{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T01:45:19Z","timestamp":1791769519830,"version":"4.3.4"},"reference-count":16,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T00:00:00Z","timestamp":1791676800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T00:00:00Z","timestamp":1791676800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmaceutical Statistics"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>When observation of recurrent events ends before the planned follow\u2010up time, conclusions may depend on untestable assumptions about outcomes during the unobserved period. This is especially challenging for recurrent outcomes such as hospitalizations or infections, for which each event creates a temporary period during which a new event cannot occur and at\u2010risk time stops accruing. In such settings, multiple imputation must jointly determine future event counts and future at\u2010risk exposure. This paper proposes an off\u2010risk\u2010aware extension of controlled multiple imputation that simulates post\u2010dropout trajectories in calendar time while allowing exposure to accrue only during on\u2010risk intervals. Standard assumptions, including missing at random and jump\u2010to\u2010reference, are implemented by modifying only the post\u2010dropout rate assignment while leaving the exposure\u2010generation mechanism unchanged. In simulation, missing\u2010at\u2010random imputation closely matched direct likelihood, whereas jump\u2010to\u2010reference progressively attenuated the treatment effect toward the null as missingness increased. In the rhDNase illustration, missing\u2010at\u2010random imputation again yielded results very close to direct likelihood, while jump\u2010to\u2010reference produced only modest attenuation. The method provides a practical sensitivity\u2010analysis framework for trials in which recurrent events temporarily remove participants from risk, helping align missing\u2010data assumptions, outcome generation, and analysis on the at\u2010risk time scale.<\/jats:p>","DOI":"10.1002\/pst.70134","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T01:21:15Z","timestamp":1791768075000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multiple Imputation for Recurrent Events With Off\u2010Risk Periods"],"prefix":"10.1002","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4145-1786","authenticated-orcid":false,"given":"Szil\u00e1rd","family":"Nemes","sequence":"first","affiliation":[{"name":"R&amp;I Biometrics and Statistical Innovation, Respiratory &amp; Immunology, BioPharmaceuticals R&amp;D, AstraZeneca  Gothenburg Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,11]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2023-076316","article-title":"The Estimands Framework: A Primer on the Ich e9 (r1) Addendum","volume":"384","author":"Kahan B. 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