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When the distribution of effect modifiers differs between trial participants and the target population, treatment effects estimated within the trial may not be directly applicable to all trial\u2010eligible individuals. Although substantial progress has been made in generalizing average treatment effects from randomized trials to target populations, clinical and policy decisions are often informed by conditional average treatment effects (CATEs) that characterize heterogeneity across selected effect modifiers in the target population. In this work, we provide a unified approach for estimating CATEs in a target population of trial\u2010eligible individuals under a nested trial design. Our approach first estimates nuisance functions using data\u2010adaptive methods and constructs pseudo\u2010outcomes from conditional influence functions. These pseudo\u2010outcomes are subsequently regressed on pre\u2010specified effect modifiers using local linear (kernel) regression to obtain a smoothed estimate of the CATE function. We establish asymptotic properties under sample splitting and cross\u2010fitting, and provide conditions under which first\u2010stage nuisance estimation has negligible impact on the final estimator. We assess the finite\u2010sample performance of the proposed estimator via simulation studies, and illustrate its practical use through an application to the Coronary Artery Surgery Study (CASS).<\/jats:p>","DOI":"10.1002\/sim.70767","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:26:18Z","timestamp":1791591978000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generalizing Conditional Average Treatment Effects From Nested Randomized Trials to Trial\u2010Eligible Individuals"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6120-8492","authenticated-orcid":false,"given":"Lan","family":"Wen","sequence":"first","affiliation":[{"name":"Department of Statistics and Actuarial Science University of Waterloo  Waterloo Ontario Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2215-9931","authenticated-orcid":false,"given":"Issa J.","family":"Dahabreh","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics Harvard T.H. Chan School of Public Health  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0904-7855","authenticated-orcid":false,"given":"Yu\u2010Han","family":"Chiu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology Brown University School of Public Health  Providence Rhode Island USA"},{"name":"Center for Gerontology and Healthcare Research Brown University School of Public Health  Providence Rhode Island USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"issue":"6","key":"e_1_2_13_2_1","first-page":"2184","article-title":"Using Group Data to Treat Individuals: Understanding Heterogeneous Treatment Effects in the Age of Precision Medicine and Patient\u2010Centred Evidence","volume":"45","author":"Dahabreh I. 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