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Reuben Adatorwovor, PhD Assistant Professor of Biostatistics University of Kentucky

Title: Modeling Bivariate Survival with Dependent Censoring: A Copula-Based Approach in Prostate Cancer Research

Abstract: Independent censoring is a common assumption in time-to-event data analysis, but it is often untestable and may be questionable, when there is disproportionate loss to follow-up. This paper introduces a copula-based likelihood method for analyzing bivariate survival data with dependent censoring. We employ a flexible Joe-Hu copula to model the dependence between the quadruple, two event and two censoring times, under a B-spline and a Cox proportional hazards framework. Our estimator is shown to be consistent and possesses favorable asymptotic properties under regular conditions. We demonstrate the utility of our approach through extensive simulations and application to twin prostate cancer dataset.

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