A non-parametric approach to predict the recruitment for randomized clinical trial

Successfully recruiting the prespecified number of trial participants is critical and remains challenging to the success of clinical trials. Although various types of prediction models for recruitment have been developed in the past, they either relied on assumptions of parametric distributions or prior information on the recruitment rate. We developed a recruitment model using a simulation-based non-parametric approach for clinical trials based on inpatient settings such as those taking place in acute care for the elderly (ACE) units at UTMB. We examined recruitment logs, we studied patterns and evaluated parametric assumptions. We found that violation of assumptions is common in real-world settings. We then conducted the simulation of future enrollment based on the empirical distribution from recruitment logs. We proposed a weighted approach that put higher weights on enrollment from prior dates near the later enrolling dates. Using simulated distributions and resampling techniques, we calculated confidence intervals for recruitment numbers at the end of the time allotted for recruitment and for the time needed to finalize recruitment. We compared our method with previously published Bayesian method using our proposed measures of efficiency. The preliminary results demonstrate that a simulation-based non-parametric approach is feasible to be used as a prediction model for clinical trial recruitment.

Clinical trial recruitment

prediction model

simulation

non-parametric

weighted sampling 

Practice and Applications

Symposium on Data Science and Statistics (SDSS) 2024