  It is well known that the integration among different data-sources is
reliable because of its potential of unveiling new functionalities of the
genomic expressions which might be dormant in a single source analysis.
Moreover, different studies have justified the more powerful analyses of
multi-platform data. Toward this, in this study, we consider the circadian
genes' omics profile such as copy number changes and RNA sequence data along
with their survival response. We develop a Bayesian structural equation
modeling coupled with linear regressions and log normal accelerated failure
time regression to integrate the information between these two platforms to
predict the survival of the subjects. We place conjugate priors on the
regression parameters and derive the Gibbs sampler using the conditional
distributions of them. Our extensive simulation study shows that the
integrative model provides a better fit to the data than its closest
competitor. The analyses of glioblastoma cancer data and the breast cancer data
from TCGA, the largest genomics and transcriptomics database, support our
findings. The developed method is wrapped in R package semmcmc available at R
CRAN.
