Integration of genomic data from multiple platforms has the capability to increase precision, accuracy, and statistical power in the identification of prognostic biomarkers. A fundamental problem faced in many multi-platform studies is unbalanced sample sizes due to the inability to obtain measurements from all the platforms for all the patients in the study. We have developed a novel Bayesian approach that integrates multi-regression models to identify a small set of biomarkers that can accurately predict time-to-event outcomes. This method fully exploits the amount of available information across platforms and does not exclude any of the subjects from the analysis. Through simulations, we demonstrate the utility of our method and compare its performance to that of methods that do not borrow information across regression models. Motivated by The Cancer Genome Atlas kidney renal cell carcinoma dataset, our methodology provides novel insights missed by non-integrative models.
Bibliographical noteFunding Information:
F.C. Stingo and K.-A. Do are partially supported by a Cancer Center Support Grant (NCI Grant P30 CA016672). The authors thank LeeAnn Chastain for editing assistance.
© 2016, The International Biometric Society
- Bayesian variable selection
- Integrating multi-regressions
- Markov random field
- Multiplatform genomic data
- Non-local prior