Clustering Individuals Based on Similarity in Idiographic Factor Loading Patterns.

Idiographic measurement models such as p-technique and dynamic factor analysis (DFA) assess latent constructs at the individual level. These person-specific methods may provide more accurate models than models obtained from aggregated data when individuals are heterogeneous in their processes. Developing clustering methods for the grouping of individuals with similar measurement models would enable researchers to identify if measurement model subtypes exist across individuals as well as assess if the different models correspond to the same latent concept or not.

Health-related quality of life profiles in adolescents and young adults with chronic conditions.

Purpose: To assess health-related quality of life (HRQOL) among adolescents and young adults (AYAs) with chronic conditions.

Methods: AYAs (N = 872) aged 14-20 years completed NIH's Patient-Reported Outcomes Measurement Information System (PROMIS) measures of physical function, pain interference, fatigue, social health, depression, anxiety, and anger. Latent profile analysis (LPA) was used to group AYAs into HRQOL profiles using PROMIS T-scores.

Applying latent profile analysis to identify adolescents and young adults with chronic conditions at risk for poor health-related quality of life.

The impact of chronic diseases on health-related quality of life (HRQOL) in adolescents and young adults (AYAs) is understudied. Latent profile analysis (LPA) can identify profiles of AYAs based on their HRQOL scores reflecting physical, mental, and social well-being. This paper will (1) demonstrate how to use LPA to identify profiles of AYAs based on their scores on multiple HRQOL indicators; (2) explore associations of demographic and clinical factors with LPA-identified HRQOL profiles of AYAs; and (3) provide guidance on the selection of adult or pediatric versions of Patient-Reported Outcomes Measurement Information System® (PROMIS®) in AYAs.