  Important objectives in cancer research are the prediction of a patient's
risk based on molecular measurements such as gene expression data and the
identification of new prognostic biomarkers (e.g. genes). In clinical practice,
this is often challenging because patient cohorts are typically small and can
be heterogeneous. In classical subgroup analysis, a separate prediction model
is fitted using only the data of one specific cohort. However, this can lead to
a loss of power when the sample size is small. Simple pooling of all cohorts,
on the other hand, can lead to biased results, especially when the cohorts are
heterogeneous. For this situation, we propose a new Bayesian approach suitable
for continuous molecular measurements and survival outcome that identifies the
important predictors and provides a separate risk prediction model for each
cohort. It allows sharing information between cohorts to increase power by
assuming a graph linking predictors within and across different cohorts. The
graph helps to identify pathways of functionally related genes and genes that
are simultaneously prognostic in different cohorts. Results demonstrate that
our proposed approach is superior to the standard approaches in terms of
prediction performance and increased power in variable selection when the
sample size is small.
