Quantile Regression for Clinical Outcomes
A practical guide to quantile regression for clinical outcomes, including interpretation of median and other quantile treatment effects, clinical examples, assumptions, confidence intervals, R implementation, and comparison with ordinary least-squares regression.
Progression-Free Survival vs. Overall Survival
A practical clinical biostatistics guide to progression-free survival versus overall survival, including endpoint definitions, censoring, hazard ratios, Kaplan-Meier interpretation, treatment effects, common pitfalls, and oncology trial examples.
Power Analysis for ANOVA Designs
A practical guide to power analysis for ANOVA designs, including Cohen
Poisson Regression for Count Data in Clinical Trials
A practical guide to Poisson regression for count data in clinical trials, including rate ratios, exposure offsets, model assumptions, overdispersion, recurrent events, worked clinical examples, R and SAS implementation, and interpretation.
Permutation Tests for Clinical Endpoints
A practical guide to permutation tests for clinical endpoints, including the permutation principle, exact and Monte Carlo permutation tests, randomized treatment comparisons, p-values, exchangeability, covariate adjustment, survival endpoints, and R implementation.
Pearson vs. Spearman Correlation Coefficients
A practical guide to Pearson and Spearman correlation coefficients in clinical research, including when to use each method, assumptions, interpretation, confidence intervals, examples, ties, outliers, and R and SAS implementation.
Pattern-Mixture Models for Missing Data
A practical guide to pattern-mixture models for missing data in clinical trials, including missingness patterns, MAR versus MNAR, pattern-specific distributions, delta adjustment, tipping-point analysis, sensitivity analysis, and implementation in R and SAS.
Outlier Detection Methods in Clinical Datasets
A practical guide to detecting, investigating, and handling outliers in clinical datasets using graphical methods, IQR rules, robust statistics, z-scores, modified z-scores, clinical reference ranges, regression diagnostics, Mahalanobis distance, and R examples.
One-Sample and Two-Sample t-Tests in Clinical Data
A practical guide to one-sample and two-sample t-tests in clinical research, including hypotheses, assumptions, pooled and Welch t-tests, paired analyses, confidence intervals, effect sizes, clinical interpretation, and implementation in R and SAS.
Non-Parametric Tests: Wilcoxon and Mann-Whitney U
A practical guide to non-parametric testing in clinical research, including the Wilcoxon signed-rank test, Wilcoxon rank-sum test, Mann-Whitney U test, assumptions, hypotheses, exact and asymptotic methods, effect sizes, clinical examples, and R implementation.