Copula link-based additive models for bivariate time-to-event outcomes with general censoring scheme

Abstract

Bivariate survival outcomes arise frequently in applied studies where the occurrence of two events of interest are associated. Often the exact event times are unknown due to censoring which can manifest in various forms. A general and flexible copula regression model that can handle bivariate survival data subject to various censoring mechanisms, which include a mixture of uncensored, left-, right-, and interval-censored data, is proposed. The proposal permits to specify all model parameters as flexible functions of covariate effects, flexibly model the baseline survival functions by means of monotonic P-splines, characterise the marginals via transformations of the survival functions which yield, e.g., the proportional hazards and odds models as special cases, and model the dependence between events using a wide variety of copulae. The algorithm is based on a computationally efficient and stable penalised maximum likelihood estimation approach with integrated automatic multiple smoothing parameter selection. The proposed model is evaluated in a simulation study and illustrated using data from the Age-Related Eye Disease Study. The modelling framework has been incorporated in the newly-revised R package GJRM, hence allowing any user to fit the desired model(s) and produce easy-to-interpret numerical and visual summaries.