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Joint models for longitudinal and time-to-event data have seen manydevelopments in recent years. Though spatial joint models are still rare andthe traditional proportional hazards formulation of the time-to-event part ofthe model is accompanied by computational challenges. We propose a joint modelwith a piece-wise exponential formulation of the hazard using the countingprocess representation of a hazard and structured additive predictors able toestimate (non-)linear, spatial and random effects. Its capabilities areassessed in a simulation study comparing our approach to an established one andhighlighted by an example on physical functioning after cardiovascular eventsfrom the German Ageing Survey. The Structured Piecewise Additive Joint Modelyielded good estimation performance, also and especially in spatial effects,while being double as fast as the chosen benchmark approach and performingstable in imbalanced data setting with few events.

"Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event Data"