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Higher Dimensional Clayton-Oakes Models for Multivariate Failure Time Data.

R L Prentice1

  • 1Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M3-A410, Seattle, Washington, 98109, U.S.A.

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|October 15, 2016
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Summary
This summary is machine-generated.

This study extends the Clayton-Oakes bivariate failure time model to higher dimensions (m > 2). The new model accommodates unspecified marginal survivor functions, enhancing survival analysis for complex data.

Keywords:
Bivariate survivor functionClayton–Oakes modelCopulaCross ratioMultivariate survivor function

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Area of Science:

  • Statistics
  • Survival Analysis
  • Multivariate Data Analysis

Background:

  • The Clayton-Oakes model is a common tool for analyzing two related failure times.
  • Extending this model to more than two dimensions presents statistical challenges.
  • Existing methods often require fully specified marginal distributions, limiting applicability.

Purpose of the Study:

  • To generalize the Clayton-Oakes bivariate failure time model to m dimensions (m > 2).
  • To develop a flexible framework that allows for unspecified marginal survivor functions.
  • To describe special cases for analyzing dependencies in higher-dimensional survival data.

Main Methods:

  • Extension of the Clayton-Oakes model using copula-based approaches.
  • Development of a general framework for m-dimensional survival data.
  • Formulation of specific cases for q-dimensional marginals with higher-order dependencies.

Main Results:

  • A novel m-dimensional extension of the Clayton-Oakes model is proposed.
  • The model successfully incorporates unspecified marginal survivor functions for dimensions less than m.
  • Special cases are detailed, offering flexibility in modeling complex dependencies.

Conclusions:

  • The proposed extension provides a powerful and flexible tool for multivariate survival analysis.
  • This framework advances the analysis of time-to-event data in higher dimensions.
  • The model is applicable in various fields requiring the analysis of multiple, potentially dependent, failure times.