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Modeling Individual Patient Count/Rate Data over Time with Applications to Cancer Pain Flares and Cancer Pain

George J Knafl¹, Salimah H Meghani²

¹School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Open Journal of Statistics

|August 8, 2022

Summary

This study introduces two advanced statistical methods for analyzing patient count data over time, accounting for complex correlations and varying data spread. Extended linear mixed modeling proved more effective and efficient for this type of data.

Keywords:

Adaptive Regression Extended Linear Mixed Modeling Generalized Estimating Equations Likelihood-Like Cross-Validation Poisson Regression

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Area of Science:

Biostatistics
Statistical Modeling
Longitudinal Data Analysis

Background:

Analyzing individual patient count/rate data over time presents challenges due to temporal correlations and non-constant dispersions.
Existing methods may require extensive computational time for model selection.

Purpose of the Study:

To investigate and compare two extended generalized estimating equations (GEE) modeling approaches for individual patient count/rate data.
To develop methods that efficiently search for optimal models while accounting for temporal dynamics and dispersion.
To evaluate model performance using a likelihood-like cross-validation (LCV) score.

Main Methods:

Two extensions of generalized estimating equations (GEE) were formulated using a multivariate normal density-based likelihood-like function.
The first approach augments GEE with dispersion parameter estimation equations.
The second approach extends linear mixed modeling by deriving estimating equations from the partial derivatives of the likelihood-like function.
Three correlation structures (independent, exchangeable, spatial autoregressive) were considered.
Adaptive regression modeling and LCV scores were used for model selection.

Main Results:

Extended linear mixed modeling demonstrated superior performance in example analyses of cancer patient count/rate data.
This approach yielded better LCV scores and/or more parsimonious models compared to the augmented GEE approach.
The extended linear mixed modeling approach required substantially less computation time.

Conclusions:

Extended linear mixed modeling is a preferable method for analyzing individual patient count/rate data over time.
This approach effectively handles temporal correlations and non-constant dispersions while allowing for efficient model selection.
The findings support the use of extended linear mixed modeling for complex longitudinal count data in clinical research.