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, older (2001) introduction to some frameworks for fitting logistic models to clustered data. More recent advancements have improved some estimation options:

Includes smoothing splines, interrupted times series, and associative latent growth curve models.  SAS PROC GLIMMIX now offers options to simply specification of smoothing splines. 

Factor Analysis Methods

 

An introduction to VARCLUS, a SAS procedure that we (mostly) prefer to exploratory factor analysis.

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.

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

A summary of the types of inputs required to conduct statistical power analyses for common research designs. 

A no-math introduction to multiple imputation.