  Motivation: Identification of genomic, molecular and clinical markers
prognostic of patient survival is important for developing personalized disease
prevention, diagnostic and treatment approaches. Modern omics technologies have
made it possible to investigate the prognostic impact of markers at multiple
molecular levels, including genomics, epigenomics, transcriptomics, proteomics
and metabolomics, and how these potential risk factors complement clinical
characterization of patient outcomes for survival prognosis. However, the
massive sizes of the omics data sets, along with their correlation structures,
pose challenges for studying relationships between the molecular information
and patients' survival outcomes. Results: We present a general workflow for
survival analysis that is applicable to high-dimensional omics data as inputs
when identifying survival-associated features and validating survival models.
In particular, we focus on the commonly used Cox-type penalized regressions and
hierarchical Bayesian models for feature selection in survival analysis, which
are are especially useful for high-dimensional data, but the framework is
applicable more generally. Availability and implementation: A step-by-step R
tutorial using The Cancer Genome Atlas survival and omics data for the
execution and evaluation of survival models has been made available at
https://ocbe-uio.github.io/survomics/survomics.html.
