Series Overview
This comprehensive tutorial series teaches advanced statistical modeling for clinical research using modern R workflows. Each tutorial builds systematically from basic concepts to advanced applications, with tested code examples, real data simulations, and clinical interpretations.
Statistical Modeling Foundations
Introduction to modern R statistical workflows
Learn the fundamentals of statistical modeling with glmmTMB and tidyverse. Covers data preparation, model fitting, and basic interpretation using real clinical trial data.
- glmmTMB for unified model fitting
- tidyverse data manipulation
- Basic mixed-effects models
- Clinical data examples
Model Comparison & Selection
Advanced techniques for model evaluation
Master model comparison techniques using information criteria, likelihood ratio tests, and cross-validation. Learn to select optimal models for clinical research.
- AIC, BIC, and likelihood methods
- Cross-validation techniques
- Model selection strategies
- Overfitting prevention
Visual Diagnostics
Comprehensive model validation through visualization
Develop expertise in model diagnostics using advanced visualization techniques. Learn to identify assumption violations and improve model reliability.
- Residual analysis plots
- Q-Q plots and normality tests
- Influence and leverage measures
- ggplot2 diagnostic workflows
Model Validation
Cross-validation and prediction assessment
Learn advanced validation techniques including bootstrap methods, cross-validation, and prediction intervals for robust statistical inference.
- Bootstrap validation methods
- Cross-validation strategies
- Prediction accuracy assessment
- Learning curve analysis
Emmeans Interpretation
Estimated marginal means and clinical inference
Master the emmeans package for extracting clinically meaningful results from complex models. Learn pairwise comparisons and interaction analysis.
- Marginal means extraction
- Pairwise comparisons
- Interaction effects analysis
- Clinical interpretation methods
Custom Contrasts
Advanced hypothesis testing and contrast design
Design and implement custom contrasts for theory-driven hypothesis testing. Learn polynomial contrasts, effect size interpretation, and multiple testing corrections.
- Custom contrast design
- Polynomial trend analysis
- Effect size calculation
- Multiple testing adjustments
Broom Ecosystem
Tidy modeling workflows and automation
Master the broom ecosystem for consistent model output extraction and automated reporting. Learn batch processing and reproducible analysis pipelines.
- tidy(), glance(), augment() workflows
- Batch model processing
- Automated report generation
- Reproducible analysis pipelines
Clinical Trial Case Study
Complete analysis of crossover epilepsy trial
Apply all concepts in a comprehensive case study analyzing a simulated crossover trial for treatment-resistant epilepsy, including regulatory considerations.
- Crossover trial design
- Advanced mixed-effects modeling
- Regulatory submission analysis
- Precision medicine applications
Covariance Structures
Impact on longitudinal trial analysis
Understand how covariance structure assumptions affect statistical inference in longitudinal studies. Compare compound symmetry, AR(1), unstructured, and Toeplitz patterns.
- Covariance structure comparison
- Impact on precision and power
- Model selection criteria
- Clinical trial implications
rstatix Package
Tidy statistical analysis with group-wise comparisons
Master the rstatix ecosystem for comprehensive statistical testing with tidy output. Learn group-wise multiple comparisons, effect sizes, and ggpubr visualization integration.
- Comprehensive statistical testing functions
- Group-wise multiple comparison corrections
- Effect size calculations and interpretation
- ggpubr integration for statistical annotations
Flextable for Publication-Ready Tables
Professional table creation with Microsoft Word integration
Master flextable for creating publication-quality tables with advanced formatting and Word document integration. Essential for clinical research reporting and regulatory submissions.
- Advanced table formatting and typography
- Conditional formatting and professional styling
- Microsoft Word integration with officer
- Clinical table templates and best practices
🎯 Recommended Learning Path
Follow this structured progression to master advanced statistical modeling for clinical research
Statistical modeling, comparison, and diagnostics
Validation, emmeans, and custom contrasts
Broom workflows, clinical applications, and professional reporting
Technologies & Packages
This series uses cutting-edge R packages and modern statistical methods for clinical research applications.
📊 Core Statistical Packages
- glmmTMB - Unified mixed-effects modeling
- emmeans - Marginal means and contrasts
- broom & broom.mixed - Tidy model outputs
- tidyverse - Modern data science workflow
📈 Visualization & Reporting
- ggplot2 - Advanced statistical graphics
- flextable - Publication-ready tables
- officer - Microsoft Word integration
- rstatix - Tidy statistical testing
- patchwork - Multi-panel compositions
- viridis - Perceptually uniform colors
🏥 Clinical Applications
- Crossover trial analysis
- Longitudinal mixed-effects models
- Regulatory submission methods
- Precision medicine approaches