Biostatistics and medical statistics tutorials for physicians and researchers. Learn hypothesis testing, ANOVA, regression analysis, statistical distributions, and inferential statistics for clinical research.
Randomized trials are not always possible. Observational data can still tell us something about treatment effects, but only if we deal with a basic problem: treated and untreated patients usually differ before treatment even starts. Inverse Probability of Treatment Weighting (IPTW) is one way to handle this. In this tutorial we build an IPTW analysis … Read more
Non-inferiority Trials in Medicine: Last updated: June 2026 Author: Michele D. Pierri Reading time: 15–20 minutes In clinical research, we are used to thinking that a new drug, device, or procedure should be tested by asking a simple question: Is it better than what we already have? This is the logic of a superiority trial. … Read more
Non-Parametric Statistics in Medicine: A Practical Guide for Skewed, Ordinal, and Small Clinical Datasets Last updated: May 2026 Author: Michele D. Pierri Reading time: 15–20 minutes Introduction Clinical data rarely behave like the textbook examples we encounter in introductory statistics courses. Length of stay tends to be right-skewed. Biomarkers such as C-reactive protein, ferritin, D-dimer, … Read more
How to Put a Turbo on Regression Models Bootstrap Validation and Cubic Splines for Medical Data A practical guide for data scientists and clinical researchers working with limited medical datasets Last updated: December 2025 Author: Michele D. Pierri Reading time: 15–20 minutes Introduction — Why regression often disappoints in medicine Regression models are still the … Read more
A Complete Guide to Multiple Imputation for Missing Data: When, How, and Why Last updated: November 2025 Author: Michele D. Pierri Reading time: 15–20 minutes Glossary MCAR: Missing Completely At Random. Missingness is unrelated to observed or unobserved data; complete case can be unbiased. MAR: Missing At Random. Missingness depends only on observed variables; standard … Read more
Introduction Decision Curve Analysis (DCA) is a powerful tool for evaluating the clinical utility of predictive models and diagnostic tests. Unlike traditional metrics like AUC or calibration, DCA focuses on what truly matters in practice: whether using a model leads to better decisions and outcomes. DCA was introduced by Vickers and Elkin in 2006 to … Read more
Effect Size: What It Is and Why It Matters More Than Statistical Significance A result can be statistically significant — yet practically meaningless. Learn how effect size reveals the real-world impact of research findings. Introduction: The Hidden Problem with p-values You’ve probably seen headlines like: “New Study Shows Coffee Improves Memory!” But what if the … Read more
Introduction: Understanding Calibration Challenges Consider a thermometer that perfectly identifies when one temperature is higher or lower than another, but consistently reads 5 degrees too high. This thermometer has good discrimination (it correctly ranks temperatures), but poor calibration (its absolute values are inaccurate). The same problem occurs with clinical risk models such as EuroSCORE: they … Read more
Definition Sensitivity analysis is a collection of techniques that determine how input parameters affect model results. Specifically, it measures how much variation in the results stems from different types of uncertainty. For a model: $Y=f(X_1,X_2,X_3…..X_n)$ examines how Y changes when each X is modified. Sensitivity analysis can be applied across several key areas: predictive models, … Read more
Both one-way ANOVA (Analysis of Variance) and repeated measures ANOVA employ the F statistic as a tool to evaluate the null hypothesis that there are no significant differences between the groups being studied. Numerous other statistical tests utilize the F statistic, including regression, Levene’s and Bartlett’s tests for homogeneity of variance, MANOVA, ANCOVA, Wilks’ lambda … Read more