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An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Nonparametric association analysis of exchangeable clustered competing risks data.

Yu Cheng¹, Jason P Fine, Michael R Kosorok

¹Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA. yucheng@pitt.edu

|June 14, 2008

Summary

This study addresses dementia onset clustering in families, developing new statistical methods for analyzing sibling data. The findings reveal age-dependent associations in dementia risk within sibships, crucial for understanding familial aggregation.

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Published on: October 23, 2020

Area of Science:

Epidemiology
Biostatistics
Gerontology

Background:

Population-based studies like the Cache County Study of Aging investigate familial aggregation of dementia.
Standard statistical methods may be inadequate for analyzing dementia onset in the presence of competing risks, such as death without dementia.
Independent censoring assumptions are often violated in familial studies.

Purpose of the Study:

To develop and validate novel nonparametric statistical methods for analyzing clustered, multivariate competing risks data.
To adapt existing estimators for bivariate cumulative hazard and incidence functions to handle exchangeable clustered data.
To evaluate time-dependent association measures for dementia onset within sibships.

Main Methods:

Adaptation of nonparametric estimators for bivariate cumulative functions to exchangeable clustered data.
Application of empirical process techniques for rigorous large sample inference.
Development of methods to address multivariate competing risks and potential violation of independent censoring.

Main Results:

The developed methodology effectively analyzes sibship associations in dementia onset.
Time-dependent association measures reveal significant age-related patterns in dementia clustering.
Simulations and application to the Cache County Study demonstrate practical utility.

Conclusions:

The new statistical approach appropriately handles complex data structures in aging and dementia research.
Dementia onset clustering among siblings is strongly influenced by age.
The methods provide valuable tools for understanding familial aggregation of age-related diseases.