DeepAI AI Chat
Log In Sign Up

Copula-based semiparametric transformation model for bivariate data under general interval censoring

by   Tao Sun, et al.
University of Pittsburgh

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 (i.e., current status data), case 2 interval censoring, and right 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 two-step 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 estimation and testing 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 (AREDS) data, to successfully identify novel risk variants associated with the disease progression. We also produce the predicted joint and conditional progression-free probabilities, for patients with different genetic characteristics.


Gene-based Association Analysis for Bivariate Time-to-event Data through Functional Regression with Copula Models

Several gene-based association tests for time-to-event traits have been ...

Maximum Likelihood Estimation for Semiparametric Regression Models with Interval-Censored Multi-State Data

Interval-censored multi-state data arise in many studies of chronic dise...

Joint Modeling of An Outcome Variable and Integrated Omic Datasets Using GLM-PO2PLS

In many studies of human diseases, multiple omic datasets are measured. ...

Robust parametric modeling of Alzheimer's disease progression

Quantitative characterization of disease progression using longitudinal ...