[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127014-en":3,"doc-seo-127014-105":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},127014,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Assisted Adjustment Boosts Efficiency of Exact Inference in Randomized Controlled Trials - Abstract","The study introduces a new inferential procedure for randomized controlled trials that uses machine-learning-assisted covariate adjustment within Rosenbaum’s exact-test framework. Simulation results show robust control of type I error and improved statistical efficiency for RCTs. The proposed approach is further illustrated through a real-world application and is designed to be simple, flexible, and robust, particularly when nonlinear covariate associations or interactions are expected. The method can reduce required sample size and cost, including for phase III studies.","arXiv :2403 .03058v2 [ stat .ME] 22 Jul 2024  \nMachine Learning Assisted Adjustment Boosts Efficiency of Exact Inference in Randomized  \nControlled Trials  \nHan Yu, Alan D. Hutson, Xiaoyi Ma ∗  \nAbstract  \nIn this work, we proposed a novel inferential procedure assisted by machine learning based adjustment for randomized control trials. The method was developed under the Rosenbaum’s framework of exact tests in randomized experiments with covariate adjustments. Through extensive simulation experiments, we showed the proposed method can robustly control the type I error and can boost the statistical efficiency for a randomized controlled trial (RCT) . This advantage was further demonstrated in a real-world example. The simplicity, flexibility, and robustness of the proposed method makes it a competitive candidate as a routine inference procedure for RCTs, especially when nonlinear association or interaction among covariates is expected. Its application may remarkably reduce the required sample size and cost of RCTs, such as phase III clinical trials.  \n∗ Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY 14623 . This work was supported by Roswell Park Cancer Institute and National Cancer Institute (NCI) grant P30CA016056, NRG Oncology Statistical and Data Management Center grant U10CA180822 and IOTN Moonshot grant U24CA232979-01, ARTNet Moonshot grant U24CA274159-01, CAP-IT grant U24CA274159-02 .  \n1 Introduction  \nThe randomized controlled trial (RCT) is the gold standard in determining treatment efficacy. RCTs are characterized by their extensive participant involvement, which typically encompass a substantial cohort size, particularly evident in phase III clinical trials. However, this rigorous process comes at a considerable cost. Especially for trials on immunotherapy, the cost of some of the newest therapies can reach $850,000 per patient when including the value of medical support necessary to deliver these treatments [1] . Given the significant investment required, maximizing the statistical power of RCTs becomes critical.  \nA common approach to improve the statistical efficiency is baseline adjustment using linear models, such as analysis of covariance (ANCOVA) . Related models and their applications in RCT has been extensively discussed [2, 3] . The method of covariate adjustments is supported by FDA in phase III oncology trials due to the enhanced efficiency and its minimal impact on bias or type I error rate [5] . Hence, the agency advises sponsors to incorporate adjustments for covariates expected to show the strong association with the outcome of interest. In 2002, Rosenbaum proposed method of exact inference which is free of distributional assumptions [4] . This method is endorsed by the FDA guidance, which states that ”Sponsors can conduct randomization/permutation tests with covariate adjustment (Rosenbaum 2002)”. Although this method was based on ordinary least square (OLS) linear regression, the author raised possibility of using more general forms of covariate adjustment.  \nNowadays, due to the ever-decreasing cost of medical testing and sequencing techniques, the number of baseline data available in clinical trials has been growing dramatically. The rich data can provide a much finer picture of individuals and thus capture larger variation in the outcomes. This makes it possible to design inferential procedures with superior efficiency. However, the traditional adjustment methods using OLS method quickly break down when the number of covariates increases to a relatively large scale. Even if penalized regressions [6, 7, 8, 9] can accommodate high-dimensional data,  \nsuch linear models are neither capable of handling nonlinear associations nor complex interactions.  \nOn the other hand, machine learning techniques have been successfully applied in numerous settings during the last decade. Supervised learning methods such as random forests (RFs), b","cbCailimoXwfEwdL","https://ap.wps.com/l/cbCailimoXwfEwdL","pdf",12687707,1,30,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 Methods\n## 2.1 Hypothesis testing under non-parametric adjustment","[{\"question\":\"What problem does the proposed method address in randomized controlled trials?\",\"answer\":\"It aims to improve statistical power and efficiency in RCTs by using covariate adjustments under Rosenbaum’s exact inference framework.\"},{\"question\":\"How does the method ensure valid statistical inference?\",\"answer\":\"It is built on Rosenbaum’s framework of exact tests, and simulations demonstrate robust control of the type I error rate.\"},{\"question\":\"Why can machine learning help when many covariates are available?\",\"answer\":\"Machine learning models like random forests can capture complex functional forms, including nonlinear associations and interactions that linear OLS-based adjustments may miss.\"}]","Machine Learning Assisted Adjustment Boosts Efficiency of Exact Inference in Randomized Controlled Trials - Abstract | PDF",1785936331,76,{"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},"machine-learning-assisted-adjustment-boosts-efficiency-of-exact-inference-in-randomized-controlled-trials-abstract","",{"@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/machine-learning-assisted-adjustment-boosts-efficiency-of-exact-inference-in-randomized-controlled-trials-abstract/127014/",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","2026-08-05",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 proposed method address in randomized controlled trials?","Question",{"text":75,"@type":76},"It aims to improve statistical power and efficiency in RCTs by using covariate adjustments under Rosenbaum’s exact inference framework.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method ensure valid statistical inference?",{"text":80,"@type":76},"It is built on Rosenbaum’s framework of exact tests, and simulations demonstrate robust control of the type I error rate.",{"name":82,"@type":73,"acceptedAnswer":83},"Why can machine learning help when many covariates are available?",{"text":84,"@type":76},"Machine learning models like random forests can capture complex functional forms, including nonlinear associations and interactions that linear OLS-based adjustments may miss.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":21,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]