Non-Parametric Recruitment Prediction for Randomized Clinical Trials

Accurate prediction of subject recruitment for Randomized Clinical Trials (RCT) remains an ongoing challenge. Many previous prediction models rely on parametric assumptions. We present functions for non-parametric RCT recruitment prediction under several scenarios.

RCTRecruit

Installation

Accurate prediction of subject recruitment for randomized clinical trials (RCT) remains an ongoing challenge. Many previous prediction models rely on parametric assumptions. We present functions for non-parametric RCT recruitment prediction under several scenarios.

You can install the development version of RCTRecruit from GitHub with:

devtools::install_github("imalagaris/RCTRecruit")

Example

library(RCTRecruit)
LoadData(gripsYR1, ScreenDt, Enrolled)
#> 
#> Variables Enrolled and ScreenDt were successfully loaded
Time2Nsubjects()
#> Enrolling 50 subjects requires 137 weeks
#> 
#>  2.5%   50% 97.5% 
#>   137   137   137
GetDistance(gripsYR2Weekly$enrolled)
#>  2.5%   50% 97.5% 
#>    85    85    85
res <- GetWeekPredCI()
plot(res)