Dataset Information

Detection and Diagnostic Overall Accuracy Measures of Medical Tests.

ABSTRACT:

Background

Overall accuracy measures of medical tests are often used with unclear interpretations.

Objectives

To develop methods of calculating the overall accuracy of medical tests in the patient population.

Methods

Algebraic equations based on Bayes' theorem.

Results

A new approach is proposed for calculating overall accuracy in the patient population. Examples and applications using published data are presented.

Conclusions

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.

SUBMITTER: Grunau G

PROVIDER: S-EPMC6186004 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

Detection and Diagnostic Overall Accuracy Measures of Medical Tests.

Grunau Gilat G Linn Shai S

Rambam Maimonides medical journal 20181004 4

<h4>Background</h4>Overall accuracy measures of medical tests are often used with unclear interpretations.<h4>Objectives</h4>To develop methods of calculating the overall accuracy of medical tests in the patient population.<h4>Methods</h4>Algebraic equations based on Bayes' theorem.<h4>Results</h4>A new approach is proposed for calculating overall accuracy in the patient population. Examples and applications using published data are presented.<h4>Conclusions</h4>The overall accuracy is the propo ...[more]

PMID: 30309437

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Project description:IntroductionNovel screening tests used to detect a target condition are compared against either a reference standard or other existing screening methods. However, as it is not always possible to apply the reference standard on the whole population under study, verification bias is introduced. Statistical methods exist to adjust estimates to account for this bias. We extend common methods to adjust for verification bias when multiple tests are compared to a reference standard using data from a prospective double blind screening study for prostate cancer.MethodsBegg and Greenes method and multiple imputation are extended to include the results of multiple screening tests which determine condition verification status. These two methods are compared to the complete case analysis using the IP1-PROSTAGRAM study data. IP1-PROSTAGRAM used a paired-cohort double-blind design to evaluate the use of imaging as alternative tests to screen for prostate cancer, compared to a blood test called prostate specific antigen (PSA). Participants with positive imaging (index) and/or PSA (control) underwent a prostate biopsy (reference standard).ResultsWhen comparing complete case results to Begg and Greenes and methods of multiple imputation there is a statistically significant increase in the specificity estimates for all screening tests. Sensitivity estimates remained similar across the methods, with completely overlapping 95% confidence intervals. Negative predictive value (NPV) estimates were higher when adjusting for verification bias, compared to complete case analysis, even though the 95% confidence intervals overlap. Positive predictive value (PPV) estimates were similar across all methods.ConclusionStatistical methods are required to adjust for verification bias in accuracy estimates of screening tests. Expanding Begg and Greenes method to include multiple screening tests can be computationally intensive, hence multiple imputation is recommended, especially as it can be modified for low prevalence of the target condition.

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Dataset Information

Detection and Diagnostic Overall Accuracy Measures of Medical Tests.

Background

Objectives

Methods

Results

Conclusions

Publications

Detection and Diagnostic Overall Accuracy Measures of Medical Tests.

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