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Related Experiment Video

Updated: Jun 30, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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Published on: December 9, 2015

A new serially correlated gamma-frailty process for longitudinal count data.

M Fiocco¹, H Putter, J C Van Houwelingen

¹Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Postzone S-05-P, PO Box 9600, 2300 RC Leiden, The Netherlands. m.fiocco@lumc.nl

Biostatistics (Oxford, England)

|September 18, 2008

Summary

A novel multivariate gamma distribution aids Poisson-correlated gamma-frailty models for longitudinal count data. This composite likelihood approach simplifies parameter estimation, enhancing analysis of complex dependencies.

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Area of Science:

Statistics
Biostatistics
Statistical Modeling

Background:

Longitudinal count data often exhibit between-subjects correlation.
High-dimensional dependencies in statistical models complicate likelihood-based inference.
Existing models may struggle to efficiently account for complex correlations in repeated measures.

Purpose of the Study:

Introduce a new multivariate gamma distribution.
Develop a Poisson-correlated gamma-frailty model for longitudinal count data.
Propose a computationally efficient estimation procedure using composite likelihoods.

Main Methods:

Described a new multivariate gamma distribution.
Formulated a Poisson-correlated gamma-frailty model.
Developed a 2-stage composite-likelihood procedure for parameter estimation.
Applied the method to a survival curve meta-analysis.

Main Results:

The multivariate gamma distribution integrates into the gamma-frailty model.
Composite likelihood significantly reduces computational complexity compared to full likelihood.
The 2-stage procedure provides a viable method for parameter estimation.
Demonstrated applicability in a real-world meta-analysis context.

Conclusions:

The proposed multivariate gamma distribution and frailty model effectively handle correlated longitudinal count data.
Composite likelihood methods offer a practical solution for complex statistical inference.
The 2-stage estimation procedure is efficient and applicable to survival data analysis.