Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data

Frontiers in Psychology

Ji Hoon Ryoo¹ 

关键词: fractional polynomial;    generalized additive model;    Non-Gaussian longitudinal data;    Chicago longitudinal study;    reading of the mind;   

DOI  :  10.3389/fpsyg.2017.01431

学科分类：心理学（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY