Searcharxiv⌕ Search

arXiv · 2609.34874

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

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Renato Valladares Panaro, Vinícius Mayrink, Fábio Demarqui. 2026-09-28. Using the spsurv R package for semi-parametric time-to-event analysis. https://arxiv.org/abs/2609.34874

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Univariate-Guided Interaction Modeling

We propose a procedure for sparse regression with pairwise interactions, by generalizing the Univariate Guided Sparse Regression (UniLasso) methodology. A central contribution is our introduction of TripletScan, which screens a pair $(j,k)$ using the coefficient of $X_jX_k$ in the local regression of the response on $1$, $X_j$, $X_k$, and $X_jX_k$. The retained products are incorporated either jointly with the main effects through UniLasso, yielding uniPairs, or after a first-stage main-effects fit, yielding uniPairs-2stage. For the UniLasso components of the procedures, we prove false-positive exclusion and uniform coefficient-error bounds. In simulations and an HIV drug-resistance application, the proposed procedures produce substantially smaller fitted models than competing interaction methods while retaining competitive predictive performance.

stat.ME↗

Factoring A-Optimality into D-Optimality and Sphericity

The D criterion measures the volume of the joint confidence ellipsoid for the linear model coefficients and ignores its shape, so designs with the same D value can estimate individual effects with different variances. Meanwhile, A-optimality minimizes average coefficient variance. With both criteria expressed as information values, A equals D multiplied by a sphericity index for the same ellipsoid. In a fixed coefficient basis, sphericity further factors into coefficient-variance balance and a determinant-based correlation component. In five published screening comparisons, the A-optimal design has a larger correlation component despite slightly poorer variance balance; in three it also has a smaller D value. In a seven-run family of designs that all tie under D, the two with equal coefficient variances have the lowest A value. Both sphericity components can be calculated directly from standard errors and estimate correlations available in statistical software. After whitening by a prediction moment matrix, the same determinant-sphericity factorization applies to the I-criterion.

stat.ME↗

Testing the equality of parameters in fixed and increasing dimension

This paper proposes a general and unified framework for testing the equality of a broad class of parameters, defined as a smooth function of expectations of symmetric kernels, across multiple independent populations. We consider two test statistics, a Wald-type statistic and an ANOVA-type statistic. The asymptotic distribution of the first one is derived under a fixed-dimension regime, whereas the second one is studied under both fixed and increasing-dimension regimes, where the parameter dimension diverges with the sample size. Based on these limiting distributions, we construct test procedures enabling asymptotically exact inference without parametric assumptions. Additionally, an alternative null distribution estimator based on a weighted bootstrap approximation is studied, which is applicable to the ANOVA-type statistic under a fixed-dimension regime. The finite-sample performance and computational efficiency of the proposed procedures are evaluated through an extensive simulation study and a real dataset application.

stat.ME↗