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

Abstract

This research is motivated by discovering and underpinning genetic causes for the progression of a bilateral eye disease, age-related macular degeneration (AMD), of which the primary outcomes, progression times to late-AMD, are bivariate and interval-censored due to intermittent assessment times. We propose a novel class of copula-based semiparametric transformation models for bivariate data under general interval censoring, which includes the case 1 interval censoring (current status data) and case 2 interval censoring. Specifically, the joint likelihood is modeled through a two-parameter Archimedean copula, which can flexibly characterize the dependence between the two margins in both tails. The marginal distributions are modeled through semiparametric transformation models using sieves, with the proportional hazards or odds model being a special case. We develop a computationally efficient sieve maximum likelihood estimation procedure for the unknown parameters, together with a generalized score test for the regression parameter(s). For the proposed sieve estimators of finite-dimensional parameters, we establish their asymptotic normality and efficiency. Extensive simulations are conducted to evaluate the performance of the proposed method in finite samples. Finally, we apply our method to a genome-wide analysis of AMD progression using the Age-Related Eye Disease Study data, to successfully identify novel risk variants associated with the disease progression. We also produce predicted joint and conditional progression-free probabilities, for patients with different genetic characteristics.

Authors+Show Affiliations

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

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31506682

Citation

Sun, Tao, and Ying Ding. "Copula-based Semiparametric Regression Method for Bivariate Data Under General Interval Censoring." Biostatistics (Oxford, England), 2019.
Sun T, Ding Y. Copula-based semiparametric regression method for bivariate data under general interval censoring. Biostatistics. 2019.
Sun, T., & Ding, Y. (2019). Copula-based semiparametric regression method for bivariate data under general interval censoring. Biostatistics (Oxford, England), doi:10.1093/biostatistics/kxz032.
Sun T, Ding Y. Copula-based Semiparametric Regression Method for Bivariate Data Under General Interval Censoring. Biostatistics. 2019 Sep 11; PubMed PMID: 31506682.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Copula-based semiparametric regression method for bivariate data under general interval censoring. AU - Sun,Tao, AU - Ding,Ying, Y1 - 2019/09/11/ PY - 2019/05/24/received PY - 2019/08/09/revised PY - 2019/08/11/accepted PY - 2019/9/12/entrez KW - Bivariate KW - Copula KW - GWAS KW - Interval-censored KW - Semiparametric KW - Sieve JF - Biostatistics (Oxford, England) JO - Biostatistics N2 - This research is motivated by discovering and underpinning genetic causes for the progression of a bilateral eye disease, age-related macular degeneration (AMD), of which the primary outcomes, progression times to late-AMD, are bivariate and interval-censored due to intermittent assessment times. We propose a novel class of copula-based semiparametric transformation models for bivariate data under general interval censoring, which includes the case 1 interval censoring (current status data) and case 2 interval censoring. Specifically, the joint likelihood is modeled through a two-parameter Archimedean copula, which can flexibly characterize the dependence between the two margins in both tails. The marginal distributions are modeled through semiparametric transformation models using sieves, with the proportional hazards or odds model being a special case. We develop a computationally efficient sieve maximum likelihood estimation procedure for the unknown parameters, together with a generalized score test for the regression parameter(s). For the proposed sieve estimators of finite-dimensional parameters, we establish their asymptotic normality and efficiency. Extensive simulations are conducted to evaluate the performance of the proposed method in finite samples. Finally, we apply our method to a genome-wide analysis of AMD progression using the Age-Related Eye Disease Study data, to successfully identify novel risk variants associated with the disease progression. We also produce predicted joint and conditional progression-free probabilities, for patients with different genetic characteristics. SN - 1468-4357 UR - https://www.unboundmedicine.com/medline/citation/31506682/Copula_based_semiparametric_regression_method_for_bivariate_data_under_general_interval_censoring_ L2 - https://academic.oup.com/biostatistics/article-lookup/doi/10.1093/biostatistics/kxz032 DB - PRIME DP - Unbound Medicine ER -
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