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Discriminant analysis using a multivariate linear mixed model with a normal mixture in the random effects

Arnošt Komárek¹, Bettina E Hansen, Edith M M Kuiper

¹Faculty of Mathematics and Physics, Department of Probability and Mathematical Statistics, Charles University in Prague, Sokolovská 83, 186 75 Praha 8-Karlín, Czech Republic. arnost.komarek@mff.cuni.cz

Statistics in Medicine

|December 21, 2010

Summary

This summary is machine-generated.

This study introduces a new method for classifying patients into prognostic groups using longitudinal marker data. The approach enhances existing models by relaxing normality assumptions for improved prognostic classification.

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Area of Science:

Biostatistics
Medical Prognostics
Longitudinal Data Analysis

Background:

Accurate prognostic classification is crucial for patient management and treatment strategies.
Existing methods often rely on restrictive assumptions, such as the normality of random effects in mixed models.
Longitudinal marker data offers valuable insights into disease progression and patient outcomes.

Purpose of the Study:

To develop and validate a novel statistical method for classifying subjects into distinct prognostic groups using longitudinal marker data.
To improve upon existing prognostic classification techniques by relaxing the normality assumption for random effects in multivariate linear mixed models.
To provide freely available software for implementing the proposed methodology.

Main Methods:

The method involves fitting multivariate linear mixed models to longitudinal marker data within known prognostic groups from a training dataset.
A discrimination rule is developed for future subjects based on the fitted mixed models.
A key innovation is the assumption of a heteroscedastic multivariate normal mixture for random effects, relaxing traditional normality assumptions.
Inference is conducted using Bayesian methods and Markov chain Monte Carlo (MCMC) simulations.

Main Results:

The developed method successfully classifies subjects into prognostic groups based on longitudinal marker data.
Relaxing the normality assumption for random effects leads to improved model flexibility and potentially more accurate classifications.
The application to the Dutch Primary Biliary Cirrhosis Study demonstrates the practical utility of the method.

Conclusions:

The proposed method offers a robust and flexible approach to longitudinal prognostic classification.
The relaxation of normality assumptions in mixed models enhances the ability to model complex data structures.
The freely available software facilitates the adoption and application of this advanced statistical technique in clinical research.