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 0.1.4Updated dependencies to account for recent breaking changes in tibble v3.0.0.
trialr 0.1.3Adjusted uses of tidyr::unnest to suppress warnings when using v1.0 of tidyr.
trialr 0.1.2Added 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 0.1.1Plumbed in support for tidybayes. Added pathways analysis for CRM and rewrote the same for EffTox.
trialr 0.1.0Added TITE-CRM implmentation plus tests and vignette. Rebased package to the format now advocated by rstantools.
trialr 0.0.7Making package work with staged installation, with help from Tomas Kalibera.
trialr 0.0.6Fixing some duplicated vignette titles
trialr 0.0.5Adding changes advised by rstan maintainer so that package may build on Solaris.
trialr 0.0.4Updated to use rstan 2.18.1, which in-turn has been updated to use C++14.
trialr 0.0.3This release updates some Stan code that was generating C++ code that would compile on Linux, Mac & Windows, but not Solaris.
trialr 0.0.2This 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 0.0.1First release with implementations of: - Thall & Cookâs EffTox dose-finding clinical trial design; - Thall et al.âs hierarchical Bayesian phase II design for diseases with multiple subtypes - Brock et al.âs BEBOP design for efficacy and toxicity outcomes in phase II where predictive information
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