Clinical Trial Designs in 'rstan'
Convert crm_fit object to data.frame.
Convert efftox_fit object to data.frame.
Convert crm_fit to instance of mcmc.list
Convert efftox_fit to instance of mcmc.list
Cast augbin_2t_1a_fit object to tibble.
Cast dose_finding_paths object to tibble.
Class used by trialr to fit Wason & Seaman's Augmented Binary method...
Class used by trialr to fit Wason & Seaman's Augmented Binary method...
Calculate the binary probability of success.
Dose selection function that practices careful escalation.
Get index of element in vector with value closest to a target
Calculate codified CRM doses.
Calculate dose-transition pathways for a CRM study
Class of model fit by trialr using the CRM dose-finding design.
Container class for parameters to fit the CRM models in trialr.
Fit a CRM model to the incrementally observed outcomes on a trial path...
Get the prior beliefs for a CRM trial scenario.
Process RStan samples from a CRM model.
Parse a string of dose-finding trial outcomes to binary vector notatio...
Class of dose-finding model fit by trialr using Stan.
Class to hold the elements of a single dose-finding analysis residing ...
Get the number of efficacy events seen at the doses under investigatio...
EffTox analysis to data.frame
Plot EffTox utility contours
Calculate dose-transition pathways for an EffTox study
Calculate dose-transition pathways for an EffTox study
Class of model fit by trialr using the EffTox dose-finding design.
Get the Prob(Tox) for Prob(Eff) and utility pairs
Get parameters to run the EffTox demo
Container class for parameters to fit the EffTox model in trialr.
Parse a string of EffTox outcomes to binary vector notation.
Fit an EffTox model to the incrementally observed outcomes on a trial ...
Simple class to hold prior hyperparameters for the EffTox model.
Process RStan samples from an EffTox model
Run EffTox simulations
Calculate the p-index for EffTox utility contours
Get dose-superiority matrix in EffTox
Get the utility of efficacy & toxicity probability pairs
Plot densities of EffTox dose utilities
Get normal prior hyperparameters for the EffTox model.
Get the number of patients treated at the doses under investigation.
Parse a string of dose-finding trial outcomes.
Parse a string of phase I/II dose-finding trial outcomes.
Get data to run the PePS2 trial example
Process RStan samples from a BEBOP model fit to PePS2 data
Plot an crm_fit
Plot an efftox_fit
Predict probability of success for given tumour size measurements.
Print augbin_fit object.
Print crm_fit object.
Print efftox_fit object.
Print nbg_fit object.
Sample data from the Augmented Binary model prior predictive distribut...
Calculate the probability of success.
Calculate the probability that the rate of toxicity exceeds some thres...
Sample pairs of correlated binary events
Sample LKJ correlation matrices.
Spread the information in dose_finding_paths object to a wide data.fra...
Fit Wason & Seaman's Augmented Binary model for tumour response.
Simple helper function to demonstrate fitting of an Augmented Binary m...
Fit a CRM model
Fit an EffTox model
Fit the EffTox model presented in Thall et al. (2014)
Fit the hierarchical response model described by Thall et al. (200...
Fit a Neuenschwander, Branson & Gsponer logit dose-finding model
Fit the P2TNE model developed for the PePS2 trial to some outcomes.
Obtain summary of an crm_fit
Obtain summary of an efftox_fit
Get the total weight of patient outcomes at the doses under investigat...
Get the number of toxicity events seen at the doses under investigatio...
The 'trialr' package.
Run a simulation study.
Get the weights of patient outcomes at the doses under investigation.
A collection of clinical trial designs and methods, implemented in 'rstan' and R, including: the Continual Reassessment Method by O'Quigley et al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and more. We provide functions to aid model-fitting and analysis. The 'rstan' implementations may also serve as a cookbook to anyone looking to extend or embellish these models. We hope that this package encourages the use of Bayesian methods in clinical trials. There is a preponderance of early phase trial designs because this is where Bayesian methods are used most. If there is a method you would like implemented, please get in touch.