Subject recruitment for medical research is challenging. Slow patient accrual leads to delay in research. Accrual monitoring during the process of recruitment is critical. Researchers need reliable tools to manage the accrual rate. We developed a Bayesian method that integrates researcher's experience on previous trials and data from the current study, providing reliable prediction on accrual rate for clinical studies. In this R package, we present functions for Bayesian accrual prediction which can be easily used by statisticians and clinical researchers.
|Author||Junhao Liu, Yu Jiang, Cen Wu, Steve Simon, Matthew S. Mayo, Rama Raghavan, Byron J. Gajewski|
|Date of publication||2016-07-17 23:01:27|
|Maintainer||Junhao Liu <firstname.lastname@example.org>|
accrual.data: Example Accrual Data
accrual.gui: GUI Version of the Bayesian Accrual Prediciton
accrual.n.hedging: Prediction of Accrual with Hedging Prior in Fixed Time Frame
accrual.n.inform: Prediction of Accrual with Informative Prior in Fixed Time...
accrual.n.plot: Plot for Prediction of Accrual in Fixed Time Frame
accrual-package: Bayesian Accrual Prediction
accrual.plots: Dignostic Plots
accrual.T.hedging: Prediction of Time with Hedging Prior
accrual.T.inform: Prediction of Time with Informative Prior
accrual.T.plot: Plot for Prediction of Time