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Activity Number: 310 - Advances and Novel Problems in Flexible Analysis of Clustered Data with Complex Structures
Type: Invited
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #322016
Title: Flexible Bayesian Additive Joint Models
Author(s): Meike Köhler and Nikolaus Umlauf and Sonja Greven*
Companies: Helmholtz Zentrum München and Universität Innsbruck and Ludwig-Maximilians-Universität München
Keywords: joint models ; biomarkers ; longitudinal data ; time-to-event data ; P-splines ; anisotropic smoothing

The joint modeling of longitudinal and time-to-event data is of growing importance to gain insights into the association between a longitudinal (bio)marker and an event process. We develop a general framework of flexible additive joint models that allows the specification of a variety of effects, such as smooth nonlinear, time-varying and random effects, in the longitudinal and survival parts of the models. Mean and variance of the longitudinal submodel as well as baseline hazard and potentially time-varying covariate and marker effects in the survival submodel can be specified using flexible additive predictors. By making use of Bayesian P-splines and functional random intercepts, we are in particular able to capture highly nonlinear subject-specific marker trajectories as well as a time-varying or non-linear association between the marker and the event process. The model is estimated within a Bayesian framework and implemented in the R-package bamlss.

Authors who are presenting talks have a * after their name.

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