Package: goldilocks 0.5.0.9000

goldilocks: Goldilocks Adaptive Trial Designs for Time-to-Event Endpoints

Implements the Goldilocks adaptive trial design for a time to event outcome using a piecewise exponential model and conjugate Gamma prior distributions. The method closely follows the article by Broglio and colleagues <doi:10.1080/10543406.2014.888569>, which allows users to explore the operating characteristics of different trial designs.

Authors:Graeme L. Hickey [aut, cre], Ying Wan [aut], Thevaa Chandereng [aut], Becton, Dickinson and Company [cph], Tim Kacprowski [ctb]

goldilocks_0.5.0.9000.tar.gz
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goldilocks_0.5.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
goldilocks/json (API)

# Install 'goldilocks' in R:
install.packages('goldilocks', repos = c('https://graemeleehickey.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/graemeleehickey/goldilocks/issues

Pkgdown/docs site:https://graemeleehickey.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

adaptivebayesianbayesian-statisticsclinical-trialsstatisticscpp

6.12 score 7 stars 15 scripts 329 downloads 10 exports 22 dependencies

Last updated from:4de43c646f. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK181
linux-devel-x86_64OK187
source / vignettesOK209
linux-release-arm64OK198
linux-release-x86_64OK176
macos-release-arm64OK132
macos-release-x86_64OK272
macos-oldrel-arm64OK123
macos-oldrel-x86_64OK286
windows-develOK142
windows-releaseOK155
windows-oldrelOK140
wasm-releaseOK176

Exports:enrollmentppweprop_to_hazpwe_imputepwe_simrandomizationsim_comp_datasim_trialssummarise_simssurvival_adapt

Dependencies:BHclidplyrgenericsgluelatticelifecyclemagrittrMatrixpbmcapplypillarpkgconfigPWEALLR6Rcpprlangsurvivaltibbletidyselectutf8vctrswithr

Bayesian piecewise-exponential designs
When piecewise hazards help | Setting up the design | The Bayesian decision rule | A single simulated trial | Notes on the piecewise model | Sensitivity to the cut-point specification | See also

Last update: 2026-07-01
Started: 2026-06-10

Single-arm designs with a performance goal
The decision rule | Setting up the design | Why block and rand_ratio still appear | Operating characteristics | A practical caveat on benchmarks | See also

Last update: 2026-07-01
Started: 2026-06-10

Technical details of the Goldilocks design
Vignette summary | 1. Design and notation | 2. Event-time model | 3. Posterior distribution of hazards | $$\pi(\boldsymbol{\lambda} \mid \mathcal{D}) | 4. Predictive distribution for incomplete outcomes | $$p(\mathcal{D}^{\mathrm{mis}} \mid \mathcal{D}_{\ell}^{\mathrm{obs}}) | 5. Interim decision algorithm | 5.1 Predictive probability at the current sample size | $$P_ | 5.2 Predictive probability at the maximum sample size | $$P_ | 6. Final analysis | 6.1 Frequentist final tests | 6.2 Bayesian final test | $$\Delta^ | \left[1 - \exp{-H_1^{(b)}(\tau)}\right] | $$\widehat{\Pr}(\Delta < h_0 \mid \mathcal{D}) | 6.3 Loss to follow-up at the final analysis | 7. Operating characteristics | 8. Threshold selection | 9. Relation to group-sequential designs | 10. Package-specific scope | References

Last update: 2026-07-01
Started: 2026-06-16

Package architecture
Overview | Function dependency diagram | Function roles | Simulation layer | Trial engine | Data generation and analysis utilities

Last update: 2026-06-16
Started: 2026-06-09

Two-arm randomized trials
One-sided tests | References

Last update: 2026-06-16
Started: 2021-03-07