4 research outputs found
"Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event Data"
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
Variable Selection and Allocation in Joint Models via Gradient Boosting Techniques
Modeling longitudinal data (e.g., biomarkers) and the risk for events separately leads to a loss of information and bias, even though the underlying processes are related to each other. Hence, the popularity of joint models for longitudinal and time-to-event-data has grown rapidly in the last few decades. However, it is quite a practical challenge to specify which part of a joint model the single covariates should be assigned to as this decision usually has to be made based on background knowledge. In this work, we combined recent developments from the field of gradient boosting for distributional regression in order to construct an allocation routine allowing researchers to automatically assign covariates to the single sub-predictors of a joint model. The procedure provides several well-known advantages of model-based statistical learning tools, as well as a fast-performing allocation mechanism for joint models, which is illustrated via empirical results from a simulation study and a biomedical application
State-switching decision trees
Adam T, Ötting M, Michels R. State-switching decision trees. In: Bergherr E, Groll A, Mayr A, eds. Proceedings of the 37th International Workshop on Statistical Modelling. Part II. Dortmund: TU Dortmund; 2023: 321-325