Targeted use of growth mixture modeling: a learning perspective
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- 作者：Booil Jo ; Robert L. Findling ; Chen-Pin Wang ; Trevor J. Hastie ; Eric A. Youngstrom ; L. Eugene Arnold ; Mary A. Fristad and Sarah McCue Horwitz
- 刊名：Statistics in Medicine
- 出版年：2017
- 出版时间：20 February 2017
- 年：2017
- 卷：36
- 期：4
- 页码：671-686
- 全文大小：606K
- ISSN：1097-0258
文摘
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