🤖 AI Summary
This study addresses the limitations of overly restrictive parametric assumptions on baseline functions and the challenges of model selection in survival analysis. We propose a semi-parametric time-to-event regression method based on Bernstein polynomials, implemented in the R package spsurv. By avoiding prespecified baseline distributions, this approach enables smooth estimation of the baseline hazard and supports both Bayesian inference and maximum likelihood estimation via Stan. It unifies the interfaces for proportional hazards, proportional odds, and accelerated failure time models while preserving intuitive effect interpretations. Monte Carlo simulations demonstrate the method's robustness in finite samples, and its practical utility is illustrated through an application to oncology clinical trial data. Overall, this work provides a flexible and efficient tool for survival modeling.
📝 Abstract
We present spsurv, an R package for semi-parametric time-to-event regression based on Bernstein-polynomial estimation of unknown baseline functions. The package provides a unified modelling interface for proportional hazards (PH), proportional odds (PO), and accelerated failure time (AFT) models for right-censored data, with either maximum likelihood or Bayesian estimation via Stan. Smooth baseline hazard, odds-function, or log-time structures are estimated without assuming a parametric baseline family, while retaining familiar hazard-ratio, odds-ratio, and time-ratio interpretations. We describe methodology, implementation, and syntax; evaluate finite-sample behaviour in a Monte Carlo study; and illustrate usage with oncology trials.