patient subpopulations, evaluated empirically across multiple simulation scenarios.\"}]","Adaptive Identification of Populations with Treatment Benefit in Clinical Trials - Machine Learning Challenges and Solutions | PDF",1785817647,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"adaptive-identification-of-populations-with-treatment-benefit-in-clinical-trials-machine-learning-challenges-and-solutions","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/adaptive-identification-of-populations-with-treatment-benefit-in-clinical-trials-machine-learning-challenges-and-solutions/123615/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in clinical trials?","Question",{"text":75,"@type":76},"It studies how to adaptively identify patient subpopulations that benefit from a given treatment during a confirmatory clinical trial.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper differ from classical adaptive enrichment trial designs?",{"text":80,"@type":76},"It aims to relax classical restrictions, making designs more flexible and efficient by adapting ideas from machine learning research on adaptive and online experimentation.",{"name":82,"@type":73,"acceptedAnswer":83},"What are AdaGGI and AdaGCPI?",{"text":84,"@type":76},"They are two meta-algorithms proposed for constructing beneficial patient subpopulations, evaluated empirically across multiple simulation scenarios.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]