Time-Varying Covariates in Survival Models
A practical guide to time-varying covariates in survival analysis, including extended Cox models, counting-process data, start-stop intervals, treatment exposure, time-dependent laboratory measurements, landmark analysis, immortal time bias, R and SAS implementation, and interpretation.
The ICH E9(R1) Estimand Framework Explained
A practical guide to the ICH E9(R1) estimand framework, including treatment, population, variable, intercurrent events, summary measure, estimand strategies, estimators, estimates, and worked clinical trial examples.
Testing the Proportional Hazards Assumption
A practical guide to testing the proportional hazards assumption in Cox regression, including Schoenfeld residuals, graphical diagnostics, time-by-covariate interactions, log-minus-log plots, interpretation, R and SAS implementations, and a complete clinical example.
Surrogate Endpoints in Survival Analysis
A practical guide to surrogate endpoints in survival analysis, including progression-free survival, overall survival, validation of surrogate endpoints, Prentice criteria, trial-level and individual-level surrogacy, treatment effects, correlation, meta-analysis, and oncology examples.
Standard Error vs. Standard Deviation Explained
A practical guide to standard deviation versus standard error, including how each is calculated, what each measures, why standard error decreases with sample size, confidence intervals, hypothesis testing, and clinical research examples.
Skewness and Kurtosis in Clinical Data
A practical guide to skewness and kurtosis in clinical data, including interpretation of asymmetric distributions, tail behavior, outliers, mean versus median, clinical examples, formulas, R implementation, and implications for statistical analysis.
Sensitivity Analyses for Estimands
A practical guide to sensitivity analyses for clinical trial estimands, including ICH E9(R1), intercurrent events, treatment policy, hypothetical, while-on-treatment, composite, principal stratum strategies, missing data assumptions, tipping point analyses, and worked examples.
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.