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Biostatistics Advance Access originally published online on April 14, 2005
Biostatistics 2005 6(3):450-464; doi:10.1093/biostatistics/kxi021
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© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org.

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*

Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, USA schen46{at}jhmi.edu

Patrice Watson

Hereditary Cancer Institute, Creighton University School of Medicine, Omaha, NE, USA

Giovanni Parmigiani

Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, USA, and Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

* To whom correspondence should be addressed. Current address: Division of Clinical Trials and Biometry, 550 North Broadway, Suite 1103, Baltimore, MD 21205, USA.

Microsatellite instability (MSI) testing is a common screening procedure used to identify families that may harbor mutations of a mismatch repair (MMR) 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 MMR 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 0.81 (0.73–0.89). 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 address these challenges in the context of a Bayesian meta-analytic implementation of the Hui–Walter design, tailored to account for various forms of incomplete data. Posterior inference is handled via a Gibbs sampler.

Keywords: Diagnostic test; Hereditary nonpolyposis colorectal cancer; Microsatellite instability; Sensitivity; Specificity


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