{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T20:36:29Z","timestamp":1780605389347,"version":"3.54.1"},"reference-count":50,"publisher":"Wiley","issue":"17","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MC_UU_00002\/5"],"award-info":[{"award-number":["MC_UU_00002\/5"]}],"id":[{"id":"10.13039\/501100000265","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":[[2024,7,30]]},"abstract":"<jats:p>Many clinical trials generate both longitudinal biomarker and time\u2010to\u2010event data. We might be interested in their relationship, as in the case of tumor size and overall survival in oncology drug development. Many well\u2010established methods exist for analyzing such data either sequentially (two\u2010stage models) or simultaneously (joint models). Two\u2010stage modeling (2stgM) has been challenged (i) for not acknowledging that biomarkers are endogenous covariable to the survival submodel and (ii) for not propagating the uncertainty of the longitudinal biomarker submodel to the survival submodel. On the other hand, joint modeling (JM), which properly circumvents both problems, has been criticized for being time\u2010consuming, and difficult to use in practice. In this paper, we explore a third approach, referred to as a novel two\u2010stage modeling (N2stgM). This strategy reduces the model complexity without compromising the parameter estimate accuracy. The three approaches (2stgM, JM, and N2stgM) are formulated, and a Bayesian framework is considered for their implementation. Both real and simulated data were used to analyze the performance of such approaches. In all scenarios, our proposal estimated the parameters approximately as JM but without being computationally expensive, while 2stgM produced biased results.<\/jats:p>","DOI":"10.1002\/sim.10128","type":"journal-article","created":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T04:38:44Z","timestamp":1717475924000},"page":"3280-3293","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Bridging the gap between two\u2010stage and joint models: The case of tumor growth inhibition and overall survival models"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3764-0397","authenticated-orcid":false,"given":"Danilo","family":"Alvares","sequence":"first","affiliation":[{"name":"MRC Biostatistics Unit University of Cambridge  Cambridge 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