accrual.plots

The diagnostic panel shows four figures that help to understand the data distribution.
The top-left figure is the exponential quantile plot, which checks whether the distribution of waiting times is exponential.
The top-right figure is the histogram of the waiting times, with the red line is the theoretical exponential distribution.
The figure of waiting time verse cumulative accrual time is shown on the bottom left.
The figure of total accrual verse cumulative accrual time is shown on the bottom right.

exponetial

Diagnostic

Participant recruitment for medical research is challenging. Slow accrual leads to delays 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 the researcher's experience with previous trials and data from the current study, providing reliable predictions on accrual rate for clinical studies. For more details and background on these methodologies, see the publications of Byron, Stephen and Susan (2008) <doi:10.1002/sim.3128>, and Yu et al. (2015) <doi:10.1002/sim.6359>. In this R package, Bayesian accrual prediction functions are presented, which can be easily used by statisticians and clinical researchers.

Junhao Liu

accrual

Bayesian Accrual Prediction

Yu Jiang

Cen Wu

Steve Simon

Matthew S. Mayo

Rama Raghavan

Byron J. Gajewski

accrual.plots function

<dl> <dt>w</dt>
<dd>The accrual or waiting time dataset</dd></dl>

Arguments

Junhao Liu, Yu Jiang, Cen Wu, Steve Simon, Matthew S. Mayo, Rama Raghavan, Byron J. Gajewski

Author

Diagnostic Plots — accrual.plots

<dl>

 <dt>w</dt>
<dd>The accrual or waiting time dataset</dd>

</dl>

accrual.plots: Diagnostic Plots

Description

Usage

Value

Arguments

Author

Examples