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gsDesignTune

Enables systematic, dependency-aware scenario exploration for group sequential designs created by gsDesign. gsDesignTune is built for design-space evaluation (ranking, filtering, Pareto trade-offs) rather than claiming a single “optimal design”. With a focus on user experience, correctness, and speed, it supports off-the-shelf parallel processing with progress tracking, caching, and reproducible reporting.

Installation

You can install the development version of gsDesignTune from GitHub with:

# install.packages("pak")
pak::pak("nanxstats/gsDesignTune")

Features

• Drop-in workflow: replace gsDesign()/gsSurv()/gsSurvCalendar() with gsDesignTune()/gsSurvTune()/gsSurvCalendarTune(), then $run().
• Dependency-aware tuning: express design parameter dependency relationships precisely, for example, spending functions and their spending parameters.
• Grid and random search over candidate sets, with vector-valued arguments treated atomically.
• Parallel evaluation via {future} / {future.apply} with progress via {progressr}. Use any {future} backend that fits your infrastructure.
• Reproducible and auditable results: per-configuration warnings/errors and reconstructable underlying call.
• Optional caching of design objects to disk and HTML reporting via {rmarkdown}.

Quick start

Evaluate time-to-event designs:

library(gsDesign)
library(gsDesignTune)
library(future)

plan(multisession, workers = 2)

job <- gsSurvTune(
  k = 3,
  test.type = 4,
  alpha = 0.025,
  beta = 0.10,
  timing = tune_values(list(c(0.33, 0.67, 1), c(0.5, 0.75, 1))),
  hr = tune_seq(0.55, 0.75, length_out = 5),
  upper = SpendingFamily$new(
    SpendingSpec$new(sfLDOF, par = tune_fixed(0)),
    SpendingSpec$new(sfHSD, par = tune_seq(-4, 4, length_out = 9))
  ),
  lower = SpendingSpec$new(sfLDOF, par = tune_fixed(0)),
  lambdaC = log(2) / 6,
  eta = 0.01,
  gamma = c(2.5, 5, 7.5, 10),
  R = c(2, 2, 2, 6),
  T = 18,
  minfup = 6,
  ratio = 1
)

job$run(strategy = "grid", parallel = TRUE, seed = 1, cache_dir = "gstune_cache")

res <- job$results()
head(res)

job$best("final_events", direction = "min")
job$pareto(metrics = c("final_events", "upper_z1"), directions = c("min", "min"))

job$plot(metric = "final_events", x = "hr", color = "upper_fun")

job$report("gstune_report.html")

Tune specifications

• tune_fixed(x): explicit fixed value (useful inside dependencies)
• tune_values(list(...)): explicit candidates (supports vector-valued candidates)
• tune_seq(from, to, length_out), tune_int(from, to, by)
• tune_choice(...): categorical choices
• tune_dep(depends_on, map): dependent mapping for any argument

See vignettes for end-to-end examples, spending function tuning, and parallel + reproducible reporting.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.