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Estimating twin concordance for bivariate competing risks twin data.

Thomas H Scheike1, Klaus K Holst, Jacob B Hjelmborg

  • 1Department of Biostatistics, University of Copenhagen, Denmark.

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|October 18, 2013
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Summary
This summary is machine-generated.

This study introduces a method to estimate twin concordance, quantifying the probability of both twins experiencing an event. The findings reveal a significant genetic component in familial influence on disease.

Keywords:
casewise concordanceconcordance functioncumulative incidence probabilitymultivariate competing riskstwins

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Genetics

Background:

  • Twin studies are crucial for understanding disease etiology.
  • Quantifying within-pair dependence is essential for assessing genetic and environmental influences.
  • Existing methods may not fully capture time-dependent concordance in twin data.

Purpose of the Study:

  • To develop and validate a novel method for estimating casewise concordance probability in twin time-to-event data.
  • To model the time-varying nature of within-pair dependence and the influence of covariates.
  • To infer familial and genetic influences on disease concordance.

Main Methods:

  • Utilizing time-to-event analysis techniques for twin data.
  • Developing estimators for concordance probability under right censoring assumptions.
  • Incorporating covariates to model marginal risk and dependence structures.
  • Establishing large sample properties of the proposed estimators.

Main Results:

  • Successfully estimated casewise twin concordance probability in the presence of right censoring.
  • Demonstrated the ability to model the magnitude of within-pair dependence over time.
  • Identified significant familial influence, suggesting a genetic component in disease concordance.
  • Applied the method to twin cancer data with competing risks.

Conclusions:

  • The proposed method effectively quantifies within-pair dependence in twin studies.
  • The findings highlight the importance of genetic factors in disease concordance.
  • This approach provides a robust framework for analyzing familial influences in time-to-event data.