{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T10:07:51Z","timestamp":1790244471432,"version":"4.1.0"},"reference-count":27,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"publisher","award":["NIHR301614"],"award-info":[{"award-number":["NIHR301614"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]}],"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>\n                    Sample size re\u2010estimation (SSR) can be a powerful tool to ensure that a clinical trial meets its pre\u2010specified power requirements when uncertainty regarding a design parameter exists at the planning stage. However, long\u2010term primary endpoints can be harmful to the efficiency of this trial design. If recruitment is continued while treatment outcomes are awaited, long delays can potentially lead to a large number of \u2018pipeline\u2019 participants being recruited in the trial that do not contribute to the interim analysis. This may lead to a larger number of recruited participants than are actually deemed required, resulting in an over\u2010powered trial with high cost. This paper studies the exact impact of such outcome delay on the efficiency of \u2018internal pilot\u2019 type SSR designs. The distribution of the final sample size post SSR is obtained under various delay lengths for both continuous and binary outcome data; how delay impacts the precision of the final sample size estimate is then discussed. Precisely, the impact of delay on this precision is assessed through RMSE, as well as two more novel metrics, termed the\n                    <jats:italic>delay impact<\/jats:italic>\n                    and\n                    <jats:italic>cost<\/jats:italic>\n                    . The results indicate that with an increase in delay length, the\n                    <jats:italic>delay impact<\/jats:italic>\n                    increases, inflating average sample size and power. However, the severity of the effect of delayed outcomes depends highly on the exact trial setting. Trials where the re\u2010estimated sample size is smaller than originally planned suffer the most from delayed outcomes, often leading to an over\u2010powered trial. However, the impact of delay is substantially less if the originally planned sample size remains smaller than the re\u2010estimated sample size.\n                  <\/jats:p>","DOI":"10.1002\/pst.70117","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:21:31Z","timestamp":1790241691000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating the Impact of Outcome Delay on the Efficiency of Sample Size Re\u2010Estimation"],"prefix":"10.1002","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4531-6047","authenticated-orcid":false,"given":"Aritra","family":"Mukherjee","sequence":"first","affiliation":[{"name":"Population Health Sciences Institute Newcastle University  Newcastle upon Tyne UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0680-6668","authenticated-orcid":false,"given":"Michael J.","family":"Grayling","sequence":"additional","affiliation":[{"name":"Janssen  High Wycombe UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4691-126X","authenticated-orcid":false,"given":"James M. S.","family":"Wason","sequence":"additional","affiliation":[{"name":"Population Health Sciences Institute Newcastle University  Newcastle upon Tyne UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"crossref","DOI":"10.1201\/b17761","volume-title":"Adaptive Design Theory and Implementation Using SAS and R","author":"Chang M.","year":"2014"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/pst.140"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.200510238"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/pst.1564"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/10543400802369053"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.1585"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.200800266"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cct.2015.12.007"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9175"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13063\u2010020\u201004931\u2010w"},{"issue":"2","key":"e_1_2_11_12_1","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1093\/biostatistics\/3.2.277","article-title":"A Sample Size Adjustment Procedure for Clinical Trials Based on Conditional Power","volume":"3","author":"Gang L. 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