A Bayesian nonparametric method for model evaluation: application to genetic studies

Babak Shahbaba, Andrew Gentles, Joseph Beyene, Sylvia Plevritis and Celia Greenwood

Journal of Nonparametric Statistics, 2009, vol. 21, issue 3, 379-396

Abstract: Statistical models applied to genetic studies commonly assume linear relationships (between disease and risk factors) and simple distributional forms (by relying on asymptotic methods) for inference. However, when the sample size is small, inference using traditional asymptotic models can be problematic. Moreover, the gene-disease relationship is not always linear. In this article, we present a new nonparametric Bayesian method for model assessment, and we demonstrate the advantages of this approach particularly when the sample size is small and/or the true model is non-linear. We evaluate our approach on simulated data and find that it performs substantially better than alternative models. We also apply our method to two real studies: diagnosis of conventional high-grade non-metastatic osteosarcoma, and survival in Burkitt's lymphoma.

Date: 2009
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DOI: 10.1080/10485250802613558

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