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FastSurvival: Fast Survival Analysis Functions for Simulation Studies

Source: R/FastSurvival-package.R
FastSurvival-package.Rd

FastSurvival provides fast alternatives to the standard survival analysis functions in the survival package. Every function is designed for repeated evaluation inside large simulation loops, such as adaptive sample-size re-estimation, probability-of-success calculations, and regional consistency evaluation in multi-regional clinical trials. Core computations are implemented in C++ via Rcpp for maximum performance inside simulation loops.

Details

The package exports four functions. The three analysis functions return S3-class objects ("survfit_fast", "survdiff_fast", "coxph_fast") with print() methods that format the results similarly to the corresponding survival package output. Each object is internally a numeric vector, so it can be used directly in arithmetic, subsetting, and aggregation after stripping the class with unclass.

survfit_fast

Kaplan-Meier survival probability, standard error, and confidence interval at a single specified time point. The C++ backend locates the evaluation cutoff via binary search and accumulates the Kaplan-Meier product and Greenwood sum in a single scan over event positions only, without constructing intermediate vectors, making it approximately 50 times faster than survfit() plus summary() for repeated single-time-point evaluations.

survdiff_fast

Log-rank test statistic (one-sided Z-score or two-sided chi-square) for two-group survival data. The C++ backend uses a two-pointer merge scan over pooled sorted vectors, eliminating the rank(), tabulate(), and rev(cumsum(rev(...))) overhead of the standard implementation, making it approximately 40 times faster than survdiff().

coxph_fast

Closed-form hazard ratio estimator via the Pike-Halley Estimator method, with Wald confidence interval. The Pike-Halley Estimator anchors at the Pike estimate and applies a single analytic Halley correction to the Cox partial likelihood score, achieving residual error of order O_p(n^{-3/2}) relative to the Cox maximum likelihood estimate. The C++ backend performs group splitting, at-risk counting, and per-distinct-event-time accumulation in a single pass, making it approximately 30 times faster than coxph().

simdata_fast

Clinical trial data simulator for one- and two-group time-to-event trials. Supports piecewise uniform accrual, simple and piecewise exponential survival and dropout times. C++ backends handle piecewise sampling and two-group interleaving, and random number generation uses dqrng, making it approximately 4 times faster than an equivalent pure-R implementation.

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.

See also

Useful links:

Author

Maintainer: Gosuke Homma my.name.is.gosuke@gmail.com

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