학술논문
Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data.
Document Type
Academic Journal
Author
Ryoo JH; Educational Leadership, Foundations, and Policy, University of VirginiaCharlottesville, VA, United States.; Long JD; Department of Psychiatry, University of IowaIowa City, IA, United States.; Welch GW; Buffett Early Childhood Institute, University of NebraskaLincoln, NE, United States.; Reynolds A; Institute of Child Development, University of MinnesotaMinneapolis, MN, United States.; Swearer SM; Department of Educational Psychology, University of NebraskaLincoln, NE, United States.
Source
Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101550902 Publication Model: eCollection Cited Medium: Print ISSN: 1664-1078 (Print) Linking ISSN: 16641078 NLM ISO Abbreviation: Front Psychol Subsets: PubMed not MEDLINE
Subject
Language
English
ISSN
1664-1078
Abstract
As in cross sectional studies, longitudinal studies involve non-Gaussian data such as binomial, Poisson, gamma, and inverse-Gaussian distributions, and multivariate exponential families. A number of statistical tools have thus been developed to deal with non-Gaussian longitudinal data, including analytic techniques to estimate parameters in both fixed and random effects models. However, as yet growth modeling with non-Gaussian data is somewhat limited when considering the transformed expectation of the response via a linear predictor as a functional form of explanatory variables. In this study, we introduce a fractional polynomial model (FPM) that can be applied to model non-linear growth with non-Gaussian longitudinal data and demonstrate its use by fitting two empirical binary and count data models. The results clearly show the efficiency and flexibility of the FPM for such applications.