FastSurvival

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 — adaptive sample-size re-estimation, probability-of-success calculations, regional consistency evaluation in multi-regional trials — where the standard iterative or object-building overhead of survfit(), survdiff(), and coxph() becomes a computational bottleneck. Core computations are implemented in C++ via Rcpp for maximum performance.

The full documentation site, including function references and articles, is available at https://gosukehommaEX.github.io/FastSurvival/.

Functions

Estimation and testing

Simulation

Visualization

The estimation and testing functions return S3-class objects with print() methods that display the results in a format similar to the corresponding survival package functions.

Installation

# Install the released version from CRAN
install.packages("FastSurvival")

# Or install the development version from GitHub
# install.packages("remotes")
remotes::install_github("gosukehommaEX/FastSurvival")

Quick start

library(FastSurvival)
library(survival)

# ----------------------------------------------------------------
# survfit_fast(): Kaplan-Meier at a single time point
# ----------------------------------------------------------------
ord <- order(ovarian$futime)
t_s <- ovarian$futime[ord]
e_s <- ovarian$fustat[ord]

survfit_fast(t_s, e_s, t_eval = 500, conf.type = "log")

# ----------------------------------------------------------------
# survdiff_fast(): log-rank test
# ----------------------------------------------------------------
survdiff_fast(ovarian$futime, ovarian$fustat, ovarian$rx,
              control = 1, side = 2)

# ----------------------------------------------------------------
# coxph_fast(): hazard ratio via the Pike-Halley Estimator (closed-form)
# ----------------------------------------------------------------
coxph_fast(ovarian$futime, ovarian$fustat, ovarian$rx, control = 1)

# ----------------------------------------------------------------
# rmst_fast(): restricted mean survival time (two-group)
# ----------------------------------------------------------------
rmst_fast(ovarian$futime, ovarian$fustat, ovarian$rx,
          control = 1, tau = 500)

# ----------------------------------------------------------------
# wmst_fast(): window mean survival time over [100, 500]
# ----------------------------------------------------------------
wmst_fast(ovarian$futime, ovarian$fustat, ovarian$rx,
          control = 1, tau1 = 100, tau2 = 500)

# ----------------------------------------------------------------
# medsurv_fast(): median survival time (two-group)
# ----------------------------------------------------------------
medsurv_fast(ovarian$futime, ovarian$fustat, ovarian$rx, control = 1)

# ----------------------------------------------------------------
# wkm_fast(): weighted Kaplan-Meier (Pepe-Fleming) test
# ----------------------------------------------------------------
wkm_fast(ovarian$futime, ovarian$fustat, ovarian$rx, control = 1)

# ----------------------------------------------------------------
# simdata_fast(): clinical trial simulation
# ----------------------------------------------------------------

# Two-group trial, simple exponential, no dropout
df <- simdata_fast(
  nsim     = 1000,
  n        = c(100, 100),
  a.time   = c(0, 12),
  a.rate   = 200 / 12,
  e.median = list(18, 24),
  seed     = 1
)
head(df)

# Two-group trial, piecewise exponential (delayed treatment effect)
df2 <- simdata_fast(
  nsim     = 1000,
  n        = c(100, 100),
  a.time   = c(0, 12),
  a.rate   = 200 / 12,
  e.hazard = list(c(0.08, 0.08), c(0.08, 0.04)),
  e.time   = c(0, 6, Inf),
  seed     = 2
)

# Two correlated endpoints (e.g. PFS and OS) from an illness-death model:
# an intermediate event (h01) and a terminal event (h02). With the post-event
# hazard defaulting to h02 this is the Fleischer maximal-independence model.
df3 <- simdata_fast(
  nsim       = 1000,
  n          = c(100, 100),
  a.time     = c(0, 12),
  a.rate     = 200 / 12,
  h01.median = list(6, 9),     # intermediate event (e.g. progression)
  h02.median = list(14, 18),   # terminal event (e.g. death)
  seed       = 3
)

Design principles

survfit_fast evaluates the Kaplan-Meier estimator at a single specified time point. A C++ backend locates the evaluation cutoff via binary search, then accumulates the Kaplan-Meier product and the Greenwood variance sum in a single scan over event positions, without constructing intermediate vectors. This makes it orders of magnitude faster than survfit() plus summary() when the same sorted data are evaluated repeatedly at a fixed landmark time inside a simulation loop.

