Clarifying Causal Effects of Interest and Underlying Assumptions in Randomized and Nonrandomized Clinical Trials in Oncology Using Directed Acyclic Graphs and Single-World Intervention Graphs.

Abstract

Recent clinical trials in oncology have used increasingly complex methodologies, such as causal inference methods for intercurrent events, external control, and covariate adjustment, posing challenges in clarifying the estimand and underlying assumptions. This article proposes expressing causal structures using graphical tools-directed acyclic graphs (DAGs) and single-world intervention graphs (SWIGs)-in the planning phase of a clinical trial. It presents five rules for selecting a sufficient set of adjustment variables on the basis of a diagram representing the clinical trial, along with three case studies of randomized and single-arm trials and a brief tutorial on DAG and SWIG. Through the case studies, DAGs appear effective in clarifying assumptions for identifying causal effects, although SWIGs should complement DAGs due to their limitations in the presence of intercurrent events in oncology research.