clinical-trialslisted

# Clinical Trials Statistical Methods ## Overview Comprehensive clinical trial design and analysis methods in R covering sample size calculation, randomization, interim analyses, multiplicity adjustment, and regulatory-compliant statistical methods. ## Sample Size Calculation ### Two-Group Comparisons ```r library(pwr) # Two-sample t-test pwr.t.test( d = 0.5, # Cohen's d effect size sig.level = 0.05, power = 0.80, type = "two.sample", alternative = "two.sided" ) # Proportions (chi-square) pwr.2p.test( h = ES.h(p1 = 0.6, p2 = 0.4), # Cohen's h sig.level = 0.05, power = 0.80 ) # Two proportions (unequal groups) pwr.2p2n.test( h = ES.h(p1 = 0.6, p2 = 0.4), n1 = 100, sig.level = 0.05 ) ``` ### Survival Endpoints ```r library(gsDesign) # Log-rank test sample size nSurv( lambda1 = log(2)/12, # Control median = 12 months lambda2 = log(2)/18, # Treatment median = 18 months (HR = 0.67) Ts = 24, # Study duration Tr = 12, # Accrual duration alpha = 0.025, # One-sided beta = 0.20, # 80% power ratio = 1 # 1:1 randomization ) # Using rpact library(rpact) getSampleSizeSurvival( hazardRatio = 0.67, lambda1 = log(2)/12, accrualTime = 12, followUpTime = 12, alpha = 0.025, beta = 0.20, allocationRatioPlanned = 1 ) ``` ### Non-Inferiority Trials ```r library(TrialSize) # Non-inferiority for proportions TwoSampleProportion.NIS( p = 0.80, # Expected propo