Academic Journal
Optimizing Treatment Decision Estimation for Right-Censored Survival Data Through Parameter Transfer Learning.
| Τίτλος: | Optimizing Treatment Decision Estimation for Right-Censored Survival Data Through Parameter Transfer Learning. |
|---|---|
| Συγγραφείς: | Pan Y; Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan, China., Guo Y; Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan, China., Yang C; Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan, China., Shao Y; School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei, China., Yang Q; School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei, China. |
| Πηγή: | Statistics in medicine [Stat Med] 2026 Jul; Vol. 45 (15-17), pp. e70668. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Chichester ; New York : Wiley, c1982- |
| Ιατρικοί όροι (MeSH): | Transfer Machine Learning*, Precision Medicine/methods ; Humans ; Survival Analysis ; Computer Simulation ; Models, Statistical ; Bias ; Prediction Algorithms |
| Περίληψη: | Accurately estimating treatment effects is crucial for designing optimal treatment plans in personalized medicine, especially in the presence of right-censored survival data. We propose a parameter transfer learning method for estimating treatment effects on right-censored survival data, which leverages multi-source 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-censored survival data to estimate candidate model parameters. To enhance performance, a leave-one-out cross-validation 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-censored 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-making. (© 2026 John Wiley & Sons Ltd.) |
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| Grant Information: | 24CTJ004 National Social Science Fund of China |
| Contributed Indexing: | Keywords: greedy algorithm; individualized treatment rule; right‐censored survival data; transfer learning |
| Entry Date(s): | Date Created: 20260707 Date Completed: 20260707 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13349455 |
| DOI: | 10.1002/sim.70668 |
| PMID: | 42411252 |
| Βάση Δεδομένων: | MEDLINE |
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