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Overview

spsurv: An R package for semi-parametric survival analysis.

The spsurv package was designed to contribute with a flexible set of semi-parametric survival regression options, including proportional hazards (PH), proportional odds (PO), and accelerated failure time (AFT) models for right-censored data.

The package provides:

  • Survival classes (PH, PO, AFT) extensions based on a fully likelihood-based approach for either Bayesian or maximum likelihood (ML) estimation procedures
  • Smooth estimates for the unknown baseline functions based on the Bernstein polynomial (BP)
  • Integration with Stan for user-defined modeling
  • Six distinct prior specification options in a Bayesian analysis

Stan is an open-source platform with its own language and log-probability functions for custom likelihoods and priors. Access to Stan in R is provided via rstan; the package uses NUTS (No-U-Turn) sampling by default for Bayesian fits.

Installation

From CRAN

Development version 

install.packages("devtools")
devtools::install_github("rvpanaro/spsurv")

Usage

Fit a BP-based survival regression PH model. If degree is omitted, the default is ceiling(sqrt(n)) where n is the number of rows in data; the resolved value is stored on the fit as fit$degree.

library("KMsurv")
data("larynx")
larynx$stage <- factor(larynx$stage)

library(spsurv)

fit <- bpph(Surv(time, delta) ~ age + stage, data = larynx, approach = "mle")
summary(fit)
fit$degree   # Bernstein polynomial degree used

Alternatively, use the spbp function or set the degree with bernstein(m):

fit <- spbp(Surv(time, delta) ~ age + stage, model = "ph", data = larynx,
            dist = bernstein(5), approach = "mle")
summary(fit)

Bayesian analysis with the approach argument:

fit2 <- spbp(Surv(time, delta) ~ age + stage,
             approach = "bayes", data = larynx,
             iter = 2000, chains = 1, warmup = 1000)
summary(fit2)

Nested model comparison (MLE) follows the same anova() patterns as survival::survreg:

fit0 <- bpph(Surv(time, delta) ~ 1, data = larynx, approach = "mle", degree = 5)
fit1 <- bpph(Surv(time, delta) ~ age, data = larynx, approach = "mle", degree = 5)
fit2 <- bpph(Surv(time, delta) ~ age + stage, data = larynx, approach = "mle", degree = 5)

anova(fit0, fit1)
anova(fit1, fit2)
anova(fit2)   # sequential term-wise table

See the reference manual and vignette vignette("getting-started", package = "spsurv") for more examples.

Tidy model summaries

Coefficient and model-level summaries follow the generics / broom convention:

library(generics)
library(ggplot2)

fit <- bpph(Surv(time, delta) ~ age + stage, data = larynx, approach = "mle")

# One row per term (hazard ratios with 95% CI)
td <- tidy(fit, conf.int = TRUE, exponentiate = TRUE)
print(td)

# One-row model summary (n, log-likelihood, global LR test, AIC, ...)
glance(fit)

# Forest plot
ggplot(td, aes(x = estimate, y = term)) +
  geom_point() +
  geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0.2) +
  geom_vline(xintercept = 1, linetype = "dashed") +
  labs(x = "Hazard ratio", y = NULL)

For publication-ready tables, pipe tidy() output into gt or similar packages.

Baseline inference, diagnostics, and model ranking 

# Baseline gamma weights with Wald table (survstan-style summary)
summary(fit, show_baseline = TRUE)
print(fit, show_baseline = TRUE)   # same via print()
print(fit, bp.param = TRUE)        # baseline-only table

# Tidy baseline weights
tidy(fit, component = "baseline", conf.int = TRUE)
confint(fit, parm = names(fit$bp.param))

# Rank nested models by AIC
f0 <- bpph(Surv(time, delta) ~ 1, data = larynx, degree = 5)
f1 <- bpph(Surv(time, delta) ~ age, data = larynx, degree = 5)
rank_models(f0, f1)                # or AIC(f0, f1)

# Baseline survival S0(t) at event times
survfit(fit, baseline = TRUE, tidy = TRUE)

# ggplot2 residual plots (requires ggplot2)
ggresiduals(fit, type = "martingale")

If you also use the survstan package, qualify spsurv helpers after library(survstan) — e.g. spsurv::estimates(fit) and spsurv::se(fit).

tidymodels and tidybayes

Load parsnip (or tidymodels) before spsurv so censored-regression engines register. Use scale = FALSE when preprocessing with recipes.

library(parsnip)
library(censored)
library(workflows)
library(spsurv)

wf <- workflow() |>
  add_formula(Surv(time, status) ~ karno) |>
  add_model(
    proportional_hazards() |>
      set_engine("spsurv", degree = 5, scale = FALSE, init = 0)
  )

fit_wf <- fit(wf, data = veteran)
predict(fit_wf, veteran[1:2, ], type = "survival", eval_time = 100)

Bayesian fits support tidybayes via as_draws_df.spbp(), spread_draws(), and spread_surv_draws.spbp(). See vignette("tidymodels", package = "spsurv").

Troubleshooting

Bayesian convergence checks

For Bayesian fits, always check:

  • divergent transitions (target: 0)
  • split-(R) (target: close to 1, typically < 1.01)
  • effective sample sizes (avoid very low bulk/tail ESS)

If warnings appear, use this escalation order:

  1. increase adapt_delta (for example, from 0.8 to 0.9 or 0.95)
  2. increase iter and warmup
  3. re-check divergences, Rhat, and ESS before interpretation

Higher adapt_delta usually reduces divergences but increases runtime, so tuning should balance stability and compute cost.

Please report issues at https://github.com/rvpanaro/spsurv/issues or contact the maintainer (see DESCRIPTION).

License

GPL-3