goldilocks (version 1.0.0)

goldilocks-package: goldilocks: Adaptive Trial Designs for Survival and Binary Endpoints

Description

Implements Goldilocks adaptive trial designs for time-to-event and fixed-time binary endpoints. Outcomes are generated with a piecewise exponential model, with conjugate Gamma priors used for predictive imputation. Final analyses may use log-rank, Cox, or restricted mean survival time tests, Bayesian piecewise-exponential inference, frequentist risk differences, or Bayesian beta-binomial inference. The method closely follows Broglio et al. (2014) tools:::Rd_expr_doi("10.1080/10543406.2014.888569") and supports simulation of design operating characteristics.

Author

Maintainer: Graeme L. Hickey (ORCID)

Authors:

• Ying Wan

• Thevaa Chandereng (ORCID) (bayesDP code as a template)

Other contributors:

• Becton, Dickinson and Company [copyright holder]

• Tim Kacprowski (For code from fastlogrank R package.) [contributor]

See Also

Useful links:

• https://graemeleehickey.github.io/goldilocks/

• https://github.com/graemeleehickey/goldilocks

• https://CRAN.R-project.org/package=goldilocks

• Report bugs at https://github.com/graemeleehickey/goldilocks/issues