Single-World Intervention Graphs for Defining, Identifying, and Communicating Estimands in Clinical Trials

TL;DR

This paper introduces Single-World Intervention Graphs (SWIGs) as a visual tool to clearly define, identify, and communicate estimands in clinical trials, enhancing understanding among interdisciplinary stakeholders.

Contribution

It presents SWIGs as a novel graphical method to represent and communicate causal estimands and assumptions in clinical trial design and analysis.

Findings

SWIGs effectively illustrate estimands and assumptions.

Application to various intercurrent event strategies.

Real-world clinical trial example demonstrates utility.

Abstract

Confusion often arises when attempting to articulate target estimand(s) of a clinical trial in plain language. We aim to rectify this confusion by using a type of causal graph called the Single-World Intervention Graph (SWIG) to provide a visual representation of the estimand that can be effectively communicated to interdisciplinary stakeholders. These graphs not only display estimands, but also illustrate the assumptions under which a causal estimand is identifiable by presenting the graphical relationships between the treatment, intercurrent events, and clinical outcomes. To demonstrate its usefulness in pharmaceutical research, we present examples of SWIGs for various intercurrent event strategies specified in the ICH E9(R1) addendum, as well as an example from a real-world clinical trial for chronic pain. Latex code to generate all the SWIGs shown is this paper is made available. We advocate clinical trialists adopt the use of SWIGs in their estimand discussions during the planning stages of their studies.