accrual.n.inform

Produce an output for prediction of the number of participants can be recruited in a fixed time frame with Informative Prior.

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.n.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>Tp</dt>
<dd>The specific time that want to predict the recruitment</dd></dl>

Arguments

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

Author

Prediction of Accrual with Informative Prior in Fixed Time Frame — accrual.n.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>Tp</dt>
<dd>The specific time that want to predict the recruitment</dd>

</dl>

accrual.n.inform: Prediction of Accrual with Informative Prior in Fixed Time Frame

Description

Usage

Value

Arguments

Author

Examples