Causal Directed Acyclic Graphs to Mitigate Confounding Bias in Exposure‐Response Analyses
Abstract
The use of exposure-response (E-R) analysis to support drug development and treatment individualization requires estimating the causal effect of drug exposure on response. This may be challenging when the E-R relationship is confounded. This perspective examines the problem of confounding in E-R analyses in oncology through the lens of causal inference, demonstrating how a causal directed acyclic graph (DAG) can be used as a powerful tool to understand these challenges and identify potential solutions.
Fields of Study & Subjects
Advanced Causal Inference Techniques
Statistical Methods in Clinical Trials
Bayesian Modeling and Causal Inference
Publication details
- Journal / source
- CPT Pharmacometrics & Systems Pharmacology
- Volume
- 15
- Issue
- 3
- Pages
- e70227–e70227
- Publication date
- 1 March 2026
- Language
- EN