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Latent factor regression models for grouped outcomes
Author(s) -
Woodard D. B.,
Love T. M. T.,
Thurston S. W.,
Ruppert D.,
Sathyanarayana S.,
Swan S. H.
Publication year - 2013
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12037
Subject(s) - factor analysis , regression analysis , econometrics , random effects model , flexibility (engineering) , latent variable , set (abstract data type) , statistics , factor regression model , nested set model , regression , computer science , mathematics , data mining , proper linear model , polynomial regression , medicine , meta analysis , relational database , programming language
Summary We consider regression models for multiple correlated outcomes, where the outcomes are nested in domains. We show that random effect models for this nested situation fit into a standard factor model framework, which leads us to view the modeling options as a spectrum between parsimonious random effect multiple outcomes models and more general continuous latent factor models. We introduce a set of identifiable models along this spectrum that extend an existing random effect model for multiple outcomes nested in domains. We characterize the tradeoffs between parsimony and flexibility in this set of models, applying them to both simulated data and data relating sexually dimorphic traits in male infants to explanatory variables.