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

Wu, Ying; Wang, Hongwei; Chen, Jie; Lee, Hana

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

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…The ICH E9(R1) focuses on clinical trials but states that its principles are relevant for observational studies as well (ICH E9(R1) Expert Working Group, 2021). However, setting up an EC estimand is less straightforward than in clinical trials, which is in line with Wu et al, 2023 stating that "constructing estimands for real-world evidence (RWE) studies might require additional considerations". These additional considerations have been presented for EC studies in this paper.…”

Section: Discussionmentioning

confidence: 73%

“…More general introductions to the estimand framework are available (e.g., Gogtay et al, 2021), and some specific considerations about observational studies have been derived as well (Li et al, 2022;Chen et al, 2023;Wu et al, 2023). Although they are relevant for EC studies, they do not discuss estimands in the EC setting specifically, such that a concrete discussion in the context of EC studies provides added value.…”

Section: Introductionmentioning

confidence: 99%

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External comparators and estimands

Rippin

2024

Front. Drug Saf. Regul.

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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.

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Section: Discussionmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

External comparators and estimands

Rippin

2024

Front. Drug Saf. Regul.

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Estimands in Real-World Evidence Studies

Chen,

Scharfstein,

Wang

et al. 2023

Statistics in Biopharmaceutical Research

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External comparator studies and the joint application of the estimand and target trial emulation frameworks

Rippin,

Sanz

2024

Front. Drug Saf. Regul.

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The estimand framework (EF) and the target trial emulation framework (TTEF) are two important frameworks that can inform the design and analysis of external comparator (EC) studies. The EF helps clarifying the quantity to be estimated (the “estimand”), especially regarding the handling of post-baseline intercurrent events which interfere with the existence or interpretation of endpoints. Similarly, the TTEF is instrumental for specifying key design components of a hypothetical randomized trial and identifying which of these an EC study can and cannot emulate. We offer considerations about the joint application of both frameworks by combining the five EF attributes— treatment, population, endpoint, intercurrent events, and population-level summary—with the seven TTEF components— eligibility criteria, treatment strategies, assignment procedures, follow-up period, outcomes, causal contrasts, and data analysis plan. Any overlap is identified, as well as omissions and unique contributions from both frameworks. Furthermore, we highlight specific considerations when applying these joint elements to EC studies.

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

Cited by 3 publications

References 44 publications

External comparators and estimands

External comparators and estimands

Estimands in Real-World Evidence Studies

External comparator studies and the joint application of the estimand and target trial emulation frameworks

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