Estimands in Real-World Evidence Studies

TL;DR

This paper reviews the concept of estimands in real-world evidence studies, emphasizing their importance in aligning research questions with study design amidst the complexities of real-world data.

Contribution

It provides a comprehensive overview of constructing estimands for RWE studies, highlighting differences from traditional clinical trials and offering a practical roadmap.

Findings

Highlights key components of estimands in RWE studies

Discusses challenges unique to real-world data

Provides guidance for selecting appropriate estimands

Abstract

A Real-World Evidence (RWE) Scientific Working Group (SWG) of the American Statistical Association Biopharmaceutical Section (ASA BIOP) has been reviewing statistical considerations for the generation of RWE to support regulatory decision-making. As part of the effort, the working group is addressing estimands in RWE studies. Constructing the right estimand -- the target of estimation -- which reflects the research question and the study objective, is one of the key components in formulating a clinical study. ICH E9(R1) describes statistical principles for constructing estimands in clinical trials with a focus on five attributes -- population, treatment, endpoints, intercurrent events, and population-level summary. However, defining estimands for clinical studies using real-world data (RWD), i.e., RWE studies, requires additional considerations due to, for example, heterogeneity of study population, complexity of treatment regimes, different types and patterns of intercurrent events, and complexities in choosing study endpoints. This paper reviews the essential components of estimands and causal inference framework, discusses considerations in constructing estimands for RWE studies, highlights similarities and differences in traditional clinical trial and RWE study estimands, and provides a roadmap for choosing appropriate estimands for RWE studies.