Session

Overview: This symposium presents diverse contributions related to understanding the dose-effect relationship and challenges conventional dose-effect paradigms by exploring pathways from mere associations to a causal perspective. The first talk introduces the language of causal inference and its potential application in oncology, exploring the complex dynamics of treatment-effect modeling and addressing challenges like unmeasured confounders, while emphasizing risk-based treatment decisions. The second talk scrutinizes different dose-effect modeling approaches within routinely collected data, presenting their clinical translation. The third speaker will illustrate the integration of genetic information into dose-response relationships, pondering causality and the impact of microenvironment factors. Finally, the last speaker will question the transition from mere associations to robust causal inferences, provoking reflection on the frontier between hope and hype in dose-effect modeling.