What This Document Is
This document presents a set of detailed example analyses related to Biostatistics 411: Analysis of Correlated Data at UCLA. It’s designed as a companion resource to illustrate practical applications of statistical modeling techniques for correlated data structures. The material focuses on applying these methods to real-world scenarios, offering a deeper understanding of how to approach complex datasets. It features two distinct case studies, providing varied perspectives on data analysis challenges.
Why This Document Matters
Students enrolled in correlated data analysis courses, or those preparing for related statistical applications, will find this resource particularly valuable. It’s ideal for reinforcing concepts learned in lectures and textbooks by demonstrating how those concepts translate into concrete analytical workflows. Researchers and practitioners seeking to refine their skills in longitudinal data analysis or repeated measures modeling will also benefit from studying these examples. This resource is best utilized *after* foundational knowledge of correlated data models has been established.
Topics Covered
* Modeling repeated measures data with covariance structures
* Fixed effects modeling and interpretation in correlated data
* Designing statistical inferences for complex group comparisons
* Analyzing treatment effects over time
* Applications of statistical modeling to public health and medical research
* Considerations for model selection and variable inclusion
* Understanding differences of differences in longitudinal studies
What This Document Provides
* Two complete example analyses, each exploring a unique dataset.
* Detailed descriptions of the research context and data collection methods for each case study.
* Overviews of the statistical models employed, including the rationale behind model choices.
* Discussions of the key research questions addressed in each analysis.
* References to supporting materials, such as SAS output and presentation-ready tables.
* Insights into practical considerations for conducting and interpreting correlated data analyses.
