Scale Conventions in BayesianMCPMod | Calculation of a MAP Prior | 1) Establish MAP prior (beta mixture distribution) | 2) Robustify prior | 3) Translate prior to logit scale (to approximate via normal mixture model) | Specification of reference scale (this follows the idea of [@Neuenschwander2016]). | Specify a prior list | Trial Data | Posterior Calculation | Bayesian MCPMod Test Step | Model Fitting and Visualization | Assessment of the Minimally Efficacious Dose | Additional Note

Background and Data | Calculation of a MAP Prior | Specification of the New Trial Design | Calculation of the Success Probabilities

Introduction | Calculation of a MAP Prior | Dose-Response Model Shapes | Trial Data | Posterior Calculation | Bayesian MCPMod Test Step | Model Fitting and Visualization | Assessment of the Minimally Efficacious Dose | Additional Note

Background and Data | Calculation of a MAP Prior | Specification of the New Trial Design | Calculation of the Success Probabilities | Assessment of the Minimally Efficacious Dose

Introduction | Varying the Expected Effect for Maximum Dose | Convergence of Power Values

Setup of the parallel backend | Running some bhmbasket code on HPC

Analysis of a Basket Trial's Outcome | Creating the trial | Running the model | Getting the estimates | Assessing the Operating Characteristics of a Basket Trial Design | Simulating the scenarios | Running the model | Estimating the biases and MSEs | Estimating the cohort-wise go probabilities | References