A method to compare the performance of two molecular diagnostic tools in the absence of a gold standard

Abstract

The paper is motivated by the problem of comparing the accuracy of two molecular tests in detecting genetic mutations in tumor samples when there is no gold standard test. Commonly used sequencing methods require a large number of tumor cells in the tumor sample and the proportion of tumor cells with mutation positivity to be above a threshold level whereas new tests aim to reduce the requirement for number of tumor cells and the threshold level. A new latent class model is proposed to compare these two tests in which a random variable is used to represent the unobserved proportion of mutation positivity so that these two tests are conditionally dependent; furthermore, an independent random variable is included to address measurement error associated with the reading from each test, while existing latent class models often assume conditional independence and do not allow measurement error. In addition, methods for calculating the sample size for a study that is sufficiently powered to compare the accuracy of two molecular tests are proposed and compared. The proposed methods are then applied to a study which aims to compare two molecular tests for detecting EGFR mutations in lung cancer patients.

Keywords

Gold standard latent class model sensitivity specificity measurement error sample size calculation

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References

Ogino

Kawasaki

Brahmandam

et al.

Sensitive sequencing method for KRAS mutation detection by Pyrosequencing. J Mol Diagn 2005; 7: 413–421.

Oktay

Adler

Hakima

et al.

The application of molecular diagnostics to stained cytology smears. J Mol Diagn 2016; 18: 407–415.

van Smeden

Naaktgeboren

Reitsma

et al.

Latent class models in diagnostic studies when there is no reference standard – a systematic review. Am J Epidemiol 2014; 179: 423–431.

Rutjes

Reitsma

Coomarasamy

et al.

Evaluation of diagnostic tests when there is no gold standard. A review of methods. Health Technol Assess (Winchester, England) 2007; 11: iii, ix–51.

Zhou

X-H

Obuchowski

McClish

. Methods for correcting imperfect gold standard bias. Statistical methods in diagnostic medicine, 2nd ed. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2011, pp. 389–433.

Garrett

Eaton

Zeger

. Methods for evaluating the performance of diagnostic tests in the absence of a gold standard: a latent class model approach. Stat Med 2002; 21: 1289–1307.

Joseph

Gyorkos

Coupal

. Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold standard. Am J Epidemiol 1995; 141: 263–272.

Dendukuri

Joseph

. Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics 2001; 57: 158–167.

Brenner

. How independent are multiple ‘independent’ diagnostic classifications? Stat Med 1996; 15: 1377–1386.

10.

Tan

Kutner

. Random effects models in latent class analysis for evaluating accuracy of diagnostic tests. Biometrics 1996; 52: 797–810.

11.

Dendukuri

Belisle

Joseph

. Bayesian sample size for diagnostic test studies in the absence of a gold standard: comparing identifiable with non-identifiable models. Stat Med 2010; 29: 2688–2697.

12.

Dendukuri

Rahme

Belisle

et al.

Bayesian sample size determination for prevalence and diagnostic test studies in the absence of a gold standard test. Biometrics 2004; 60: 388–397.

13.

Alonzo

Pepe

Moskowitz

. Sample size calculations for comparative studies of medical tests for detecting presence of disease. Stat Med 2002; 21: 835–852.

14.

Schumacher

van Smeden

Dendukuri

et al.

Diagnostic test accuracy in childhood pulmonary tuberculosis: a Bayesian latent class analysis. Am J Epidemiol 2016; 184: 690–700.

15.

Connor

. Sample size for testing differences in proportions for the paired-sample design. Biometrics 1987; 43: 207–211.

16.

Snedecor

Cochran

. Statistical method, 8th ed, Ames: USA: Iowa State University, 1994.

17.

Cheng

Macaluso

. Comparison of the accuracy of two tests with a confirmatory procedure limited to positive results. Epidemiology (Cambridge, Mass) 1997; 8: 104–106.

18.

Schatzkin

Connor

Taylor

et al.

Comparing new and old screening tests when a reference procedure cannot be performed on all screenees. Example of automated cytometry for early detection of cervical cancer. Am J Epidemiol 1987; 125: 672–678.

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