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Copula-based semiparametric regression method for bivariate data under general interval censoring.

Tao Sun1, Ying Ding1

  • 1Department of Biostatistics, University of Pittsburgh, 130 DeSoto St, Pittsburgh, PA 15261, USA.

Biostatistics (Oxford, England)
|September 12, 2019
PubMed
Summary
This summary is machine-generated.

This study identifies genetic factors influencing age-related macular degeneration (AMD) progression. The novel statistical model effectively analyzes complex, interval-censored data to uncover new disease risk variants.

Keywords:
BivariateCopulaGWASInterval-censoredSemiparametricSieve

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Area of Science:

  • Biostatistics
  • Ophthalmology
  • Genetics

Background:

  • Age-related macular degeneration (AMD) is a bilateral eye disease with complex genetic underpinnings.
  • Progression to late AMD is often measured with bivariate, interval-censored data due to irregular patient assessments.

Purpose of the Study:

  • To develop a novel statistical framework for analyzing bivariate, interval-censored data in disease progression studies.
  • To identify genetic risk variants associated with the progression of age-related macular degeneration (AMD).

Main Methods:

  • Proposed a novel class of copula-based semiparametric transformation models for bivariate interval-censored data.
  • Utilized a two-parameter Archimedean copula for joint likelihood modeling and sieve-based semiparametric models for marginal distributions.
  • Developed a sieve maximum likelihood estimation procedure and a generalized score test for parameter estimation and inference.

Main Results:

  • The proposed statistical method demonstrated good performance in finite sample simulations.
  • Successfully identified novel genetic risk variants associated with AMD progression in a genome-wide analysis of the Age-Related Eye Disease Study data.
  • Generated predicted joint and conditional progression-free probabilities based on genetic profiles.

Conclusions:

  • The developed copula-based semiparametric models provide a powerful tool for analyzing complex bivariate, interval-censored data in disease progression.
  • This research successfully identified novel genetic variants contributing to AMD progression, advancing our understanding of the disease's genetic architecture.