{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T15:46:59Z","timestamp":1791906419632,"version":"4.3.4"},"reference-count":23,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CPT Pharmacom &amp;amp; Syst Pharma"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Although time\u2010varying Cox regression modeling approaches have been developed, exposure\u2013response analyses for time\u2010to\u2010event (TTE) endpoints often rely on static exposure covariates and may overlook the real\u2010world dosing variability and drug concentration fluctuations over time. To better characterize pharmacokinetic (PK) or pharmacodynamic (PD) effects on TTE endpoints, a methodology was proposed to integrate time\u2010varying pharmacometric models with Cox regression in the non\u2010linear mixed effects modeling software, NONMEM. Clinical trial simulations were conducted with different sample sizes and dose levels, employing a one\u2010compartment PK model, a bathtub\u2010shaped function for event hazard, and an\n                    <jats:italic>I<\/jats:italic>\n                    <jats:sub>max<\/jats:sub>\n                    model for concentration\u2010hazard relationships. Model parameters were estimated based on partial likelihood using the first order approximation method in NONMEM, and results were compared to those obtained from parametric methods under different baseline hazard assumptions. The performance of the models using static and time\u2010varying exposure metrics was also assessed. In the absence of a pre\u2010specified baseline hazard, the proposed semi\u2010parametric approach delivered robust parameter estimates and aligned with the parametric method with the correct baseline hazard assumption. The semi\u2010parametric method outperforms other parametric approaches with incorrect baseline hazard assumptions. Furthermore, the semi\u2010parametric method using time\u2010varying exposure metrics outperforms those using static exposure metrics. The proposed methodology successfully integrates time\u2010varying PK effects on TTE endpoints in the simulation study. It offers a flexible framework in NONMEM and can be extended to include other pharmacometric models with ordinary differential equations, thus enhancing model\u2010informed decision\u2010making for assessing TTE endpoints in drug development.\n                  <\/jats:p>","DOI":"10.1002\/psp4.70253","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T03:35:16Z","timestamp":1777001716000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Integration of Time\u2010Varying Pharmacometric Modeling With Cox Regression for Time\u2010to\u2010Event Analysis in\n                    <scp>NONMEM<\/scp>"],"prefix":"10.1002","volume":"15","author":[{"given":"Chih\u2010Wei","family":"Lin","sequence":"first","affiliation":[{"name":"Clinical Pharmacology, Modeling and Simulation Amgen Inc  Thousand Oaks California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Po\u2010Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Clinical Pharmacology, Modeling and Simulation Amgen Inc  Thousand Oaks California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sameer","family":"Doshi","sequence":"additional","affiliation":[{"name":"Clinical Pharmacology, Modeling and Simulation Amgen Inc  Thousand Oaks California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandeep","family":"Dutta","sequence":"additional","affiliation":[{"name":"Clinical Pharmacology, Modeling and Simulation Amgen Inc  Thousand Oaks California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"U.S. Food and Drug Administration \u201cGuidance for Industry: Exposure\u2013Response Relationships\u2014Study Design Data Analysis and Regulatory Applications 2003\u201d https:\/\/www.fda.gov\/media\/71277\/download."},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1972.tb00899.x"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.70534\/SRYF5789"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.13074"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.70119"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.70149"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365\u20102125.2012.04378.x"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3294-8"},{"key":"e_1_2_10_10_1","volume-title":"Survival Analysis Using SAS: A Practical Guide","author":"Allison P. D.","year":"2010"},{"key":"e_1_2_10_11_1","unstructured":"C.Davidson\u2010Pilon \u201cLifelines: Survival Analysis in Python 2023\u201d https:\/\/lifelines.readthedocs.io\/."},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/78.3.605"},{"issue":"1","key":"e_1_2_10_13_1","first-page":"16","article-title":"A Time to Event Tutorial for Pharmacometricians","volume":"94","author":"Holford D. R. M.","year":"2013","journal-title":"Clinical Pharmacology and Therapeutics"},{"key":"e_1_2_10_14_1","volume-title":"The Kaplan\u2013Meier Mean Covariate Plot (KMMC): A New Diagnostic for Covariates in Time\u2010To\u2010Event Models","author":"Hooker A. C.","year":"2012"},{"key":"e_1_2_10_15_1","volume-title":"NONMEM User's Guides (1989\u20132018)","author":"Beal S. L.","year":"2018"},{"key":"e_1_2_10_16_1","first-page":"5","volume-title":"A Package for Survival Analysis in R","author":"Therneau T. 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