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