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We propose a parameter transfer learning method for estimating treatment effects on right\u2010censored survival data, which leverages multi\u2010source auxiliary data to enhance the prediction accuracy and robustness of the target model. This method constructs multiple source models by extracting shared parameters from other datasets and uses a smoothed concordance index function specifically designed for right\u2010censored survival data to estimate candidate model parameters. To enhance performance, a leave\u2010one\u2010out cross\u2010validation criterion is applied to optimize model averaging weights. Theoretically, we have demonstrated that under mild conditions, the proposed method asymptotically achieves the highest smoothed concordance index when the target model is misspecified, and ensures model weight consistency when the target model is correctly specified. Simulation studies confirm the advantages of our proposed method in reducing bias and enhancing prediction accuracy, particularly with right\u2010censored and heterogeneous data. Its application to the SUPPORT (Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments) extension dataset, support2, further demonstrates its strong potential in personalized clinical decision\u2010making.<\/jats:p>","DOI":"10.1002\/sim.70668","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T08:10:31Z","timestamp":1783411831000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimizing Treatment Decision Estimation for Right\u2010Censored Survival Data Through Parameter Transfer Learning"],"prefix":"10.1002","volume":"45","author":[{"given":"Yingli","family":"Pan","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics Hubei University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinfei","family":"Guo","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics Hubei University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chentao","family":"Yang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics Hubei University  Wuhan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhong","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Statistics and Mathematics Zhongnan University of Economics and Law  Wuhan Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinglong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Statistics and Mathematics Zhongnan University of Economics and Law  Wuhan Hubei China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2020.107015"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1515\/jci-2022-0005"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2214889120"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9198"},{"key":"e_1_2_11_6_1","first-page":"381","article-title":"On Estimation of Optimal Treatment Regimes for Maximizing t\u2010Year Survival Probability","volume":"88","author":"Jiang R.","year":"2016","journal-title":"Journal of the Royal Statistical Society. 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