Paper Info
Title
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.
Year
DOI
Venue
2022
10.1016/j.csda.2022.107550
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Keywords
DocType
Volume
Additive predictor, Bivariate survival data, Copula, Link function, Mixed censoring scheme, Simultaneous penalised parameter, estimation
Journal
175
ISSN
Citations 
PageRank 
0167-9473
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Danilo Petti100.34
Alessia Eletti200.34
Giampiero Marra3163.84
Rosalba Radice400.34