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On cross-odds ratio for multivariate competing risks data

Thomas H Scheike1, Yanqing Sun

  • 1Department of Biostatistics, University of Copenhagen, Øster Farimagsgade 5, Copenhagen DK-1014, Denmark.

Biostatistics (Oxford, England)
|June 15, 2012
PubMed

Insights

This study introduces the cross-odds ratio to measure associations between correlated failure times within clusters. It presents methods for modeling this ratio, applied to twin data on menopause timing.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Correlated failure times within clusters, such as twins, require specialized statistical methods.
  • The cross-odds ratio is a measure of association for cause-specific events in clustered data.
  • Existing methods may not fully capture the complexities of time-to-event data in clustered settings.

Purpose of the Study:

  • To develop and explore parametric regression modeling for the cross-odds ratio.
  • To propose estimating equations for unknown parameters and investigate their asymptotic properties.
  • To discuss non-parametric estimation of the cross-odds ratio.

Main Methods:

  • Definition and theoretical exploration of the cross-odds ratio.
  • Development of parametric regression models for the cross-odds ratio.
  • Derivation of estimating equations and analysis of asymptotic properties.
  • Application to real-world data (Danish twin data).

Main Results:

  • The joint cumulative incidence function can be expressed using marginal functions and the cross-odds ratio.
  • Parametric regression modeling of the cross-odds ratio is feasible.
  • Estimating equations and their asymptotic properties are established.
  • Application to Danish twin data provides insights into menopause timing associations.

Conclusions:

  • The cross-odds ratio is a valuable tool for analyzing associations in clustered time-to-event data.
  • The proposed modeling approach allows for investigation of factors influencing these associations.
  • The Danish twin data analysis reveals patterns in menopause timing and zygosity differences.

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