accrual.T.inform

Prediction of time frame with Informative Prior for a certain number of participants.

accrual

Bayesian

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

Bayesian Accrual Prediction

Yu Jiang

Cen Wu

Steve Simon

Matthew S. Mayo

Rama Raghavan

Byron J. Gajewski

accrual.T.inform function

<dl> <dt>n</dt>
<dd>Target sample size</dd> <dt>TT</dt>
<dd>Target completion time</dd> <dt>P</dt>
<dd>The prior certainty, range 0-1</dd> <dt>m</dt>
<dd>Sample observed to date</dd> <dt>tm</dt>
<dd>Time to date</dd> <dt>np</dt>
<dd>The specific number of participants want to be predicted</dd></dl>

Arguments

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

Author

Prediction of Time with Informative Prior — accrual.T.inform

<dl>

 <dt>n</dt>
<dd>Target sample size</dd>

 <dt>TT</dt>
<dd>Target completion time</dd>

 <dt>P</dt>
<dd>The prior certainty, range 0-1</dd>

 <dt>m</dt>
<dd>Sample observed to date</dd>

 <dt>tm</dt>
<dd>Time to date</dd>

 <dt>np</dt>
<dd>The specific number of participants want to be predicted</dd>

</dl>

accrual.T.inform: Prediction of Time with Informative Prior

Description

Usage

Value

Arguments

Author

Examples