Linear Mixed Effects Models for Longitudinal Data
A practical guide to linear mixed effects models for longitudinal clinical data, covering fixed and random effects, treatment-by-time interactions, covariance structures, REML, missing data, R, SAS, diagnostics, and clinical interpretation.
Last Observation Carried Forward: Why It’s Outdated
A practical guide to why Last Observation Carried Forward (LOCF) is generally outdated for handling missing longitudinal clinical trial data, including its assumptions, biases, alternatives such as MMRM and multiple imputation, regulatory considerations, and practical R examples.
Landmark Analysis in Survival Studies
A practical guide to landmark analysis in survival studies, including landmark time selection, time-dependent exposure, immortal time bias, Kaplan–Meier analysis, landmark Cox models, interpretation, worked examples, and common pitfalls.
Joint Modeling of Longitudinal and Survival Data
A practical guide to joint modeling of longitudinal and survival data, including longitudinal mixed models, survival submodels, shared random effects, association structures, dynamic prediction, interpretation, R implementation, diagnostics, and a complete clinical example.
Introduction to Statistical Power
A practical introduction to statistical power in clinical research, including type II error, effect size, sample size, significance level, power calculations, interpretation, and common mistakes.
Introduction to Repeated Measures ANOVA
An introduction to repeated measures ANOVA for clinical and longitudinal data, covering within-subject designs, between- and within-subject effects, assumptions, sphericity, Greenhouse-Geisser correction, post-hoc comparisons, effect sizes, interpretation, and R implementation.
Interval-Censored Survival Data Analysis
A practical guide to interval-censored survival data analysis, including likelihood construction, Turnbull
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.
Instrumental Variable Analysis in Clinical Research
A practical guide to instrumental variable analysis in clinical research, including confounding by indication, randomized encouragement designs, treatment nonadherence, the IV assumptions, Wald estimators, two-stage least squares, local average treatment effects, diagnostics, R implementation, and a complete worked example.
Holm-Bonferroni Step-Down Procedure
A practical guide to the Holm-Bonferroni step-down procedure for controlling family-wise error rate, including the algorithm, adjusted p-values, a complete worked example, comparison with ordinary Bonferroni, and implementation in R and SAS.