Accuracy of MSI Testing in Predicting Germline Mutations of MSH2 and MLH1: A Case Study in Bayesian Meta-Analysis of Diagnostic Tests Without a Gold Standard
Sining Chen (The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University) Patrice Watson (Hereditary Cancer Institute, Creighton University School of Medicine, Omaha, NE) Giovanni Parmigiani (Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University and Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health)
Abstract
Microsatellite instability (MSI) testing is a common screening procedure used to identify families that may harbor mutations of a mismatch repair gene and therefore may be at high risk for hereditary colorectal cancer. A reliable estimate of sensitivity and specificity of MSI for detecting germline mutations of mismatch repair genes is critical in genetic counseling and colorectal cancer prevention. Several studies published results of both MSI and mutation analysis on the same subjects. In this article we perform a meta-analysis of these studies and obtain estimates that can be directly used in counseling and screening. In particular we estimate the sensitivity of MSI for detecting mutations of MSH2 and MLH1 to be 0.78 (0.69--0.86). Statistically, challenges arise from the following: a) traditional mutation analysis methods used in these studies cannot be considered a gold standard for the identification of mutations; b) studies are heterogeneous in both the design and the populations considered; and c) studies may include different patterns of missing data resulting from partial testing of the populations sampled. We addressed these challenges in the context of a Bayesian meta-analytic implementation of the Hui-Walter design, designed to account for various forms of incomplete data. Posterior inference are handled via a Gibbs sampler.
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