Each week, we will be publishing lay abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.

The article featured today is a Tutorial in Biostatistics paper from Statistics in Medicine, with the full article now available to read on Early View here.

Ocampo, A, Bather, JR. Single-world intervention graphs for defining, identifying, and communicating estimands in clinical trials. Statistics in Medicine. 2023; 1– 11. doi: 10.1002/sim.9833

Deciding how to handle intercurrent events in a clinical trial protocol can be tricky. Single-world intervention graphs (SWIGs) are a causal graph that can aid in determining intercurrent event strategies and the resulting desired treatment effect(s) of interest for a particular randomized clinical trial. These graphs are intuitive tools that clearly display the interplay between treatments, intercurrent events, and outcomes. They move beyond Directed Acyclic Graphs (DAGs) in that they allow you to depict counterfactual (or hypothetical) scenarios that may not have occurred throughout the course of the trial for a given patient. To demonstrate their utility in clinical trial design, we apply a SWIG to different intercurrent event strategies outlined in the ICH E9(R1) addendum as well as a clinical trial for chronic pain with multiple types of intercurrent events. By clearly defining the targets of estimation, SWIGs make it easier for multiple stakeholders to align on the appropriate statistical methodology for a given trial. We recommend statisticians use SWIGs to communicate and align on the targeted treatment effects of their clinical trials and make explicit the assumptions needed to estimate estimands.