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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Semiparametric modeling and analysis of longitudinal method comparison data.

Lasitha N Rathnayake¹, Pankaj K Choudhary¹

¹Department of Mathematical Sciences, University of Texas at Dallas, FO 35, Richardson, 75083-0688, TX, U.S.A.

Statistics in Medicine

|February 20, 2017

Summary

This study introduces a new statistical method for analyzing longitudinal data to measure agreement between continuous measurement methods. The approach effectively handles dependent observations and performs well with 30+ subjects.

Keywords:

agreement concordance correlation longitudinal data mixed-effects model multiple outcomes data penalized splines total deviation index

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Area of Science:

Biostatistics
Biomedical Engineering
Health Sciences

Background:

Method comparison studies are crucial in biomedical fields for assessing agreement between different measurement techniques.
Longitudinal data, involving repeated measurements over time for the same subjects, present unique challenges due to dependent observations.
Existing methods may not adequately capture the complex dependencies inherent in longitudinal method comparison data.

Purpose of the Study:

To propose a novel nonparametric approach for modeling longitudinal method comparison data.
To accurately assess agreement between multiple measurement methods using mixed-effects models.
To provide a flexible framework for analyzing correlated within-subject errors in method comparison studies.

Main Methods:

Utilizing penalized regression splines within a mixed-effects modeling framework to capture trajectory patterns.
Incorporating random effects for subjects and their interactions to account for within-subject dependence.
Employing maximum likelihood estimation for model fitting and performing inference on agreement measures like the concordance correlation coefficient and total deviation index.

Main Results:

The proposed methodology demonstrates robust performance in simulations with 30 or more subjects.
The model effectively captures the dependence structure in longitudinal data from multiple measurement methods.
The approach was successfully applied to analyze percentage body fat measurements, illustrating its practical utility.

Conclusions:

The developed mixed-effects model using penalized splines offers a powerful tool for analyzing longitudinal method comparison data.
This method provides reliable estimation of agreement measures, crucial for validating new measurement techniques.
The findings support the application of this methodology in various biomedical disciplines requiring rigorous method comparison.