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.
Maintainer: Graeme L. Hickey [email protected] (ORCID)
Authors:
Ying Wan [email protected]
Thevaa Chandereng [email protected] (ORCID) (bayesDP code as a template)
Other contributors:
Becton, Dickinson and Company [copyright holder]
Tim Kacprowski [email protected] (For code from fastlogrank R package.) [contributor]