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Hierarchical dependency models for multivariate survival data with censoring.

S Gross¹, C Huber

¹City University of New York, Baruch College, 17 Lexington av., New York 10010, USA. Shulamith_Gross@baruch.cuny.edu

Lifetime Data Analysis

|February 24, 2001

Summary

This study introduces partial likelihood logistic models for discrete-time, censored clustered survival data. The models account for within-cluster dependencies while assuming independence between clusters, applicable to varying cluster sizes.

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Area of Science:

Biostatistics
Survival Analysis
Statistical Modeling

Background:

Clustered survival data presents unique analytical challenges due to potential dependencies within clusters.
Discrete time reporting and censoring are common in longitudinal and observational studies.

Purpose of the Study:

To propose a flexible family of partial likelihood logistic models for analyzing discrete-time clustered survival data.
To accommodate potential dependence structures within clusters while maintaining cluster independence.
To address scenarios with both fixed and variable cluster sizes.

Main Methods:

Development of partial likelihood logistic regression models tailored for discrete time intervals.
Modeling of intra-cluster correlation using logistic regression framework.

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Asymptotic analysis for large numbers of small, independent clusters.

Main Results:

The proposed models effectively handle within-cluster dependence in discrete-time survival data.
The methodology is robust for both identically sized and varying-sized clusters.
Asymptotic properties are established for a large number of small clusters.

Conclusions:

The developed partial likelihood logistic models offer a robust statistical framework for clustered survival data analysis.
This approach provides valuable tools for researchers dealing with complex dependencies in discrete time survival outcomes.
The models are applicable across various scientific fields employing clustered survival data analysis.