FastSurvival 1.0.0

New features

• coxph_fast() gains a strata argument for a stratified Pike-Halley estimate of a common hazard ratio with a separate baseline hazard in each stratum (#1, suggested by Isaac Gravestock). The risk sets are formed within each stratum, and the observed and expected totals, the score, the information, and the curvature term are summed over strata, so the result approximates coxph(... + strata(s), ties = "breslow"). The strata argument of analysis_fast() now also stratifies stat = "coxph".

Bug fixes

• simdata_fast() with subgroups failed when the sample size was given as a scalar n with alloc (#2, reported by Isaac Gravestock), because a scalar n with a common prevalence was always treated as a single group. A scalar n now gives a two-group simulation when alloc is supplied explicitly or when a survival or dropout specification is a per-group list with per-cell elements (such as list(list(0.10, 0.08, 0.06), 0.05)), and the result is identical to the per-group n specification. Without either signal, a list with subgroups is still read as per-cell values of a single group. The rules are described in the Details of ?simdata_fast. In the same area:
  • a shared (non-list) survival or dropout specification is accepted in a two-group simulation with subgroups;
  • lists of the wrong length are reported instead of being silently truncated or failing with “subscript out of bounds”;
  • per-cell e.time and d.time lists are honored in a one-group simulation with subgroups;
  • group-specific prevalence must use the same number of levels for each factor in both groups;
  • the illness-death model also treats an explicit alloc as a two-group request;
  • without subgroups, a scalar n with an explicit alloc and a shared (non-list) hazard now gives two groups sharing that hazard, where it previously gave one group of size n and ignored alloc.
• analysis_fast() with stat = "milestone", ms.method = "loglog", and side = 1 returned the upper-tail p-value although the log-log statistic is negative under treatment benefit, so the one-sided p-value was approximately one minus the correct value. It now uses the lower tail, as milestone_fast() does.
• analysis_fast() now reports the "ahsw" p-values according to side, as ahsw_fast() does; they were always two-sided before.
• pairwise_fast() in event-driven mode reported every administratively censored subject as a dropout (n.pipeline was always 0). Dropout and pipeline counts are now computed at the per-simulation cutoff. The event-driven mode also keeps the other columns of data, so strata works there, and an ambiguous p.col (several statistics) is reported clearly.
• The log-rank family (survdiff_fast(), maxcombo_fast(), rmw_fast(), and the weighted and stratified variants) left out of the observed and expected counts an event at a time when only one subject was at risk. The test statistics were unaffected, but the printed counts differed from survival::survdiff().
• medsurv_fast() (and stat = "medsurv" in analysis_fast()) now follows the survival::survfit() median convention: the comparison with 0.5 uses a tolerance, and a curve that equals 0.5 on a flat stretch gives the midpoint of that stretch. Before, floating-point rounding could move the median to the next event time. With method = "km" the kernel hazard is evaluated at the new median, so its standard error changes in the flat-stretch case as well. The printed median of kmcurve_fast() objects uses the same step-function rule instead of linear interpolation.
• simdata_fast() with a scalar n split the total with round(), which could lose or add a subject (for example n = 7 gave 4 + 4). The split now always adds up to n, and alloc is validated.
• simdata_fast() with fixed.alloc = TRUE assigned the subgroup cells in contiguous blocks, so subgroup membership was tied to the accrual interval. The fixed labels are now randomly permuted within each simulation, which changes the generated data for fixed.alloc = TRUE (only) for a given seed. The fixed counts are also protected against floating-point shares such as 100 * 0.29.
• simdata_fast() in the illness-death model now treats a per-group d.hazard or d.median list as a two-group request, as in the single-endpoint model.
• milestone_fast() (and stat = "milestone") returned a NaN standard error when a Kaplan-Meier curve reached zero by the milestone; it is now 0, as in survfit_fast().
• survfit_fast() caps the upper limit of the "log" interval at 1 and gives a degenerate interval when the standard error is zero, as survival::survfit() does.
• The print method of survdiff_fast() labels the unweighted stratified test as stratified.
• simsummary_fast() silently kept only the last row when data had more than one row per simulation and look, such as the stacked arms of pairwise_fast() output. It now summarizes each arm separately when data has an arm column (the output gains an arm column and the print method a heading per arm), and stops with an error for other duplicated rows.
• The print method of simsummary_fast() no longer runs the label “Expected analysis time at stop:” into its value.

Input validation

• rmst_fast(), ahsw_fast(), milestone_fast(), wmst_fast() (a supplied tau2), and ahr_fast() (a supplied tau) warn when the truncation time or milestone exceeds the largest observed time of a group, where the Kaplan-Meier curve is not estimated and is carried forward flat.
• survdiff_fast(), coxph_fast(), rmst_fast(), maxcombo_fast(), rmw_fast(), ahsw_fast(), survfit_fast(), and analysis_fast() now check that event is coded 0/1 without missing values and, for the two-group functions, that group has exactly two values without missing values and that control is one of them. Before, a wrong control label or a 1/2 event coding silently produced wrong or missing results.
• analysis_fast() requires whole-number event.looks and recodes factor and character subgroup columns consistently, so by.subgroup = TRUE labels the populations correctly for such columns.
• survfit_fast() checks that t_sorted and e_sorted have the same length and, with presorted = TRUE, that the times are sorted.
• simdata_fast() checks that hazards are non-negative and that piecewise breakpoints start at 0 and increase, and that the output fits in an R vector.
• milestone_fast(), medsurv_fast(), and wmst_fast() reject missing times or groups.

Documentation

• The modestly-weighted weight cap is documented as 1 / S(t_star-), the pooled Kaplan-Meier value just before t_star, which is what the code computes (as in nphRCT).
• The speed-comparison vignette reports speed gains re-measured for this release.
• The validation vignette compares the stratified coxph_fast() with coxph() using strata().
• The ahr_fast() documentation explains that, as in the AHR package, the estimate is not symmetric in the groups when both groups have events at the same time, so control should be the actual reference group.
• The ahsw_fast() documentation states the actual condition for NA results (no events up to tau in a group).
• The analysis_fast() documentation no longer lists a look.type column, which the function does not return.

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.