FastSurvival 0.2.0

• New estimation and testing functions:
  • rmst_fast(): restricted mean survival time for a single group or a two-group comparison (difference and ratio contrasts), integrating the Kaplan-Meier survival step function in a single C++ scan.
  • wmst_fast(): window mean survival time over an interval, generalizing rmst_fast() (which is the special case with a lower window limit of zero), for a single group or a two-group difference, computed in the same single C++ scan with a Greenwood-type variance in which each event time contributes its squared remaining window area.
  • milestone_fast(): two-group comparison of Kaplan-Meier survival at a milestone timepoint, with Wald, log-log, and MOVER inference methods.
  • medsurv_fast(): median survival time for a single group or a two-group difference, with a native kernel-hazard variance method and an nph-compatible local-constant-hazard method that reproduces the median comparison of the nph package to numerical precision; the point estimate is the same under both methods.
  • maxcombo_fast(): max-combo test over a set of Fleming-Harrington weighted log-rank statistics, with the joint p-value obtained from the implied multivariate normal distribution.
  • rmw_fast(): robust modestly-weighted log-rank test of Magirr and Öhrn, the maximum of the standard log-rank and a modestly-weighted log-rank statistic, with the joint p-value obtained from the implied bivariate normal distribution.
  • wkm_fast(): weighted Kaplan-Meier (Pepe-Fleming) test, the weighted integrated difference between two Kaplan-Meier curves, with Pepe-Fleming, square-root, and constant weights, reproducing the weighted Kaplan-Meier statistic of the nphsim package.
  • ahsw_fast(): average hazard with survival weight of Uno and Horiguchi, reporting the ratio (RAH) and difference (DAH) contrasts.
  • ahr_fast(): Kalbfleisch-Prentice average hazard ratio between two groups over a restricted interval, the estimator used by Dormuth et al. (2024) for sample-size calculation under non-proportional hazards, with a test on the group-share scale and an equivalent test and confidence interval on the log scale.
• survdiff_fast() gains weighted log-rank tests (Fleming-Harrington, modestly-weighted, Gehan-Breslow, Tarone-Ware) and stratified and stratified-weighted variants, all sharing the single-scan C++ backend.
• New simulation layer:
  • simdata_fast() extended with optional subgroups defined by a prevalence specification and a flexible accrual specification: a.rate gives absolute accrual rates (with the end of an open final interval solved from the total when a trailing rate is supplied) and a.prop gives accrual proportions, with deterministic per-interval accrual counts. The entire generation pipeline runs in a single C++ kernel that materializes the output data frame once. It can also generate two correlated time-to-event endpoints (for example progression-free and overall survival) from an illness-death model with three transition hazards and optional treatment switching, reducing to the Fleischer maximal-independence model when the post-event hazard equals the direct terminal hazard. A vector n of length greater than two together with a per-arm survival list generates a multi-arm trial, each arm produced with the single-group kernel over a common accrual window and labeled 1 to length(n), for analysis as pairwise contrasts against a shared control.
  • analysis_fast(): interim or sequential analysis of simulated data at one or more looks, defined by target event counts or calendar times, computed by a fused C++ kernel that reuses the analysis cores of the standalone functions. Supports subgroup analyses.
  • pairwise_fast(): runs analysis_fast() for each experimental arm against a shared control on multi-arm data, at either fixed calendar looks or the per-simulation cutoffs of a designated primary contrast, and stacks the results with an optional Bonferroni adjustment across contrasts.
  • simsummary_fast(): operating-characteristic summary (rejection and futility rates, stopping-look distribution, expected timing) from analysis_fast() output and supplied group-sequential boundaries, with a print() method that lays the results out as a group-sequential design report.
• New visualization layer:
  • gen_scenario_fast(): assembles one or more two-group scenarios into a scenario_fast object for design-stage exploration, with a plot() method that draws the analytic survival curves and the piecewise hazard ratio of each scenario and a print() method that summarizes the medians, the start and end hazard ratios, and whether the curves cross.
  • kmcurve_fast(): builds the Kaplan-Meier curves of a single trial realization (for example one replicate of simdata_fast()) into a kmcurve_fast object, with a plot() method that draws the curves with optional restricted-mean shading and a smoothed time-varying hazard-ratio panel, and a print() method that summarizes the events and medians.
• Each estimation and testing function has a corresponding print() method, and the print methods share a unified display format.
• New vignettes accompany the analysis, simulation, and visualization layers: validation against established packages, a speed comparison, a group-sequential design reproduction, a log-rank versus RMST comparison under nonproportional hazards, the Freidlin-Korn strong-null investigation, a correlated PFS and OS group-sequential design under the Fleischer model, a multiregional regional-consistency evaluation, and a multi-arm design analyzed as pairwise contrasts against a shared control.

FastSurvival 0.1.0

• Initial release.
• Core computations implemented in C++ via Rcpp for use inside large simulation loops.
• survfit_fast(): single-time-point Kaplan-Meier estimator with Greenwood standard error and plain / log / log-log confidence intervals. The C++ backend locates the evaluation cutoff via binary search and accumulates the Kaplan-Meier product and Greenwood variance sum in a single scan over event positions. Returns an object of class "survfit_fast" with a print() method.
• survdiff_fast(): log-rank test returning a one-sided Z-score or a two-sided chi-square statistic. The C++ backend uses a two-pointer merge scan over pooled sorted vectors, eliminating the rank construction, tabulate(), and reverse cumulative sum operations of the standard implementation. Returns an object of class "survdiff_fast" with a print() method.
• coxph_fast(): closed-form hazard ratio estimator via the Pike-Halley Estimator method with Wald confidence interval. The C++ backend performs group splitting, at-risk counting, and per-distinct-event-time accumulation in a single pass. Returns an object of class "coxph_fast" with a print() method.
• simdata_fast(): clinical trial data simulator supporting one- and two-group designs, piecewise uniform accrual, and simple and piecewise exponential survival and dropout times. C++ backends handle piecewise sampling and two-group interleaving, and random number generation uses dqrng.