title: Identifying latent clusters of variability in longitudinal data.
abstract: Means or other central tendency measures are by far the most common focus of statistical analyses. However, as Carroll (2003) noted, "systematic dependence of variability on known factors" may be "fundamental to the proper solution of scientific problems" in certain settings. We develop a latent cluster model that relates underlying "clusters" of variability to baseline or outcome measures of interest. Because estimation of variability is inextricably linked to estimation of trend, assumptions about underlying trends are minimized by using nonparametric regression estimates. The resulting residual errors are then clustered into unobserved clusters of variability that are in turn related to subject-level predictors of interest. An application is made to psychological affect data.
authors.affiliation: Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, USA. mrelliot@umich.edu
authors.forename: Michael R
authors.initials: MR
authors.lastname: Elliott
mesh_terms: D000339:Affect; D000465:Algorithms; D000704:Analysis of Variance; D001699:Biometry; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D009203:Myocardial Infarction / Q000523:psychology; D018709:Statistics, Nonparametric