survdiff_fast computes the log-rank statistic using a two-pointer merge scan over the pooled sorted data, walking the time axis once while maintaining per-group at-risk counters and processing tied event times atomically. The C++ backend avoids the rank construction, tabulate(), and reverse cumulative sum operations of the standard R implementation, and the function itself bypasses the S3 object construction and formula parsing of survdiff(). Beyond the ordinary unweighted test, it supports the Fleming-Harrington, modestly-weighted, Gehan-Breslow, and Tarone-Ware weighted tests, as well as stratified and stratified-weighted variants, all sharing the same single-scan backend.

coxph_fast implements the Pike-Halley Estimator proposed by Homma (2025), a closed-form approximation to the Cox partial likelihood maximizer. The estimator anchors at the Pike closed-form estimate and applies a single analytic Halley correction to the Cox score, giving residual error of order O_p(n^{-3/2}) relative to the Cox maximum likelihood estimate. On the pharmacoSmoking dataset (tie rate 77.5%), the Pike-Halley Estimator reproduces the Breslow-based Cox estimate to within on the order of 1e-08. The Wald confidence interval uses the observed information at the Pike anchor as the variance estimate. The C++ backend performs group splitting, at-risk counting, and per-distinct-event-time accumulation in a single pass.

rmst_fast integrates the Kaplan-Meier survival step function up to a horizon in a single C++ scan, reused once per group. With a single group it returns the RMST with a Greenwood-type standard error and a Wald confidence interval; with two groups it adds the RMST difference and ratio contrasts, each with a standard error, confidence interval, and two-sided test.

wmst_fast computes the window mean survival time, the area under the Kaplan-Meier curve between a lower and an upper window limit, generalizing the restricted mean survival time (which is the special case with a lower limit of zero). It targets late differences and early crossings by focusing the mean on a clinically relevant window, following Paukner and Chappell (2021). The same single C++ scan as rmst_fast accumulates the windowed area and a Greenwood-type variance in which each event time contributes its squared remaining window area. With two groups it reports the difference (treatment minus control) with a standard error, confidence interval, and two-sided test.

milestone_fast compares the Kaplan-Meier survival probabilities of two groups at a prespecified milestone timepoint, estimating the difference (treatment minus control). Three inference methods are provided: a Greenwood Wald interval, and two methods that build the difference interval by the method of variance estimates recovery (MOVER) from complementary log-log and log transformed one-sample intervals, following Tang (2021).

medsurv_fast estimates the Kaplan-Meier median survival time and, for two groups, the difference (treatment minus control), in a single C++ scan that locates the median on the Kaplan-Meier curve. Because the variance of a quantile depends on the local hazard, two variance methods are provided: a native method using a kernel-smoothed hazard with a Greenwood increment, and an nph-compatible method using a local constant hazard, which reproduces the standard error and p-value of the median comparison in the nph package to numerical precision. The point estimate is the same under both methods.

maxcombo_fast computes the max-combo test, the maximum over a set of Fleming-Harrington weighted log-rank statistics, for comparing survival under non-proportional hazards. The C++ backend evaluates every weighted numerator and the full between-scheme covariance matrix in a single scan, and the p-value follows from the implied multivariate normal distribution. Taking the most extreme of several complementary weights makes the test robust to the shape of the hazard difference, with the multiplicity accounted for through the joint distribution.

rmw_fast computes the robust modestly-weighted (rMW) test of Magirr and Öhrn (2026), the maximum of the standard log-rank statistic and a single modestly-weighted log-rank statistic. The modestly-weighted component uses the survival-threshold parameterization, with the weight capped at the reciprocal of s_star (so s_star = 0.5 caps the weight at 2 and s_star = 1 recovers the log-rank test). The C++ backend computes both component numerators, both variances, and their null covariance in a single scan, and the combined statistic and p-value follow from the implied bivariate normal distribution. Because the standard log-rank statistic is one of the two components and the components are strongly correlated under the null, the multiplicity adjustment is small, so the test loses little power relative to the log-rank test in worst-case scenarios while gaining power under delayed effects.

wkm_fast computes the weighted Kaplan-Meier test of Pepe and Fleming (1989), the weighted integral of the difference between the two Kaplan-Meier curves, standardized to a Wald z statistic. Unlike weighted log-rank tests it targets the integrated difference in survival and remains sensitive when the hazards cross. The default Pepe-Fleming combined weight, built from the censoring distributions of the two groups, gives less weight to regions with heavy censoring; square-root and constant weights are also available. The single C++ scan evaluates the per-group Kaplan-Meier curves, the censoring weights, and the variance over the pooled event grid, and the result reproduces the weighted Kaplan-Meier statistic of the nphsim package.

ahsw_fast computes the average hazard with survival weight of Uno and Horiguchi for two groups. The average hazard on the window up to a horizon is the ratio of the cumulative event probability to the restricted mean survival time, both from the Kaplan-Meier estimate. The function returns the per-group average hazard, the ratio of average hazards (RAH) on the log scale, and the difference of average hazards (DAH) on the identity scale, each with a confidence interval and a two-sided test.

ahr_fast computes the average hazard ratio of Kalbfleisch and Prentice (1981) between two groups over a restricted interval, the estimator used by Dormuth et al. (2024) for sample-size calculation under non-proportional hazards. Writing the two survival functions as S0 and S1, each group’s share of the total hazard is obtained by integrating one survival curve against the increments of the other; the average hazard ratio is the ratio of the two shares, and equals the true hazard ratio under proportional hazards. A C++ backend builds both Kaplan-Meier curves and their Greenwood variances on the pooled event grid, then evaluates the shares, their direct variances, and the double-integral covariance terms in linear scans. The primary test is on the group-share scale, as in the source method, with an equivalent test and a confidence interval reported on the log scale.

simdata_fast generates individual patient data for one- or two-group time-to-event trials. Accrual times follow a piecewise-uniform distribution, and survival and dropout times follow either a simple or piecewise exponential distribution, selected automatically based on whether a scalar or vector hazard is supplied. Optional subgroups are defined by a prevalence specification. It can also generate two correlated time-to-event endpoints, such as progression-free and overall survival, from an illness-death model with transition hazards for the intermediate event, the direct terminal event, and the post-event terminal event, and with optional treatment switching. The entire generation pipeline runs in a single C++ kernel that materializes the output data frame once, avoiding intermediate R-level vector operations and copies, and random-number generation uses dqrng for speed.

analysis_fast performs interim or sequential analyses of simulated two-group data at one or more looks, defined either by a target cumulative number of events or by a calendar time. At every look the data are administratively censored at the corresponding calendar cutoff, and the requested statistics are computed for each simulated trial. The censoring, time sorting, and per-cell statistics are handled by a fused C++ kernel that reuses the same analysis cores as the standalone functions, so the same results are obtained without the per-iteration overhead of repeated wrapper calls. Statistics can also be reported within each subgroup.

pairwise_fast compares each experimental arm against a shared control on multi-arm data by running analysis_fast() once per contrast and stacking the results with an arm column. With fixed calendar looks every contrast shares the same data cutoff. With an event-driven design a designated primary contrast fixes the per-simulation calendar cutoff at which all contrasts are analyzed, reproducing the standard rule that the primary analysis defines a single data cutoff. An optional Bonferroni adjustment controls the family-wise error rate across the contrasts at each look.

simsummary_fast aggregates the per-simulation, per-look output of analysis_fast() into operating characteristics: the rejection rate, the futility-stopping rate, the distribution of the stopping look, and the expected analysis timing. It consumes group-sequential boundaries computed elsewhere (for example by gsDesign or rpact) and estimates the stopping probabilities by Monte Carlo over the simulated trials; it does not compute or spend alpha itself.

gen_scenario_fast assembles one or more two-group scenarios, each a complete set of simdata_fast() arguments, into a single object for design-stage exploration. Its plot() method draws, for every scenario, the analytic control and experimental survival curves with the piecewise-constant hazard ratio on a secondary axis, computed directly from the piecewise-exponential parameters without simulation, and its print() method summarizes the medians, the hazard ratio at the start and end of the window, and whether the curves cross. The same object can be passed to simdata_fast() to generate the data.

kmcurve_fast builds the Kaplan-Meier step functions of a single realized data set, for example one replicate from simdata_fast(), for both the two-group and single-group cases. Its plot() method draws the curves and can shade the area under each curve up to a truncation time to visualize the restricted mean survival time, and can add a second panel showing the smoothed time-varying hazard ratio, a kernel-smoothed Nelson-Aalen estimate, together with the constant Cox proportional-hazards estimate. Its print() method reports the sample sizes, events, and medians, with the Cox hazard ratio in the two-group case.

Using the functions together

A typical simulation workflow combines the simulation and analysis layers. simdata_fast() generates the data once, analysis_fast() performs the looks and computes the statistics for every simulated trial, and simsummary_fast() aggregates the operating characteristics against the supplied boundaries:

library(FastSurvival)

# 1. Simulate a two-group trial with a delayed treatment effect
df <- simdata_fast(
  nsim     = 1000,
  n        = c(350, 350),
  a.time   = c(0, 12),
  a.rate   = 700 / 12,
  e.hazard = list(c(0.08, 0.08), c(0.08, 0.04)),
  e.time   = c(0, 6, Inf),
  d.hazard = list(0.01, 0.01),
  seed     = 42
)

# 2. Analyze at two interim looks defined by target event counts
res <- analysis_fast(
  df, control = 1,
  event.looks = c(300, 450),
  stat = "logrank", side = 1
)

# 3. Summarize operating characteristics against efficacy boundaries
#    (nominal levels from a group-sequential design, e.g. gsDesign)
oc <- simsummary_fast(
  res,
  p.col = "logrank.p",
  alpha = c(0.0006, 0.0238)
)
oc

The estimation functions can also be used directly inside a loop. Each returned object is internally a numeric vector, so after stripping the class with unclass() the per-simulation results can be combined with rbind() into an ordinary numeric matrix:

results <- vector("list", 1000L)
for (s in seq_len(1000L)) {
  d <- df[df$sim == s, ]
  results[[s]] <- coxph_fast(d$tte, d$event, d$group, control = 1)
}

hr_mat <- do.call(rbind, lapply(results, unclass))
colMeans(hr_mat)

References

Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457-481.

Mantel, N. (1966). Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemotherapy Reports, 50(3), 163-170.

Gehan, E. A. (1965). A generalized Wilcoxon test for comparing arbitrarily single-censored samples. Biometrika, 52, 203-223.

Tarone, R. E., & Ware, J. (1977). On distribution-free tests for equality of survival distributions. Biometrika, 64, 156-160.

Fleming, T. R., & Harrington, D. P. (1991). Counting Processes and Survival Analysis. New York: John Wiley & Sons.

Pepe, M. S., & Fleming, T. R. (1989). Weighted Kaplan-Meier statistics: a class of distance tests for censored survival data. Biometrics, 45(2), 497-507.

Pepe, M. S., & Fleming, T. R. (1991). Weighted Kaplan-Meier statistics: large sample and optimality considerations. Journal of the Royal Statistical Society. Series B (Methodological), 53(2), 341-352.

Magirr, D., & Burman, C.-F. (2019). Modestly weighted logrank tests. Statistics in Medicine, 38(20), 3782-3790.

Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society. Series B (Methodological), 34(2), 187-220.

Berry, G., Kitchin, R. M., & Mock, P. A. (1991). A comparison of two simple hazard ratio estimators based on the logrank test. Statistics in Medicine, 10(5), 749-755.

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

Royston, P., & Parmar, M. K. B. (2013). Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome. BMC Medical Research Methodology, 13, 152.

Paukner, M., & Chappell, R. (2021). Window mean survival time. Statistics in Medicine, 40(25), 5521-5533.

Uno, H., Claggett, B., Tian, L., et al. (2014). Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis. Journal of Clinical Oncology, 32(22), 2380-2385.

Tang, Y. (2021). Some new confidence intervals for Kaplan-Meier based estimators from one and two sample survival data. Statistics in Medicine, 40(23), 4961-4976.

Tang, Y. (2022). Complex survival trial design by the product integration method. Statistics in Medicine, 41(4), 798-814.

Karrison, T. G. (2016). Versatile tests for comparing survival curves based on weighted log-rank statistics. The Stata Journal, 16(3), 678-690.

Lin, R. S., Lin, J., Roychoudhury, S., et al. (2020). Alternative analysis methods for time to event endpoints under nonproportional hazards: a comparative analysis. Statistics in Biopharmaceutical Research, 12(2), 187-198.

Magirr, D., & Öhrn, F. (2026). Robust modestly weighted log-rank tests. Pharmaceutical Statistics, 25(1), e70066.

Uno, H., & Horiguchi, M. (2023). Ratio and difference of average hazard with survival weight: new measures to quantify survival benefit of new therapy. Statistics in Medicine, 42(7), 936-952.

Kalbfleisch, J. D., & Prentice, R. L. (1981). Estimation of the average hazard ratio. Biometrika, 68(1), 105-112.

Dormuth, I., Pauly, M., Rauch, G., & Herrmann, C. (2024). Sample size calculation under nonproportional hazards using average hazard ratios. Biometrical Journal, 66(6), e202300271.

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

License

MIT © 2026 Gosuke Homma