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Considerations for master protocols using external controls.
Chen, Jie; Li, Xiaoyun Nicole; Lu, Chengxing Cindy; Yuan, Sammy; Yung, Godwin; Ye, Jingjing; Tian, Hong; Lin, Jianchang.
Afiliación
  • Chen J; Data Sciences, ECR Global, Shanghai, China.
  • Li XN; Global Statistics, BeiGene, San Mateo, California, USA.
  • Lu CC; Oncology Biometrics, AstraZeneca, Boston, Massachusetts, USA.
  • Yuan S; Oncology Statistics, GlaxoSmithKline, Collegeville, Pennsylvania, USA.
  • Yung G; Product Development Data and Statistical Sciences, Genentech/Roche, South San Francisco, Cambridge, USA.
  • Ye J; Global Statistics and Data Sciences, BeiGene, Fulton, Maryland, USA.
  • Tian H; Global Statistics, BeiGene, Ridgefield Park, New Jersy, USA.
  • Lin J; Statistical & Quantitative Sciences, Takeda, Cambridge, Massachusetts, USA.
J Biopharm Stat ; : 1-23, 2024 Feb 16.
Article en En | MEDLINE | ID: mdl-38363805
ABSTRACT
There has been an increasing use of master protocols in oncology clinical trials because of its efficiency to accelerate cancer drug development and flexibility to accommodate multiple substudies. Depending on the study objective and design, a master protocol trial can be a basket trial, an umbrella trial, a platform trial, or any other form of trials in which multiple investigational products and/or subpopulations are studied under a single protocol. Master protocols can use external data and evidence (e.g. external controls) for treatment effect estimation, which can further improve efficiency of master protocol trials. This paper provides an overview of different types of external controls and their unique features when used in master protocols. Some key considerations in master protocols with external controls are discussed including construction of estimands, assessment of fit-for-use real-world data, and considerations for different types of master protocols. Similarities and differences between regular randomized controlled trials and master protocols when using external controls are discussed. A targeted learning-based causal roadmap is presented which constitutes three key

steps:

(1) define a target statistical estimand that aligns with the causal estimand for the study objective, (2) use an efficient estimator to estimate the target statistical estimand and its uncertainty, and (3) evaluate the impact of causal assumptions on the study conclusion by performing sensitivity analyses. Two illustrative examples for master protocols using external controls are discussed for their merits and possible improvement in causal effect estimation.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Biopharm Stat / J. biopharm. stat / Journal of biopharmaceutical statistics Asunto de la revista: FARMACOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Biopharm Stat / J. biopharm. stat / Journal of biopharmaceutical statistics Asunto de la revista: FARMACOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido