# Blinded event prediction in event-driven trials (Case study E1) # Visit MedianaDesigner's online manual (https://medianasoft.github.io/MedianaDesigner) for more case studies library(MedianaDesigner) ######################################################################### parameters = list() # Load a built-in data set with the patient enrollment, event and dropout information (EventPredData) parameters$data_set = EventPredData # Future time points for computing event predictions parameters$time_points = seq(from = 12, to = 24, by = 1) # Prior distribution for the event hazard rate based on the # expected median time of 15 months and the uncertainty parameter of 0.3 (low confidence prior) parameters$event_prior_distribution = EventPredPriorDistribution(expected = log(2) / 15, uncertainty = 0.3) # Prior distribution for the patient dropout hazard rate based on the # expected median time of 150 months and the uncertainty parameter of 0.3 (low confidence prior) parameters$dropout_prior_distribution = EventPredPriorDistribution(expected = log(2) / 80, uncertainty = 0.3) # Prior distribution for the intensity rate of the patient enrollment process based on the # expected enrollment rate of 35 patients per month and the uncertainty parameter of 0.3 (low confidence prior) parameters$enrollment_prior_distribution = EventPredPriorDistribution(expected = 35, uncertainty = 0.3) # Number of simulations parameters$nsims = 1000 ######################################################################### # Forecast the number of events at the pre-defined time points results = EventPred(parameters) # Generate a simulation report GenerateReport(results, "CaseStudyE1.docx")