Detecting pulsatile hormone secretions using nonlinear mixed effects partial spline models.
Neuroendocrine ensembles communicate with their remote and proximal target cells via an intermittent pattern of chemical signaling. The identification of episodic releases of hormonal pulse signals constitutes a major emphasis of endocrine investigation. Estimating the number, temporal locations, secretion rate, and elimination rate from hormone concentration measurements is of critical importance in endocrinology. In this article, we propose a new flexible statistical method for pulse detection based on nonlinear mixed effects partial spline models. We model pulsatile secretions using biophysical models and investigate biological variation between pulses using random effects. Pooling information from different pulses provides more efficient and stable estimation for parameters of interest. We combine all nuisance parameters including a nonconstant basal secretion rate and biological variations into a baseline function that is modeled nonparametrically using smoothing splines. We develop model selection and parameter estimation methods for the general nonlinear mixed effects partial spline models and an R package for pulse detection and estimation. We evaluate performance and the benefit of shrinkage by simulations and apply our methods to data from a medical experiment.
Department of Statistics, National Taichung Institute of Technology, Taichung, Taiwan. yuchieh@ntit.edu
Yu-Chieh
YC
Yang
Anna
A
Liu
Yuedong
Y
Wang
D000818:Animals; D001699:Biometry / Q000379:methods; D006728:Hormones / Q000032:analysis* / Q000378:metabolism; D006801:Humans; D011673:Pulsatile Flow; D018709:Statistics, Nonparametric
D006728:Hormones