Fitting the fractional polynomial model to non-gaussian longitudinal data

Ji Hoon Ryoo, Jeffrey D. Long, Greg W. Welch, Arthur Reynolds, Susan M. Swearer

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Article number1431
JournalFrontiers in Psychology
Issue numberAUG
Publication statusPublished - 2017 Aug 22

Bibliographical note

Publisher Copyright:
© 2017 Ryoo, Long, Welch, Reynolds and Swearer.

All Science Journal Classification (ASJC) codes

  • Psychology(all)


Dive into the research topics of 'Fitting the fractional polynomial model to non-gaussian longitudinal data'. Together they form a unique fingerprint.

Cite this