rpsurv

rpsurv fits the Royston and Parmar (2002) family of flexible parametric survival models: the baseline distribution is a restricted cubic spline in log time on a chosen transformed scale (proportional hazards, proportional odds, or probit), which gives a smooth, fully parametric, extrapolable fit without committing to a Weibull, log-normal, or other single distribution.

The log-likelihood and its analytic gradient are evaluated in C++ via RcppParallel, so fitting stays fast at large sample sizes (see the Benchmark section below). Output mirrors coxph(): print()/summary() separate interpretable covariate effects from the baseline spline nuisance parameters, and there are predict()/plot()/residual-diagnostic methods.

All results below are real output from rstpm2::brcancer (686 rows, 299 events).

Installation

# install.packages("rpsurv")  # once on CRAN
# development version:
# remotes::install_github("ielbadisy/rpsurv")

Fitting a model

library(rpsurv)
library(survival)

data(brcancer)
brcancer$hormon <- as.numeric(brcancer$hormon)

fit <- rpsurv(Surv(rectime, censrec) ~ hormon, data = brcancer, df = 4, scale = "hazard")
fit
#> Royston-Parmar flexible parametric survival model
#> Call:
#> rpsurv(formula = Surv(rectime, censrec) ~ hormon, data = brcancer,
#>     df = 4, scale = "hazard")
#>
#> Scale: hazard   Baseline df: 4
#> n = 686 , number of events = 299
#> Log-likelihood = -2606   AIC = 5225

summary(fit)
#> Royston-Parmar flexible parametric survival model
#> Call:
#> rpsurv(formula = Surv(rectime, censrec) ~ hormon, data = brcancer,
#>     df = 4, scale = "hazard")
#>
#> Covariate effects (hazard scale):
#>           coef exp(coef) [HR] se(coef)      z Pr(>|z|)
#> hormon -0.3641         0.6948   0.1249 -2.914  0.00356 **
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Baseline spline terms:
#>                   coef  exp(coef)   se(coef)      z Pr(>|z|)
#> (Intercept) -1.960e+01  3.086e-09  3.257e+00 -6.016 1.79e-09
#> s(logt)1     2.993e+00  1.994e+01  5.966e-01  5.016 5.26e-07
#> s(logt)2    -4.429e-01  6.422e-01  5.711e-01 -0.776   0.4380
#> s(logt)3     1.357e+00  3.884e+00  8.188e-01  1.657   0.0974
#> s(logt)4    -8.841e-01  4.131e-01  4.176e-01 -2.117   0.0342
#>
#> n = 686 , number of events = 299 , parameters = 6
#> Log-likelihood = -2606   AIC = 5225   BIC = 5247

The summary keeps the interpretable covariate hazard ratio (hormon) separate from the baseline spline coefficients, which describe the shape of the baseline hazard and are not individually interpretable, matching rstpm2/flexsurv convention.

Prediction and plotting

predict(fit, newdata = data.frame(hormon = c(0, 1)), times = c(500, 1000, 2000), type = "survival")
#>   id time   est
#> 1  1  500 0.829
#> 2  1 1000 0.628
#> 3  1 2000 0.408
#> 4  2  500 0.878
#> 5  2 1000 0.724
#> 6  2 2000 0.536

plot(fit, newdata = data.frame(hormon = c(0, 1)),
     col = c("steelblue", "firebrick"), main = "rpsurv: predicted survival")

predict() also returns type = "hazard", "cumhaz", or "link" (the linear predictor), with se.fit = TRUE adding delta-method 95% confidence limits for "survival", "cumhaz", and "link".

Time-varying effects (non-proportional hazards)

A covariate’s value is fixed for a subject, but its coefficient can be allowed to change over follow-up via its own spline in log time, requested with tve:

fit_tve <- rpsurv(Surv(rectime, censrec) ~ hormon, data = brcancer,
                   df = 4, tve = "hormon", tve.df = 2)
coef(fit_tve)

A likelihood-ratio test of fit_tve against fit (both maximum-likelihood fits, nested models) tests the proportional-hazards assumption for hormon directly.

This is distinct from a time-varying covariate, where the covariate’s value itself changes during follow-up. That is a data-representation question, not a modeling one: split each subject’s follow-up into intervals over which the covariate is constant and supply counting-process data via Surv(start, stop, status). rpsurv detects the 3-column Surv object and fits the left-truncated likelihood automatically, correctly accounting for each interval’s dependence on survival to its start time under the covariate value of the previous interval. The same mechanism also handles ordinary left truncation (delayed entry) when covariate values don’t change. See vignette("rpsurv") for a full worked example and the underlying likelihood.

Diagnostics

residuals(fit, type = "coxsnell")   # also "martingale", "deviance"
coxsnell_plot(fit)                  # Cox-Snell residual plot
km_compare_plot(fit)                # fitted vs. Kaplan-Meier overlay

Validation

Coefficients, standard errors, and log-likelihoods agree with rstpm2::stpm2 to 4-5 decimal places across all three scales (hazard, odds, normal) and with flexsurv::flexsurvspline on the hazard scale. See tests/testthat/test-rpsurv.R for the full parity suite, which also covers left truncation.

Benchmark vs. rstpm2 and flexsurv

Wall-clock fit time on simulated Weibull-hazard data with two covariates, reproducible via data-raw/benchmark.R (shipped with the package source, not installed). flexsurv::flexsurvspline is materially slower per fit, so it was only run up to n=2e4 to keep the benchmark tractable; rstpm2::stpm2 was run up to n=2e5.

At n=200,000, rpsurv is about 12x faster than rstpm2::stpm2 and continues to scale to n=500,000 (rstpm2 was not run at that size). At n=20,000, rpsurv is about 29x faster than flexsurv::flexsurvspline. The advantage widens with n in both comparisons: rpsurv does O(n) work per optimizer step in a single fused parallel pass over the log-likelihood and its analytic gradient, computed together (RcppParallel::parallelReduce), against repeated R-level likelihood evaluation (numerical gradients, or gradients built from R-level matrix algebra) in the other two packages. See vignette("rpsurv") for the full benchmark code and what else is delegated to C++ (the spline basis itself, and closed-form starting values from a bounded Kaplan-Meier subsample).

Bundled datasets

rpsurv ships three datasets, each chosen for a specific feature of the package rather than as a generic example:

• brcancer: German breast cancer recurrence data (686 rows), the baseline fitting example used throughout this README and the vignette. Identical to rstpm2::brcancer, bundled so examples do not require rstpm2 to be installed.
• veteran: Veterans’ Administration lung cancer trial (137 rows). celltype is a well-known non-proportional-hazards effect, which makes this a direct illustration of the tve argument. Identical to survival::veteran.
• heart: Stanford heart transplant data (172 rows), already in counting-process (start, stop, event) format with a time-varying transplant covariate. Illustrates Surv(start, stop, status) fitting on a real dataset rather than a constructed one. Identical to survival::heart.

Notes

• scale selects the transformation of the survival function the spline models: "hazard" (proportional hazards, the Royston-Parmar default), "odds" (proportional odds), or "normal" (probit).
• Boundary knots default to the min/max of log time among events, and interior knots to equally spaced centiles of log time among events (default_knots()), matching rstpm2/flexsurv conventions.
• SystemRequirements: GNU make (for RcppParallel); no other external dependencies.