  We introduce a statistical procedure that integrates survival data from
multiple biomedical studies, to improve the accuracy of predictions of survival
or other events, based on individual clinical and genomic profiles, compared to
models developed leveraging only a single study or meta-analytic methods. The
method accounts for potential differences in the relation between predictors
and outcomes across studies, due to distinct patient populations, treatments
and technologies to measure outcomes and biomarkers. These differences are
modeled explicitly with study-specific parameters. We use hierarchical
regularization to shrink the study-specific parameters towards each other and
to borrow information across studies. Shrinkage of the study-specific
parameters is controlled by a similarity matrix, which summarizes differences
and similarities of the relations between covariates and outcomes across
studies. We illustrate the method in a simulation study and using a collection
of gene-expression datasets in ovarian cancer. We show that the proposed model
increases the accuracy of survival prediction compared to alternative
meta-analytic methods.
