Work

The estimation of branching curves in the presence of subject‐specific random effects

Year: 2014

Type: article

Abstract: Branching curves are a technique for modeling curves that change trajectory at a change (branching) point. Currently, the estimation framework is limited to independent data, and smoothing splines are used for estimation. This article aims to extend the branching curve framework to the longitudinal data setting where the branching point varies by subject. If the branching point is modeled as a random effect, then the longitudinal branching curve framework is a semiparametric nonlinear mixed effects model. Given existing issues with using random effects within a smoothing spline, we express the model as a B-spline based semiparametric nonlinear mixed effects model. Simple, clever smoothness constraints are enforced on the B-splines at the change point. The method is applied to Women's Health data where we model the shape of the labor curve (cervical dilation measured longitudinally) before and after treatment with oxytocin (a labor stimulant).

Source: Statistics in Medicine

DOI: 10.1002/sim.6289

Authors: Angelo Elmi, Sarah J. Ratcliffe, Wensheng Guo

Institutions: George Washington University, University of Pennsylvania

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases

Awards: G2613511925, G3226733046, G9000025875