OncoSynth: Synthetic Data Generation for Treatment Effect Estimation in Oncology

MCML Authors

Octavia-Andreea Ciora

→ Group Stefan Feuerriegel
 Artificial Intelligence in Management

Dennis Frauen

Dr.

* Former Member

→ Group Stefan Feuerriegel
 Artificial Intelligence in Management

Maresa Schröder

→ Group Stefan Feuerriegel
 Artificial Intelligence in Management

Marie Brockschmidt

→ Group Stefan Feuerriegel
 Artificial Intelligence in Management

Stefan Feuerriegel

Prof. Dr.

Core PI

Artificial Intelligence in Management

Abstract

In oncology, access to patient-level data is often restricted. Synthetic data provides an alternative for analyzing treatment effectiveness, but existing methods for synthetic data generation fail to preserve the causal relationships between covariates, treatments, and outcomes, thereby leading to biased estimates of treatment effects. Here, we introduce OncoSynth, a generative, causally-aware machine learning framework designed to produce synthetic cohorts that enable accurate estimation of population- and patient-level treatment effects. OncoSynth uses a diffusion-based sequential approach to model how covariates influence treatment assignment and how treatment affects survival. We evaluate OncoSynth using large lung (N = 37,128) and breast cancer (N = 17,046) cohorts. Our results show that OncoSynth generates high-fidelity synthetic patient cohorts that preserve real-world patient, treatment, and outcome distributions. Notably, OncoSynth improves treatment effect estimation over existing approaches, by reducing population-level treatment effect error by up to 66%, and patient-level treatment effect error by up to 58%. Thereby, OncoSynth supports reliable evidence generation for precision oncology in settings where data sharing is restricted.

misc CWD+26

Preprint

Authors

Links

Research Area

 A1 | Statistical Foundations & Explainability