# Adaptive designs with data-driven sample size or event count re-estimation (Case study A1) # 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$sample_size = c(230, 230) # Target event count at Final analysis (before event count re-estimation) parameters$event_count = 330 # Median survival time in the control arm (months) parameters$control_time = 16 # Median survival time in the treatment arm (months) parameters$treatment_time = 22.9 # Information fractions at Interim analysis 1, Interim analysis 2, Final analysis (before event count re-estimation) and FA (after event count re-estimation) parameters$info_frac = c(0.3, 0.6, 1, 1.3) # Futility threshold for conditional power at Interim analysis 1 parameters$futility_threshold = 0.1 # Promising interval for conditional power at Interim analysis 2 parameters$promising_interval = c(0.5, 0.9) # Target conditional power for increasing the number of events at Interim analysis 2 parameters$target_power = 0.9 # Enrollment period (months) parameters$enrollment_period = 36 # Median enrollment time (months) parameters$enrollment_parameter = 24 # 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 key characteristics of the adaptive design results = ADSSMod(parameters) # Generate a simulation report GenerateReport(results, "CaseStudyA1.docx")