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PreprintarXiv licencearXiv

Using the spsurv R package for semi-parametric time-to-event analysis

  • Renato Valladares Panaro
  • Vinícius Mayrink
  • Fábio Demarqui
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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.

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Cite

BibTeXRIS
BibTeX
@misc{panaro2026using,
  title = {{Using the spsurv R package for semi-parametric time-to-event analysis}},
  author = {Renato Valladares Panaro and Vinícius Mayrink and Fábio Demarqui},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.34874},
  eprint = {2609.34874},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.34874},
}
RIS
TY  - UNPB
TI  - Using the spsurv R package for semi-parametric time-to-event analysis
AU  - Renato Valladares Panaro
AU  - Vinícius Mayrink
AU  - Fábio Demarqui
PY  - 2026
DA  - 2026/09/28
PB  - arXiv
DO  - 10.48550/arxiv.2609.34874
UR  - https://arxiv.org/abs/2609.34874
AB  - 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.
ER  -