title: Predicting pregnancy outcomes using longitudinal information: a penalized splines mixed-effects model approach.
abstract: We propose a semiparametric nonlinear mixed-effects model (SNMM) using penalized splines to classify longitudinal data and improve the prediction of a binary outcome. The work is motivated by a study in which different hormone levels were measured during the early stages of pregnancy, and the challenge is using this information to predict normal versus abnormal pregnancy outcomes. The aim of this paper is to compare models and estimation strategies on the basis of alternative formulations of SNMMs depending on the characteristics of the data set under consideration. For our motivating example, we address the classification problem using a particular case of the SNMM in which the parameter space has a finite dimensional component (fixed effects and variance components) and an infinite dimensional component (unknown function) that need to be estimated. The nonparametric component of the model is estimated using penalized splines. For the parametric component, we compare the advantages of using random effects versus direct modeling of the correlation structure of the errors. Numerical studies show that our approach improves over other existing methods for the analysis of this type of data. Furthermore, the results obtained using our method support the idea that explicit modeling of the serial correlation of the error term improves the prediction accuracy with respect to a model with random effects, but independent errors. Copyright © 2017 John Wiley & Sons, Ltd.
authors.affiliation: Instituto de Estadística, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
authors.forename: Rolando
authors.identifier: 0000-0002-1415-1809
authors.initials: R
authors.lastname: De la Cruz
authors.affiliation: Department of Statistics, Oregon State University, Corvallis, OR, U.S.A.
authors.forename: Claudio
authors.initials: C
authors.lastname: Fuentes
authors.affiliation: CIMFAV - Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile.
authors.forename: Cristian
authors.initials: C
authors.lastname: Meza
authors.affiliation: BCAM - Basque Centre for Applied Mathematics, Bilbao, Basque Country, Spain.
authors.forename: Dae-Jin
authors.initials: DJ
authors.lastname: Lee
authors.affiliation: Departamento de Estadística, Universidad Carlos III de Madrid, Getafe, Spain.
authors.forename: Ana
authors.initials: A
authors.lastname: Arribas-Gil
mesh_terms: D003627:Data Interpretation, Statistical; D005260:Female; D001556:Hexachlorocyclohexane / Q000097:blood; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D011247:Pregnancy / Q000097:blood; D011256:Pregnancy Outcome / Q000453:epidemiology*; D011264:Pregnancy Trimesters / Q000097:blood
chemical_list: D001556:Hexachlorocyclohexane; C023888:beta-hexachlorocyclohexane
keywords: P-splines; classification models; correlated observations; longitudinal data; mixed-effects models