Predictive analytics mean the difference between finishing enrollment on time and on budget and delaying a trial because of recruitment problems . Companies that use predictive analytics to power the entire recruitment process finish more trials on time. That’s why predictive analytics form the core of the StudyOptimizer application. Developed by clinical trial optimization experts, StudyOptimizer’s dynamic, self-adjusting predictive analytics enable your study team to input assumptions, create a plan, adjust the plan to keep enrollment on track, and collaborate across global teams.
Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns. The predictive analytics model underlying StudyOptimizer’s Enrollment Forecasting Engine is based on a stochastic model framework that includes probability functions with expected mean values and variances. Under this model, the user is able to specify the enrollment problem variables that will drive these probability functions, e.g.: expected initial recruitment estimates, by countries and across centers, as well as the defining the rules for enrollment, e.g.: competitive, balanced or restricted enrollment. Based on DecisionView’s industry-driven development approach, the model implements S-curves rather than simple linear planning models, as these more closely predict real-world enrollment behavior.
The predictive analytics model underlying StudyOptimizer’s Enrollment Forecasting Engine was developed in conjunction with clinical operations teams and statisticians from global life sciences companies. DecisionView’s Predictive Science Lab continues to expand its efforts to test and refine the Enrollment Forecasting Engine.
