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INSP-04. Confirmatory adaptive designs for survival trials with several time-to-event endpoints

Neuro-oncology (Charlottesville, Va.), 2022-06, Vol.24 (Supplement_1), p.i187-i187 [Peer Reviewed Journal]

The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. 2022 ;ISSN: 1522-8517 ;EISSN: 1523-5866 ;DOI: 10.1093/neuonc/noac079.700

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  • Title:
    INSP-04. Confirmatory adaptive designs for survival trials with several time-to-event endpoints
  • Author: Schmidt, Rene
  • Subjects: Invited Speakers
  • Is Part Of: Neuro-oncology (Charlottesville, Va.), 2022-06, Vol.24 (Supplement_1), p.i187-i187
  • Description: Abstract Confirmatory adaptive designs comprise a range of statistical methods that allow to modify the sample size of an ongoing trial in a data-dependent way without compromising control of the type I error rate. For short-term endpoints (e.g., 3-month response rate), comprehensive methodology of adaptive designs exists. However, clinical trials in oncology often have a special focus on long-term outcome and therefore often choose a time-to-event endpoint as the primary endpoint. Typical examples are progression-free survival (PFS) or overall survival (OS). But subtle statistical problems arise when adaptively analysing survival trials. Classical designs for survival trials are therefore commonly limited to a single primary endpoint, which combines the occurrence of progression, toxicities, deaths, and other events of potential interest into a single statistical measure (composite endpoint). However, the complexity of oncological diseases can be mapped more accurately using multi-stage models, where the occurrence of progressions, toxicities and deaths is modelled jointly instead of combining them into a single composite endpoint. We present and discuss adaptive design methodology for single-arm phase II survival trials for testing hypotheses on the joint distribution of several time-to-event endpoints in the context of multi-state models. We illustrate the methodology using the example of adaptive hypothesis tests for the joint distribution of progression-free survival (PFS) and overall survival (OS) in the context of an illness-death model. The methodology is motivated from application in pediatric oncology.
  • Publisher: US: Oxford University Press
  • Language: English
  • Identifier: ISSN: 1522-8517
    EISSN: 1523-5866
    DOI: 10.1093/neuonc/noac079.700
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
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