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A novel approach to latent class modelling: identifying the various types of body mass index individuals
Author(s) -
Brown Sarah,
Greene William,
Harris Mark
Publication year - 2020
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12552
Subject(s) - latent class model , class (philosophy) , ranking (information retrieval) , representation (politics) , latent variable , econometrics , regression analysis , outcome (game theory) , variable (mathematics) , regression , range (aeronautics) , body mass index , statistics , latent variable model , mathematics , computer science , artificial intelligence , medicine , mathematical economics , mathematical analysis , pathology , politics , political science , law , composite material , materials science
Summary Given the increasing prevalence of adult obesity, furthering understanding of the determinants of measures such as the body mass index ( BMI ) remains high on the policy agenda. We contribute to existing literature on modelling the BMI by proposing an extension to latent class modelling, which serves to unveil a more detailed picture of the determinants of BMI. Interest here lies in latent class analysis with a regression model and predictor variables explaining class membership, a regression model and predictor variables explaining the outcome variable within BMI classes and instances where the BMI classes are naturally ordered and labelled by expected values within class. A simple and generic way of parameterizing both the class probabilities and the statistical representation of behaviours within each class is proposed, that simultaneously preserves the ranking according to class‐specific expected values and yields a parsimonious representation of the class probabilities. Based on a wide range of metrics, the newly proposed approach is found to dominate the prevailing approach and, moreover, results are often quite different across the two.