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Open Access
External comparators and estimands
Frontiers in Drug Safety and Regulation, 2024-02, Vol.3
2024
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Autor(en) / Beteiligte
Titel
External comparators and estimands
Ist Teil von
  • Frontiers in Drug Safety and Regulation, 2024-02, Vol.3
Ort / Verlag
Frontiers Media S.A
Erscheinungsjahr
2024
Quelle
EZB Electronic Journals Library
Beschreibungen/Notizen
  • The estimand framework as defined by the ICH E9(R1) addendum aims to clearly define “the treatment effect reflecting the clinical question posed by the trial objective”. It intends to achieve this goal of a clear definition by specifying the 5 estimand attributes: treatment conditions, population, endpoints, handling of intercurrent events (IEs), and population-level summary. However, hybrid clinical/observational research like External Comparators (ECs) leads to new reflections on existing attributes and to considerations for additional ones. Specifically, treatment conditions and exposure may be more difficult to handle in the EC, and especially Standard of Care (SoC) treatment needs detailed attention. The external population typically cannot be based on the classical Intention-to-treat population and constitutes also an approximation only. Endpoints may not be comparable across cohorts, and IEs may be more different than in an RCT setting, such that the hypothetical treatment policy according to the ICH E9(R1) addendum may become of greater interest especially for long-term endpoints. Finally, the necessary assumptions for some population-level summaries (e.g., the proportional hazards assumption) can become more fragile when joining data from different sources due to induced heterogeneity. Finally, it is shown that the baseline definition and the marginal estimator are candidates for additional estimand attributes in case the estimand framework is revised to account for observational study needs.
Sprache
Englisch
Identifikatoren
ISSN: 2674-0869
eISSN: 2674-0869
DOI: 10.3389/fdsfr.2023.1332040
Titel-ID: cdi_doaj_primary_oai_doaj_org_article_4c9fce2b7112479f8db608fcbb1be7c7

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