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Hierarchical functional data with mixed continuous and binary measurements.

Haocheng Li1, John Staudenmayer, Raymond J Carroll

  • 1Department of Statistics, Texas A&M University, 3143, TAMU, College Station, Texas, U.S.A.

Biometrics
|August 20, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new functional data method to analyze physical activity, simultaneously measuring continuous and binary outcomes over time. The approach models associations between these longitudinal variables using principal component analysis and mixed-effects models.

Keywords:
AccelerometryBinary longitudinal dataLongitudinal dataMixed-effects modelPenalized splinesPhysical activityPrincipal componentsSedentary behavior

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Analyzing longitudinal data with multiple outcome types (continuous and binary) presents statistical challenges.
  • Objective physical activity measurements often involve complex longitudinal patterns.

Purpose of the Study:

  • To develop a functional data approach for analyzing simultaneous continuous and binary longitudinal outcomes.
  • To model the association between these outcomes using principal component analysis of random effects.

Main Methods:

  • Utilized penalized splines for modeling mean and principal component curves.
  • Employed a mixed-effects model framework with a quasilikelihood approximation for the binary outcome.
  • Incorporated data-based transformations and principal component selection into the fitting algorithm.

Main Results:

  • The proposed method effectively models smooth curves and hierarchical correlations in longitudinal data.
  • The association between continuous and binary outcomes is successfully captured through principal component scores.
  • The method demonstrated good performance in a simulation study and application to physical activity data.

Conclusions:

  • The functional data approach provides a robust framework for analyzing complex longitudinal data with mixed outcome types.
  • This method offers valuable tools for understanding physical activity patterns and their associations.
  • The approach is extendable to other types of longitudinal outcomes.