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Mandrekar, Jayawant N.

https://orcid.org/0000-0002-9718-4082

Mayo Clinic

Mayo Clinic

Citations 122

Evaluation of the COBAS TaqMan HCV Test with Automated Sample Processing Using the MagNA Pure LC Instrument
Germer, Jeffrey J., Harmsen, William S. , Mandrekar, Jayawant N. , Mitchell, P. Shawn, Yao, Joseph D.

Journal of Clinical Microbiology | January 5, 2005 | Volume: 43 | Issue: 1 | Pages: 293-298

10.1128/jcm.43.1.293-298.2005 Get It

ABSTRACT The COBAS TaqMan HCV Test (TaqMan HCV; Roche Molecular Systems Inc., Branchburg, N.J.) for hepatitis C virus (HCV) performed on the COBAS TaqMan 48 Analyzer (Roche Molecular Systems) currently relies on a manual sample processing method. Implementation of an automated sample processing method would facilitate the clinical use of this test. In this study, we evaluated the performance characteristics of TaqMan HCV following automated sample processing by the MagNA Pure LC instrument (MP; Roche Applied Science, Indianapolis, Ind.). The analytical sensitivity of TaqMan HCV following sample processing by MP was 8.1 IU/ml (95% confidence interval, 6.1 to 15.2). The assay showed good linearity ( R 2 = 0.99) across a wide range of HCV RNA levels (25 to 5 × 10 6 IU/ml), with coefficients of variation ranging from 10% to 46%. Among 83 clinical specimens, the sensitivity and specificity of TaqMan HCV were 100% and 95%, respectively, when compared to the COBAS AMPLICOR hepatitis C virus test, version 2.0 (COBAS AMPLICOR; Roche Molecular Systems), with TaqMan HCV detecting two more HCV RNA-positive specimens than COBAS AMPLICOR. Both specimens were confirmed to be HCV RNA positive by the VERSANT HCV RNA qualitative test (Bayer HealthCare LLC, Tarrytown, N.Y.). There was also strong correlation ( R 2 = 0.95) and good agreement between the results from TaqMan HCV and the VERSANT HCV RNA 3.0 assay (bDNA) (Bayer HealthCare LLC) among a group of 93 clinical specimens. The MP is a versatile, labor-saving sample processing platform suitable for reliable performance of TaqMan HCV.
Are our data symmetric?
Mandrekar, Sumithra J. , Mandrekar, Jayawant N.

Statistical Methods in Medical Research | November 12, 2003 | Volume: 12 | Issue: 6 | Pages: 505-513

10.1191/0962280203sm346oa Get It

Skewness indicates a lack of symmetry in a distribution. Knowing the symmetry of the underlying data is essential for parametric analysis, fitting distributions or doing transformations to the data. The coefficient of skewness is the commonly used measure to identify a lack of symmetry in the underlying data, although graphical procedures can also be effective. We discuss three different methods to assess skewness: traditional coefficient of skewness index, skewness index based on the L-moments discussed by Hosking and the asymptotic test of symmetry developed by Randles et al. With this work, we provide easy-to-implement S-PLUS functions as well as discuss the advantages and shortcomings of each technique.