Longitudinal data analysis
Graduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.
Topics typically covered
Standard syllabus
Longitudinal data structure
- Balanced vs unbalanced panels
- Missing data patterns: MCAR, MAR, MNAR
- Exploratory analysis of trajectories
- Correlation structures over time
- Time-varying vs time-invariant covariates
Modeling approaches
- Mixed-effects (multilevel) models
- Random intercepts and random slopes
- Generalized estimating equations (GEE)
- Autoregressive and Toeplitz correlation models
- Growth curve models
Inference and diagnostics
- ML and REML estimation
- Kenward–Roger corrections (introduction)
- Model comparison for nested mixed models
- Diagnostics for longitudinal residuals
STEM / applied
Applied longitudinal analysis
- Fitting mixed models in R (lme4, nlme) or Stata
- Visualization of individual trajectories
- Clinical trial repeated measures case studies
- Education panel data applications
- Power for longitudinal studies
- Reporting random effects and ICC
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Graduate biostatistics and statistics course. Covers mixed models and GEE with emphasis on correlated within-subject observations.