Semiparametric Mixed Models for Medical Monitoring Data: An Overview.

R D Szczesniak,S A Raouf,D Li

doi:10.4172/2155-6180.1000234

Abstract

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.

Highlights

Medical monitoring plays a major role in modern clinical studies, ranging from portable actigraphy devices worn by individual subjects [1] to at-home reporting of biologic markers [2]
We focus on the application of a particular type of semiparametric regressions, known as semiparametric mixed models, to medical monitoring data and interpretations that can be made using these models when it is of interest to detect rapid changes in the mean response function and compare experimental groups
We have reviewed work demonstrating that semiparametric mixed modeling within the linear mixed model (LMM) framework is a flexible method for modeling data arising from medical monitoring

Summary

Introduction

Medical monitoring plays a major role in modern clinical studies, ranging from portable actigraphy devices worn by individual subjects [1] to at-home reporting of biologic markers [2]. Developments focused on smoothing spline analysis of variance models for longitudinal data [40, 41] and mixed effects formulations with nonparametric model fitting [42]. We can augment existing results [7,29] to construct simultaneous confidence bands of differences between group-specific smooth functions fl(t) and fl′(t) where (l ≠ l′) under the aforementioned semiparametric mixed model in Equation (5) and its covariance formulation

Results

Conclusion