Zero-Inflated Models for Clinical Count Outcomes
A practical guide to zero-inflated Poisson and zero-inflated negative binomial models for clinical count outcomes, including structural zeros, excess zeros, model formulation, interpretation, likelihoods, model selection, R implementation, diagnostics, and a complete worked example.
Robust Regression Methods for Clinical Data
A practical guide to robust regression methods for clinical data, including Huber and MM estimation, influence diagnostics, robust standard errors, quantile regression, clinical sensitivity analyses, and R and SAS implementation.
Ridge and Lasso Regression in Biostatistics
A practical guide to ridge and lasso regression in biostatistics, including penalized regression, multicollinearity, variable selection, cross-validation, interpretation, and worked R examples.
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.
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.
Negative Binomial Regression for Overdispersed Count Data
A practical clinical biostatistics tutorial on negative binomial regression for overdispersed count data, including the model, dispersion parameter, incidence rate ratios, offsets, worked clinical examples, SAS and R code, model checking, interpretation, and common mistakes.
Multinomial Logistic Regression Explained
A practical guide to multinomial logistic regression for clinical research, including baseline-category logits, interpretation of coefficients and odds ratios, global tests, predicted probabilities, a worked clinical example, R and SAS implementation, model diagnostics, and common mistakes.
Model Selection: AIC vs. BIC in Clinical Research
A practical guide to AIC and BIC for selecting statistical models in clinical research, including the formulas, interpretation, worked examples, likelihood-based model comparison, nested and non-nested models, regression, survival analysis, mixed models, and practical guidance for clinical statisticians.
Logistic Regression Part 2: A Worked Clinical Example
A practical worked clinical example of multivariable logistic regression, including model specification, odds ratios, confidence intervals, adjusted treatment effects, predicted probabilities, interpretation, R implementation, SAS implementation, model diagnostics, and common reporting mistakes.
Interaction Terms in Regression Models
A practical guide to interaction terms in regression models, including effect modification, interpretation of product terms, continuous and categorical interactions, clinical examples, marginal effects, model visualization, R and SAS implementation, and common mistakes.