IntegMultiReg: Integrative Bayesian Multiple Regression for Multi-Platform
Biomarkers
A Bayesian framework that integrates several regression models to
identify a parsimonious set of biomarkers shared across disparate data
platforms (for example genomic, transcriptomic and proteomic assays).
Subjects are partitioned into subgroups defined by their pattern of
platform availability, so that no subject with partially missing platform
data is excluded, and information is borrowed across subgroups through a
Markov random field prior on the variable-selection indicators together
with non-local (product moment) priors on the regression effects. The
methodology was introduced for time-to-event outcomes by Chekouo,
Stingo, Doecke and Do (2017) <doi:10.1111/biom.12587>; this package
additionally supports continuous (Gaussian) and binary (probit) outcomes.
Posterior inference is carried out by a Markov chain Monte Carlo sampler
implemented in C for computational efficiency.
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