Title |
Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data
|
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Published in |
Frontiers in Psychology, August 2017
|
DOI | 10.3389/fpsyg.2017.01431 |
Pubmed ID | |
Authors |
Ji Hoon Ryoo, Jeffrey D. Long, Greg W. Welch, Arthur Reynolds, Susan M. Swearer |
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. |
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