Time-to-First-Event vs. Recurrent Event Analysis
A practical guide to choosing between time-to-first-event and recurrent event analysis in clinical trials, including estimands, censoring, information loss, Andersen-Gill models, Prentice-Williams-Peterson models, frailty models, mean cumulative function, worked examples, and reporting considerations.
Random Slopes vs. Random Intercepts Explained
A practical guide to random intercepts and random slopes in linear mixed-effects models, including interpretation, covariance structures, model comparison, longitudinal clinical trial examples, R lme4 code, SAS PROC MIXED, and common modeling mistakes.
MMRM Part 2: Sensitivity Analyses and Robustness
A practical guide to sensitivity analyses and robustness checks for mixed models for repeated measures (MMRM), including covariance structures, missing-data assumptions, multiple imputation, pattern-mixture models, reference-based imputation, tipping-point analyses, alternative estimands, and SAS and R implementation.
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.
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 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.
Handling Non-Linear Trajectories in Longitudinal Data
A practical guide to modeling non-linear trajectories in longitudinal clinical data using polynomial terms, splines, categorical time, piecewise models, mixed-effects models, and flexible time structures in R and SAS.
Growth Curve Modeling in Clinical Research
A practical guide to growth curve modeling in clinical research, including longitudinal trajectories, linear mixed-effects models, fixed and random effects, time-by-treatment interactions, covariance structures, model interpretation, R implementation, and clinical trial examples.
GEE vs. Mixed Models: When to Use Each
A practical guide to choosing between generalized estimating equations and mixed-effects models for longitudinal and clustered clinical data, including population-averaged versus subject-specific effects, correlation structures, missing data, interpretation, R and SAS examples, and a worked clinical trial example.
Covariance Structure Selection in Longitudinal Models
A practical guide to covariance structure selection in longitudinal mixed models, including compound symmetry, AR(1), heterogeneous covariance structures, unstructured covariance, Toeplitz models, AIC, BIC, REML, ML, convergence, and worked R and SAS examples.