# Adaptive designs with data-driven population selection (Case study C1) # Visit MedianaDesigner's online manual (https://medianasoft.github.io/MedianaDesigner) for more case studies library(MedianaDesigner) ######################################################################### parameters = list() # Endpoint type parameters$endpoint_type = "Time-to-event" # Direction of favorable outcome parameters$direction = "Higher" # Number of enrolled patients (control, treatment) parameters$sample_size = c(480, 480) # Prevalence of biomarker-positive patients in the overall population parameters$prevalence = 0.5 # Median PFS times in the control arm (biomarker-negative subpopulation, biomarker-positive subpopulation) (months) parameters$control_time = c(11, 11) # Median PFS times in the treatment arm (biomarker-negative subpopulation, biomarker-positive subpopulation) (months) parameters$treatment_time = c(13, 15.7) # Target event count at Final analysis (overall population, biomarker-positive subpopulation) parameters$event_count = c(700, 300) # Information fractions at Interim analysis 1, Interim analysis 2, Final analysis parameters$info_frac = c(0.2, 0.6, 1) # Futility threshold for conditional power at Interim analysis 1 parameters$futility_threshold = 0.2 # Influence threshold at Interim analysis 2 parameters$influence = 0.1 # Interaction threshold at Interim analysis 2 parameters$interaction = 1.3 # Enrollment period (months) parameters$enrollment_period = 18 # Median enrollment time (months) parameters$enrollment_parameter = 13.5 # Annual patient dropout rate based on an exponential dropout distribution parameters$dropout_rate = 0.05 # One-sided alpha level parameters$alpha = 0.025 # Number of simulations parameters$nsims = 10000 # Run simulations to compute operating characteristics results = ADPopSel(parameters) # Generate a simulation report GenerateReport(results, "CaseStudyC1.docx")