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65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS)

06.09. - 09.09.2020, Berlin (online conference)

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Estimands in Clinical Trials with Treatment Switching

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  • Juliane Manitz - EMD Serono, Billerica, United States
  • Natalia Kan-Dobrosky - PPD Inc., Morrisville, United States
  • Hannes Buchner - Staburo, München, Germany
  • Marie-Laure Casadebaig - Celgene, Boudry, Switzerland
  • Vincent Haddad - AstraZeneca, Cambridge, United Kingdom
  • Fei Jie - Astellas Pharma Global Development, Northbrook, United States
  • Rui Tang - Servier Pharmaceuticals, Boston, United States
  • Godwin Yung - Takeda Pharmaceuticals, Cambridge, United States
  • Jiangxiu Zhou - GSK, Collegeville, United States
  • Emily Martin - EMD Serono, Billerica, United States
  • Viktoriya Stalbovskaya - Merus, Utrecht, Netherlands
  • Yue Shentu - Merck Sharp & Dohme, Rahway, United States
  • Kaspar Rufibach - F. Hoffmann-La Roche Ltd, Basel, Switzerland
  • Mindy Mo - Amgen Inc, Thousand Oaks, United States
  • Jyotirmoy Dey - AbbVie Inc., North Chicago, United States

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), Meeting of the Central European Network (CEN: German Region, Austro-Swiss Region and Polish Region) of the International Biometric Society (IBS). Berlin, 06.-09.09.2020. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocAbstr. 20

doi: 10.3205/20gmds258, urn:nbn:de:0183-20gmds2586

Published: February 26, 2021

© 2021 Manitz et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

Text

An addendum of the ICH E9 guideline on Statistical Principles for Clinical Trials was released in November 2019 introducing the estimand framework. This new framework aims to align trial objectives and statistical analyses by requiring a precise definition of the population quantity of interest, i.e. the estimand. Estimands should explicitly account for intercurrent events, i.e. events, which occur after treatment initiation but before observing the study endpoint, such as the start of new therapy when the endpoint is overall survival (OS). A working group was initiated to foster understanding and consistent implementation of the estimand framework in oncology clinical trials. This work summarizes the group's recommendations for appropriate estimands in the presence of treatment switching, one of the key intercurrent events in oncology clinical trials.

Traditionally, analysis of OS in the confirmatory study is performed ignoring treatment switching (treatment-policy estimand). If patients from the control group switch more frequently to the treatment which prolongs OS, than patients in the investigational group, the true survival benefit of the investigational treatment itself is likely to be underestimated. Causal inference methodologies accounting for treatment switching (hypothetical estimand) such as rank-preserving structural failure time models and inverse probability weighting have been proposed and applied in oncology trials for the analysis of OS, to mitigate this bias, providing further perspectives on the added value of novel therapies, e.g. to payers and patients. We present different choices of estimands, illustrate those estimands using case studies, and discuss how those choices may impact study design, data collection, trial conduct, analysis, and interpretation.

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The authors declare that an ethics committee vote is not required.