DoseSurv: Predicting Personalized Survival Outcomes under Continuous-valued Treatments
Moritz Gögl, Yu Liu, Christopher Yau, Peter Watkinson, Tingting Zhu
Predicting 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 (e.g, dosage-dependent) interventions and for TTE outcomes have been separately explored, their combined application remains largely overlooked in recent machine learning literature. We propose DoseSurv, a varying coefficient network designed for estimating the HTE of (multiple) continuous-valued as well as binary treatment options from TTE data. DoseSurv uses radial basis functions to model the continuity in dose-response relationships, and uses balancing representations to address covariate shifts arising in HTE estimation from observational TTE data. We present experiments in various treatment scenarios on both simulated and real-world data, demonstrating DoseSurv's superior performance over existing baseline models.