Estimand in Real-World Evidence Study: From Frameworks to Application

Ying Wu, Hongwei Wang, Jie Chen and Hana Lee ()
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Ying Wu: Southern Medical University, Department of Biostatistics
Hongwei Wang: Medical Affairs & Health Technology Assessment Statistics, AbbVie
Jie Chen: Taimei Technology & Overland Pharma
Hana Lee: Food and Drug Administration

A chapter in Real-World Evidence in Medical Product Development, 2023, pp 145-165 from Springer

Abstract: Abstract The ICH E9(R1) addendum on estimands and sensitivity analysis describes statistical principles for defining estimands in clinical trials with a focus on five attributes—target population, treatment, endpoint, intercurrent events, and population-level summary. Although the addendum states that the principles are also applicable to single-arm trials and observational studies, constructing estimands for real-world evidence (RWE) studies might require additional considerations. This chapter reviews and discusses existing estimand-related frameworks, including the ICH E9(R1), that could potentially serve as guiding principles for defining real-world estimands. This chapter also provides examples of constructing an appropriate estimand in RWE studies, with detailed description on each of the five estimand attributes outlined in ICH E9(R1).

Keywords: Estimand; Real-world evidence; Intercurrent events; Causal inference framework; Target trial emulation; Targeted learning; Observational studies; Randomized controlled trials (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-26328-6_9

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DOI: 10.1007/978-3-031-26328-6_9

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