Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Flexible Bayesian additive joint models with an application to type 1
  diabetes research   The joint modeling of longitudinal and time-to-event data is an important
tool of growing popularity to gain insights into the association between a
biomarker 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. Our extensions are motivated by the
investigation of the relationship between fluctuating disease-specific markers,
in this case autoantibodies, and the progression to the autoimmune disease type
1 diabetes. By making use of Bayesian P-splines we are in particular able to
capture highly nonlinear subject-specific marker trajectories as well as a
time-varying association between the marker and the event process allowing new
insights into disease progression. The model is estimated within a Bayesian
framework and implemented in the R-package bamlss.

24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th