Targeted use of growth mixture modeling: a learning perspective.
From the statistical learning perspective, this paper shows a new direction for the use of growth mixture modeling (GMM), a method of identifying latent subpopulations that manifest heterogeneous outcome trajectories. In the proposed approach, we utilize the benefits of the conventional use of GMM for the purpose of generating potential candidate models based on empirical model fitting, which can be viewed as unsupervised learning. We then evaluate candidate GMM models on the basis of a direct measure of success; how well the trajectory types are predicted by clinically and demographically relevant baseline features, which can be viewed as supervised learning. We examine the proposed approach focusing on a particular utility of latent trajectory classes, as outcomes that can be used as valid prediction targets in clinical prognostic models. Our approach is illustrated using data from the Longitudinal Assessment of Manic Symptoms study. Copyright © 2016 John Wiley & Sons, Ltd.
Stanford University, Stanford, CA, U.S.A.
Booil
B
Jo
Johns Hopkins University, Baltimore, MD, U.S.A.
Robert L
RL
Findling
University of Texas Health Science Center, San Antonio, TX, U.S.A.
Chen-Pin
CP
Wang
Stanford University, Stanford, CA, U.S.A.
Trevor J
TJ
Hastie
University of North Carolina, Chapel Hill, NC, U.S.A.
Eric A
EA
Youngstrom
Ohio State University, Columbus, OH, U.S.A.
L Eugene
LE
Arnold
Ohio State University, Columbus, OH, U.S.A.
Mary A
MA
Fristad
New York University, New York, NY, U.S.A.
Sarah McCue
SM
Horwitz
D001714:Bipolar Disorder / Q000175:diagnosis; D006801:Humans; D008137:Longitudinal Studies; D000069550:Machine Learning; D015233:Models, Statistical; D063868:Patient Outcome Assessment; D011379:Prognosis; D015203:Reproducibility of Results; D000069553:Supervised Machine Learning
early prediction; growth mixture modeling; latent trajectory class; sensitivity; specificity; supervised learning; unsupervised learning