DoseSurv: Predicting Personalized Survival Outcomes under Continuous-Valued Treatments
Moritz Gögl, Yu Liu, Christopher Yau, Peter Watkinson, Tingting Zhu
Abstract
Estimating heterogeneous treatment effects (HTEs) of continuous-valued interventions on survival, that is, time-to-event (TTE) outcomes, is crucial in various fields, notably in clinical decision-making and in driving the advancement of next-generation clinical trials. However, while HTE estimation for continuous-valued (i.e., dosage-dependent) interventions and for TTE outcomes have been separately explored, their combined application remains largely overlooked in the machine learning literature. We propose DoseSurv, a varying-coefficient network designed to estimate HTEs for different dosage-dependent and non-dosage treatment options from TTE data. DoseSurv uses radial basis functions to model continuity in dose-response relationships and learns balanced representations to address covariate shifts arising in HTE estimation from observational TTE data. We present experiments across various treatment scenarios on both simulated and real-world data, demonstrating DoseSurv's superior performance over existing baseline models.
BibTeX
@inproceedings{
gogl2025dosesurv,
title={DoseSurv: Predicting Personalized Survival Outcomes under Continuous-Valued Treatments},
author={Moritz G{\"o}gl and Yu Liu and Christopher Yau and Peter Watkinson and Tingting Zhu},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=5wdssRcI2Z}
}