The BivarIntCensored: An R Package for Nonparametric Inference of Bivariate Interval-Censored Data

Bivariate event time data are constantly encountered in biomedical research. In many real-life applications, both event times are possibly subject to interval censoring that gives rise to bivariate interval-censored data. Nonparametric inference of bivariate interval-censored data focuses on estimation of the joint distribution function of event times or the joint survival function. The conventional nonparametric maximum likelihood estimator suffers non-uniqueness of the estimates as well as computation inefficiency due to searching for maximal intersections and high-dimensional convex programming. A spline-based sieve nonparametric maximum likelihood estimator for the joint cumulative distribution function with bivariate interval-censored data has been developed to resolve the non-uniqueness issue in estimation. Numerically, it leads to a convex programming with complicated linear constraints but much reduced number of unknown parameters. In this project, we will illustrate the key characteristics of the sieve nonparametric maximum likelihood estimation with emphasis on numerical computation. We will develop an R-based software to facilitate the public use for computing the spline-based sieve nonparametric maximum likelihood estimator of the joint cumulative distribution function.

Duke Scholars

Author Yuan Wu Biostatistics & Bioinformatics, Division of Biostatistics