This presentation introduces latent class analysis (LCA) and latent profile analysis (LPA), person-centered statistical
methods used to identify subgroups within a larger population based on shared characteristics. The presentation contrasts
these techniques with traditional cluster analysis, highlighting LCA/LPA's advantages, such as incorporating covariates
and providing model fit evaluations. A practical walkthrough using traumatic brain injury (TBI) data is described,
demonstrating how to apply LCA/LPA, from data preparation to result interpretation. Several research studies utilizing
LCA/LPA to analyze TBI are cited as examples. The presentation concludes by emphasizing the importance of considering
data quality and the clinical relevance of identified subgroups.
Resources