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Title: Link-based survival additive models with mixed types of censoring Authors:  Giampiero Marra - University College London (United Kingdom) [presenting]
Rosalba Radice - Cass Business School (United Kingdom)
Davide Lazzaro - Cass Business School (United Kingdom)
Abstract: Existing methods for survival models are limited in that they do not often consider monotonicity constraints on the survival function, flexible covariate effects and different types of censoring mechanisms simultaneously. A methodology is discussed that addresses the three above mentioned problems by allowing for survival outcomes to be modelled using flexible parametric formulations for time-to-event data, the baseline survival function to be modelled using monotonic splines, and covariate effects to be modelled using an additive predictor incorporating several types of covariate effects. The models parameters are estimated using a carefully structured efficient and stable penalized likelihood algorithm. The proposed framework is evaluated using simulated and real data sets. The relevant numerical computations can be easily carried out using the freely available GJRM R package.