  Identification of biomarkers is an emerging area in Oncology. In this
article, we develop an efficient statistical procedure for classification of
protein markers according to their effect on cancer progression. A
high-dimensional time-course dataset of protein markers for 80 patients
motivates us for developing the model. We obtain the optimal threshold values
for markers using Cox proportional hazard model. The optimal threshold value is
defined as a level of a marker having maximum impact on cancer progression. The
classification was validated by comparing random components using both
proportional hazard and accelerated failure time frailty models. The study
elucidates the application of two separate joint modeling techniques using auto
regressive-type model and mixed effect model for time-course data and
proportional hazard model for survival data with proper utilization of Bayesian
methodology. Also, a prognostic score has been developed on the basis of few
selected genes with application on patients. The complete analysis is performed
by R programming code. This study facilitates to identify relevant biomarkers
from a set of markers.
