{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T01:48:32Z","timestamp":1775872112468,"version":"3.50.1"},"reference-count":39,"publisher":"Wiley","issue":"28-30","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AI074345"],"award-info":[{"award-number":["R01AI074345"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R00MH133985"],"award-info":[{"award-number":["R00MH133985"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>We propose a novel causal estimand that elucidates how response to an earlier treatment (e.g., treatment initiation) modifies the effect of a later treatment (e.g., treatment discontinuation), thus learning if there are effects among the (un)affected. Specifically, we consider a working marginal structural model summarizing how the average effect of a later treatment varies as a function of the (estimated) conditional average effect of an earlier treatment. We define the estimand to be a data\u2010adaptive causal parameter, allowing for estimation of the conditional average treatment effect using machine learning without making strong smoothness assumptions. We show how a sequentially randomized design can be used to identify this causal estimand, and we describe a targeted maximum likelihood estimator for the resulting statistical estimand, with influence curve\u2010based inference. We present simulation studies that evaluate the performance of this estimator under various finite\u2010sample scenarios. Throughout, we use the \u201cAdaptive Strategies for Preventing and Treating Lapses of Retention in HIV Care\u201d trial (NCT02338739) as an illustrative example, showing that discontinuation of conditional cash transfers for HIV care adherence was most harmful among those who had an increase in benefit from them initially.<\/jats:p>","DOI":"10.1002\/sim.70353","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T12:15:58Z","timestamp":1765455358000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Effects Among the Affected"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0975-4306","authenticated-orcid":false,"given":"Lina M.","family":"Montoya","sequence":"first","affiliation":[{"name":"School of Data Science and Society University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"},{"name":"Department of Biostatistics University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elvin H.","family":"Geng","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases Washington University in St. Louis  Saint Louis Missouri USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Valancius","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael R.","family":"Kosorok","sequence":"additional","affiliation":[{"name":"Department of Biostatistics University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"},{"name":"Department of Statistics and Operations Research University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maya L.","family":"Petersen","sequence":"additional","affiliation":[{"name":"Division of Biostatistics University of California, Berkeley  Berkeley California USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"e_1_2_15_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.japh.2020.12.019"},{"key":"e_1_2_15_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2458-11-395"},{"key":"e_1_2_15_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneb.2021.03.006"},{"key":"e_1_2_15_5_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-030718-105251"},{"key":"e_1_2_15_6_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2022.24324"},{"key":"e_1_2_15_7_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974188"},{"key":"e_1_2_15_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2022"},{"key":"e_1_2_15_9_1","doi-asserted-by":"publisher","DOI":"10.1056\/EVIDoa2200076"},{"key":"e_1_2_15_10_1","doi-asserted-by":"publisher","DOI":"10.1214\/23-EJS2157"},{"key":"e_1_2_15_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/ectj\/utaa027"},{"key":"e_1_2_15_12_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"e_1_2_15_13_1","unstructured":"J. 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