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Estimating covariate-adjusted measures of diagnostic accuracy based on pooled biomarker assessments.

ABSTRACT: There is a need for epidemiological and medical researchers to identify new biomarkers (biological markers) that are useful in determining exposure levels and/or for the purposes of disease detection. Often this process is stunted by high testing costs associated with evaluating new biomarkers. Traditionally, biomarker assessments are individually tested within a target population. Pooling has been proposed to help alleviate the testing costs, where pools are formed by combining several individual specimens. Methods for using pooled biomarker assessments to estimate discriminatory ability have been developed. However, all these procedures have failed to acknowledge confounding factors. In this paper, we propose a regression methodology based on pooled biomarker measurements that allow the assessment of the discriminatory ability of a biomarker of interest. In particular, we develop covariate-adjusted estimators of the receiver-operating characteristic curve, the area under the curve, and Youden's index. We establish the asymptotic properties of these estimators and develop inferential techniques that allow one to assess whether a biomarker is a good discriminator between cases and controls, while controlling for confounders. The finite sample performance of the proposed methodology is illustrated through simulation. We apply our methods to analyze myocardial infarction (MI) data, with the goal of determining whether the pro-inflammatory cytokine interleukin-6 is a good predictor of MI after controlling for the subjects' cholesterol levels.

SUBMITTER: McMahan CS

PROVIDER: S-EPMC6234508 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Project description:Overall accuracy measures of medical tests are often used with unclear interpretations. To develop methods of calculating the overall accuracy of medical tests in the patient population. Algebraic equations based on Bayes' theorem. A new approach is proposed for calculating overall accuracy in the patient population. Examples and applications using published data are presented. The overall accuracy is the proportion of the correct test results. We introduce a clear distinction between the overall accuracy measures of medical tests that are aimed at the detection of a disease in a screening of populations for public health purposes in the general population and the overall accuracy measures of tests aimed at determining a diagnosis in individuals in a clinical setting. We show that the overall detection accuracy measure is obtained in a specific study that explores test accuracy among persons with known diagnoses and may be useful for public health screening tests. It is different from the overall diagnostic accuracy that could be calculated in the clinical setting for the evaluation of medical tests aimed at determining the individual patients' diagnoses. We show that the overall detection accuracy is constant and is not affected by the prevalence of the disease. In contrast, the overall diagnostic accuracy changes and is dependent on the prevalence. Moreover, it ranges according to the ratio between the sensitivity and specificity. Thus, when the sensitivity is greater than the specificity, the overall diagnostic accuracy increases with increasing prevalence, and vice versa, that is, when the sensitivity is lower than the specificity, the overall diagnostic accuracy decreases with increasing prevalence so that another test might be more useful for diagnostic procedures. Our paper suggests a new and more appropriate methodology for estimating the overall diagnostic accuracy of any medical test. This may be important for helping clinicians avoid errors.

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Estimating covariate-adjusted measures of diagnostic accuracy based on pooled biomarker assessments.

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