Journal article
Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data
Frontiers in psychology, Vol.8, pp.1431-1431
08/22/2017
DOI: 10.3389/fpsyg.2017.01431
PMCID: PMC5572294
PMID: 28878723
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.
Details
- Title: Subtitle
- Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data
- Creators
- Ji Hoon Ryoo - University of VirginiaJeffrey D. Long - University of IowaGreg W. Welch - University of Nebraska–LincolnArthur Reynolds - University of MinnesotaSusan M. Swearer - Department of Educational Psychology, University of NebraskaLincoln, NE, United States.
- Resource Type
- Journal article
- Publication Details
- Frontiers in psychology, Vol.8, pp.1431-1431
- DOI
- 10.3389/fpsyg.2017.01431
- PMID
- 28878723
- PMCID
- PMC5572294
- NLM abbreviation
- Front Psychol
- ISSN
- 1664-1078
- eISSN
- 1664-1078
- Publisher
- Frontiers Media Sa
- Number of pages
- 11
- Language
- English
- Date published
- 08/22/2017
- Academic Unit
- Psychiatry; Biostatistics
- Record Identifier
- 9984280874602771
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