# Adaptive designs with data-driven sample size or event count re-estimation (Case study A2) # Visit MedianaDesigner's online manual (https://medianasoft.github.io/MedianaDesigner) for more case studies library(MedianaDesigner) ######################################################################### parameters = list() # Endpoint type parameters$endpoint_type = "Normal" # Number of enrolled patients (control, treatment) parameters$sample_size = c(200, 200) # Direction of favorable outcome parameters$direction = "Lower" # Patient dropout rate parameters$dropout_rate = 0.2 # Mean and SD in the control arm parameters$control_mean = -15 parameters$control_sd = 25 # Mean and SD in the treatment arm parameters$treatment_mean = -22.5 parameters$treatment_sd = 25 # Information fractions at IA1, IA2, FA (before sample size adjustment) # and FA (after sample size adjustment) parameters$info_frac = c(0.3, 0.7, 1, 1.3) # Futility threshold for conditional power at IA1 parameters$futility_threshold = 0.1 # Promising interval for conditional power at IA2 parameters$promising_interval = c(0.5, 0.9) # Target conditional power for increasing the number of events at IA2 parameters$target_power = 0.9 # 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, "CaseStudyA2.docx")