{"id":5136,"date":"2025-07-08T07:02:36","date_gmt":"2025-07-08T07:02:36","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/07\/08\/2507-03681\/"},"modified":"2025-07-08T07:02:36","modified_gmt":"2025-07-08T07:02:36","slug":"2507-03681","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/07\/08\/2507-03681\/","title":{"rendered":"Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data"},"content":{"rendered":"

Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
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arXiv:2507.03681v1 Announce Type: new
\nAbstract: Randomized trials are typically designed to detect average treatment effects but often lack the statistical power to uncover effect heterogeneity over patient characteristics, limiting their value for personalized decision-making. To address this, we propose the QR-learner, a model-agnostic learner that estimates conditional average treatment effects (CATE) within the trial population by leveraging external data from other trials or observational studies. The proposed method is robust: it has the potential to reduce the CATE prediction mean squared error while maintaining consistency, even when the external data is not aligned with the trial. Moreover, we introduce a procedure that combines the QR-learner with a trial-only CATE learner and show that it asymptotically matches or exceeds the trial-only learner in terms of mean squared error. We examine the performance of our approach in simulation studies and apply the methods to a real-world dataset, demonstrating improvements in both CATE estimation and statistical power for detecting heterogeneous effects.<\/div>\n

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\n Rickard Karlsson, Piersilvio De Bartolomeis, Issa J. Dahabreh, Jesse H. Krijthe
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Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data arXiv:2507.03681v1 Announce Type: new Abstract: Randomized trials are typically designed to detect average treatment effects but often lack the statistical power to uncover effect heterogeneity over patient characteristics, limiting their value for personalized decision-making. To address this, we propose the QR-learner, a model-agnostic […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,183,112],"tags":[2068,1091,186],"class_list":["post-5136","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-me","category-stat-ml","tag-effects","tag-learner","tag-treatment"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5136"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=5136"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5136\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=5136"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=5136"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=5136"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}