Sample Size for Two-Sample t-Tests
A practical guide to sample size calculation for two-sample t-tests, including effect size, pooled standard deviation, type I error, power, equal allocation, one- and two-sided tests, a complete worked example, and R implementation.
Sample Size for Survival Endpoints (Log-Rank Test)
A practical guide to sample size calculation for survival endpoints using the log-rank test, including hazard ratios, required events, power, allocation ratio, event proportions, accrual and follow-up, dropout, and a complete worked example.
Sample Size for Proportions (Two-Sample)
A practical guide to calculating sample size for comparing two independent proportions, including assumptions, two-sample proportion formulas, type I error, power, allocation ratios, continuity corrections, dropout inflation, R implementation, and a complete worked example.
Sample Size for Non-Inferiority Trials
A practical guide to sample size calculation for non-inferiority clinical trials, including non-inferiority margins, one-sided type I error, power, continuous and binary endpoints, dropout inflation, worked examples, formulas, and R implementation.
Sample Size for Equivalence Trials
A practical guide to sample size determination for equivalence trials, including equivalence margins, the two one-sided tests procedure, continuous and binary endpoints, power, dropout inflation, a complete worked example, and R implementation.
Sample Size for Cluster Randomized Trials
A practical guide to sample size calculation for cluster randomized trials, including the intracluster correlation coefficient, design effect, cluster size, inflation for clustering, attrition, number of clusters, and a complete worked example.
Sample Size Re-Estimation in Adaptive Trials
A practical guide to sample size re-estimation in adaptive clinical trials, including blinded and unblinded re-estimation, nuisance parameter adjustment, conditional power, type I error control, decision rules, and a complete worked example.
Power Analysis for ANOVA Designs
A practical guide to power analysis for ANOVA designs, including Cohen
Understanding Minimal Clinically Important Difference (MCID)
A practical and computational guide to the Minimal Clinically Important Difference (MCID), including anchor-based and distribution-based methods, responder definitions, confidence intervals, sample size implications, interpretation, and clinical trial examples.
Simon’s Two-Stage Design: A Worked Example
A practical and computational guide to Simon’s two-stage design for single-arm oncology trials, including early stopping for futility, type I error, power, expected sample size, a complete worked example, and implementation in R.