Statistical Methods in Health Disparities Research

In this section, we provide slides for presentations and lectures on a variety of statistical methods in health disparities research. Most are introductory and a few are more advanced.

Methods of Analyzing Longitudinal Datasets

A gentle introduction to simplified forms of interrupted time series analysis

A basic introduction to mixed linear models.


A basic introduction to fitting linear growth models.


A basic introduction to the main modeling frameworks for fitting logistic models to clustered data. Several recent advancements, such as adaptive quadrature, have improved methods.

Addressing Missing Data

A no-math introduction to multiple imputation.

Factor Analysis Methods

An introduction to VARCLUS, a SAS procedure that we prefer to standard exploratory factor analysis with any type of rotation.

An overview of exploratory factor analysis.

A conceptual look at the logic underlying tests of measurement invariance within the confirmatory factor analysis (CFA) framework.


Testing Mediators and Moderators of Outcomes

Both of the following are introductory.

Power Analysis

A summary of the types of inputs required to conduct statistical power anlayses for common reserch designs. It should help PIs better understand the information they need to relay to a statistician when requesting power analyses.


Advanced Statistical Issues

Care must be taken when comparing odds ratios across two logistic regression models where the X variables in one model are a subset of the X variables in the other model.

Reviews options for 3-level logistic models available in SAS in 2013. SAS now includes the FASTQUAD option in PROC GLIMMIX, which allows fitting 3-level logistic mixed models with larger numbers of quadrature points.

Topics include smoothing longitudinal trends via linear and quadratic splines, using linear mixed models for smoothing spline regression, fitting simple interrupted times series models, and associative latent growth curve models.