View in EDS HTML Full Text PDF Full Text

A Bayesian Latent Subgroup Design for Basket Trials with Survival Endpoints.

Saved in:
Bibliographic Details
Title: A Bayesian Latent Subgroup Design for Basket Trials with Survival Endpoints.
Authors: Han, Yueyang1 (AUTHOR), Shi, Haolun1 (AUTHOR) haoluns@sfu.ca
Source: Statistics in Biopharmaceutical Research. Jul-Sep2026, Vol. 18 Issue 3, p436-447. 12p.
Subjects: Bayesian analysis, Survival rate, Treatment effectiveness, Biomarkers, Tumor classification, Clinical trials
Abstract: We propose a basket trial design that addresses patient heterogeneity in treatment effect across subgroups and evaluates the effectiveness of treatments with a survival endpoint via Bayesian latent subgroup analysis. In basket trials, various cancer types are enrolled, yet the response to treatment can vary across these types. To accommodate this variability, our design assumes cancer types may belong to either a sensitive subgroup, responsive to treatment, or an insensitive subgroup, unresponsive to treatment. During interim analyses, Bayesian subgroup analysis is conducted to classify the cancer types into different clusters according to both the survival time and the longitudinal biomarker measurements of the patient. Finally, we make Bayesian inferences to decide whether to stop recruiting patients for each cluster early and assess whether the treatment is effective for each cluster according to the estimated median survival time. The simulation study shows that our proposed method performs better than the independent approach and the Bayesian Hierarchical Modeling (BHM) method in most of the scenarios, with higher power to detect treatment effects in sensitive cancer types while maintaining the desired type I error rate for insensitive types. This integrated approach holds promise for optimizing treatment efficacy across diverse cancer populations with varying characteristics. [ABSTRACT FROM AUTHOR]
Copyright of Statistics in Biopharmaceutical Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 195621058
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Bayesian Latent Subgroup Design for Basket Trials with Survival Endpoints.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Han%2C+Yueyang%22">Han, Yueyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shi%2C+Haolun%22">Shi, Haolun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> haoluns@sfu.ca</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Statistics+in+Biopharmaceutical+Research%22">Statistics in Biopharmaceutical Research</searchLink>. Jul-Sep2026, Vol. 18 Issue 3, p436-447. 12p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Survival+rate%22">Survival rate</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Biomarkers%22">Biomarkers</searchLink><br /><searchLink fieldCode="DE" term="%22Tumor+classification%22">Tumor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+trials%22">Clinical trials</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: We propose a basket trial design that addresses patient heterogeneity in treatment effect across subgroups and evaluates the effectiveness of treatments with a survival endpoint via Bayesian latent subgroup analysis. In basket trials, various cancer types are enrolled, yet the response to treatment can vary across these types. To accommodate this variability, our design assumes cancer types may belong to either a sensitive subgroup, responsive to treatment, or an insensitive subgroup, unresponsive to treatment. During interim analyses, Bayesian subgroup analysis is conducted to classify the cancer types into different clusters according to both the survival time and the longitudinal biomarker measurements of the patient. Finally, we make Bayesian inferences to decide whether to stop recruiting patients for each cluster early and assess whether the treatment is effective for each cluster according to the estimated median survival time. The simulation study shows that our proposed method performs better than the independent approach and the Bayesian Hierarchical Modeling (BHM) method in most of the scenarios, with higher power to detect treatment effects in sensitive cancer types while maintaining the desired type I error rate for insensitive types. This integrated approach holds promise for optimizing treatment efficacy across diverse cancer populations with varying characteristics. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Statistics in Biopharmaceutical Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=195621058
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/19466315.2025.2611817
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 436
    Subjects:
      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Survival rate
        Type: general
      – SubjectFull: Treatment effectiveness
        Type: general
      – SubjectFull: Biomarkers
        Type: general
      – SubjectFull: Tumor classification
        Type: general
      – SubjectFull: Clinical trials
        Type: general
    Titles:
      – TitleFull: A Bayesian Latent Subgroup Design for Basket Trials with Survival Endpoints.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Han, Yueyang
      – PersonEntity:
          Name:
            NameFull: Shi, Haolun
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul-Sep2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19466315
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Statistics in Biopharmaceutical Research
              Type: main
ResultId 1