A penalized spline approach to functional mixed effects model analysis.
Biometrics
; 67(3): 861-70, 2011 Sep.
Article
en En
| MEDLINE
| ID: mdl-21155747
ABSTRACT
In this article, we propose penalized spline (P-spline)-based methods for functional mixed effects models with varying coefficients. We decompose longitudinal outcomes as a sum of several terms a population mean function, covariates with time-varying coefficients, functional subject-specific random effects, and residual measurement error processes. Using P-splines, we propose nonparametric estimation of the population mean function, varying coefficient, random subject-specific curves, and the associated covariance function that represents between-subject variation and the variance function of the residual measurement errors which represents within-subject variation. Proposed methods offer flexible estimation of both the population- and subject-level curves. In addition, decomposing variability of the outcomes as a between- and within-subject source is useful in identifying the dominant variance component therefore optimally model a covariance function. We use a likelihood-based method to select multiple smoothing parameters. Furthermore, we study the asymptotics of the baseline P-spline estimator with longitudinal data. We conduct simulation studies to investigate performance of the proposed methods. The benefit of the between- and within-subject covariance decomposition is illustrated through an analysis of Berkeley growth data, where we identified clearly distinct patterns of the between- and within-subject covariance functions of children's heights. We also apply the proposed methods to estimate the effect of antihypertensive treatment from the Framingham Heart Study data.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
/
Biometría
Tipo de estudio:
Observational_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Año:
2011
Tipo del documento:
Article