trialr 0.1.5

Added the Neuenschwander, Branson & Gsponer model for dose-escalation. Also added a method for calculating EffTox parameter priors. Re-ordered vignettes to be more intuitive. Suppressed all the MCMC log messages in the tests scripts.

trialr 0.1.4

Updated dependencies to account for recent breaking changes in tibble v3.0.0.

trialr 0.1.3

Adjusted uses of tidyr::unnest to suppress warnings when using v1.0 of tidyr.

trialr 0.1.2

Added the AugBin model for phase II response assessment in cancer. Just the two-stage single-arm version added for now. The others will follow. Also added a general routine for running simulation studies.

trialr 0.1.1

Plumbed in support for tidybayes. Added pathways analysis for CRM and rewrote the same for EffTox.

trialr 0.1.0

Added TITE-CRM implmentation plus tests and vignette. Rebased package to the format now advocated by rstantools.

trialr 0.0.7

Making package work with staged installation, with help from Tomas Kalibera.

trialr 0.0.6

Fixing some duplicated vignette titles

trialr 0.0.5

Adding changes advised by rstan maintainer so that package may build on Solaris.

trialr 0.0.4

Updated to use rstan 2.18.1, which in-turn has been updated to use C++14.

trialr 0.0.3

This release updates some Stan code that was generating C++ code that would compile on Linux, Mac & Windows, but not Solaris.

trialr 0.0.2

This release adds the Continual Reassessment Method (CRM) for dose-finding. Four model variants are currently given: - empiric likelihood with normal prior on slope; - logistic likelihood with constant intercept and normal prior on slope; - logistic likelihood with constant intercept and gamma prior on slope; - logistic likelihood with normal priors on the intercept and slope.

This release also adds general model-fitting functions stan_crm and stan_efftox. It also adds S3 classes crm_fit and efftox_fit with the expected generic methods.

Unit tests have been added.

trialr 0.0.1

First release with implementations of: - Thall & Cook’s EffTox dose-finding clinical trial design; - Thall et al.’s hierarchical Bayesian phase II design for diseases with multiple subtypes - Brock et al.’s BEBOP design for efficacy and toxicity outcomes in phase II where predictive information