Overview

FastSurvival provides fast alternatives to the standard survival analysis functions in the survival package, together with a simulation layer for designing and evaluating time-to-event trials. Every function is designed for repeated evaluation inside large simulation loops, where the iterative or object-building overhead of the standard implementations becomes a bottleneck. Core computations are implemented in C++ via Rcpp.

library(FastSurvival)

Function families

The package has two families of functions.

The estimation and testing functions operate on a single dataset and return an S3 object with a print() method. survfit_fast() evaluates the Kaplan-Meier estimate at a single time point. survdiff_fast() computes the log-rank test and its weighted and stratified variants. coxph_fast() returns a closed-form hazard ratio. rmst_fast() returns the restricted mean survival time, and wmst_fast() the window mean survival time over an interval. milestone_fast() compares survival at a milestone timepoint, and medsurv_fast() compares median survival times. maxcombo_fast() computes the max-combo test, rmw_fast() the robust modestly-weighted log-rank test, and wkm_fast() the weighted Kaplan-Meier (Pepe-Fleming) test. ahsw_fast() computes the average hazard with survival weight, and ahr_fast() the Kalbfleisch-Prentice average hazard ratio.

The simulation functions support a full simulation study. simdata_fast() generates individual patient data for one- or two-group trials. analysis_fast() performs interim or sequential analyses of the simulated data at one or more looks, and can compute any of the estimation and testing statistics above, optionally within subgroups. simsummary_fast() aggregates the operating characteristics from the analysis output against supplied boundaries.

A minimal example

The following example uses the ovarian dataset from the survival package, with a one-sided test of treatment benefit (side = 1).

library(survival)

# Single-time-point Kaplan-Meier estimate
ord <- order(ovarian$futime)
survfit_fast(ovarian$futime[ord], ovarian$fustat[ord],
             t_eval = 500, conf.type = "log")
#> Kaplan-Meier survival estimate (single time point)
#> 
#>         survival std.err lower 95% upper 95%
#> t = 500   0.5961  0.0999    0.4291    0.8279
#> 
#>  Confidence interval type: log

# Log-rank test
survdiff_fast(ovarian$futime, ovarian$fustat, ovarian$rx,
              control = 1, side = 1)
#> Log-rank test (two-group)
#> 
#>   N = 26,  control = 1
#> 
#>           Observed Expected (O-E)^2/E (O-E)^2/V
#> control          7   5.2335    0.5962    1.0627
#> treatment        5   6.7665    0.4612    1.0627
#> 
#>  Z = -1.031,  one-sided p-value = 0.1513  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Hazard ratio via the Pike-Halley Estimator
coxph_fast(ovarian$futime, ovarian$fustat, ovarian$rx,
           control = 1, side = 1)
#> Pike-Halley estimator for the hazard ratio (two-group)
#> 
#>   control = 1
#>   alternative = one.sided
#> 
#> Coefficients:
#>          coef exp(coef) se(coef)      z Pr(>|z|)
#> group -0.5964    0.5508   0.5868 -1.016    0.155
#> 
#> Hazard ratio and 95% Wald confidence interval:
#>       exp(coef) exp(-coef) lower .95 upper .95
#> group    0.5508     1.8155    0.1744    1.7399

Where to go next

Several further vignettes cover the package in depth. Validation of FastSurvival checks numerical agreement with established packages on a real clinical-trial dataset. Speed comparison quantifies the performance gain. Group sequential design with the simulation trio demonstrates simdata_fast(), analysis_fast(), and simsummary_fast() against a gsDesign reference. Further applied vignettes work the simulation trio through nonproportional-hazards, correlated multiple-endpoint, and multiregional settings.

References

Homma, G. (2025). One step from Pike to Cox: a closed-form hazard ratio estimator. Manuscript under review.

Collett, D. (2014). Modelling Survival Data in Medical Research (3rd ed.). Chapman and Hall/CRC.