Nonlinear association structures in flexible Bayesian additive joint models

We present an extended framework of flexible additive joint models that allows the estimation of nonlinear covariate specific associations by making use of Bayesian P ‐splines. Our joint models are estimated in a Bayesian framework using structured additive predictors for all model components, allowing for great flexibility in the specification of smooth nonlinear, time‐varying, and random effects terms for longitudinal submodel, survival submodel, and their association. The ability to capture truly linear and nonlinear associations is assessed in simulations and illustrated on the widely studied biomedical data on the rare fatal liver disease primary biliary cirrhosis. All methods are implemented in theR packagebamlss to facilitate the application of this flexible joint model in practice.
Source: Statistics in Medicine - Category: Statistics Authors: Tags: RESEARCH ARTICLE Source Type: research