Bayesian Adaptive Designs for Diagnostic Trials

Simulate clinical trials for diagnostic test devices and evaluate the operating characteristics under an adaptive design with futility assessment determined via the posterior predictive probabilities.

adaptDiag

The goal of adaptDiag is to simplify the process of designing adaptive trials for diagnostic test studies. With accumulating data in a clinical trial of a new diagnostic test compared to a gold-standard reference, decisions can be made at interim analyses to either stop the trial for early success, stop the trial for expected futility, or continue to the next sample size look. Designs can be focused around test sensitivity, specificity, or both. The package is heavily influenced by the seminal article by Broglio et al. (2014).

References

Broglio KR, Connor JT, Berry SM. Not too big, not too small: a Goldilocks approach to sample size selection. Journal of Biopharmaceutical Statistics, 2014; 24(3): 685–705.

Installation

You can install the development version of adaptDiag GitHub with:

# install.packages("devtools")
devtools::install_github("graemeleehickey/adaptDiag")