Semiparametric Mixed Models for Medical Monitoring Data: An Overview

Journal of Biometrics & Biostatistics

article

SzczesniakRD¹  LiD³  RaoufSA² 

[1] Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center, Cincinnati;Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati;Department of Mathematical Sciences, University of Cincinnati, Cincinnati

关键词: Ambulatory blood pressure;    Covariance models;    Linear mixed models;    Longitudinal data;    Functional data analysis;    Nonparametric regression;    Obstructive sleep apnea;    Penalized splines;    Semiparametric regression;    Serial correlation;   

DOI  :  10.4172/2155-6180.1000234

来源: Hilaris Publisher

【 摘 要 】

The potential to characterize nonlinear progression over time is now possible in many health conditions due to advancements in medical monitoring and more frequent data collection. It is often of interest to investigate differences between experimental groups in a study or identify the onset of rapid changes in the response of interest using medical monitoring data; however, analytic challenges emerge. We review semiparametric mixed-modeling extensions that accommodate medical monitoring data. Throughout the review, we illustrate these extensions to the semiparametric mixed-model framework with an application to prospective clinical data obtained from 24-hour ambulatory blood pressure monitoring, where it is of interest to compare blood pressure patterns from children with obstructive sleep apnea to those arising from healthy controls.

【 授权许可】

Unknown