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Abstract Background Staquet et al. and Brenner both developed correction methods to estimate the sensitivity and specificity of a binary-response index test when the reference standard is imperfect and its sensitivity and specificity are known. However, to our knowledge, no study has compared the statistical properties of these methods, despite their long application in diagnostic accuracy studies. Aim To compare the correction methods developed by Staquet et al. and Brenner. Methods Simulations techniques were employed to compare the methods under assumptions that the new test and the reference standard are conditionally independent or dependent given the true disease status of an individual. Three clinical datasets were analysed to understand the impact of using each method to inform clinical decision-making. Results Under the assumption of conditional independence, the Staquet et al. correction method outperforms the Brenner correction method irrespective of the prevalence of disease and whether the performance of the reference standard is better or worse than the index test. However, when the prevalence of the disease is high (> 0.9) or low (

In some applications, only a coarsened version of a categorical outcome variable can be observed. Parametric inference based on the maximum likelihood approach is feasible in principle, but it cannot be covered computationally by standard software tools. In this article, we present two commands facilitating maximum likelihood estimation in this situation for a wide range of parametric models for categorical outcomes—in the cases both of a nominal and an ordinal scale. In particular, the case of probabilistic information about the possible values of the outcome variable is also covered. Two examples motivating this scenario are presented and analyzed. [ABSTRACT FROM AUTHOR]

Background: Staquet et al. and Brenner both developed correction methods to estimate the sensitivity and specificity of a binary-response index test when the reference standard is imperfect and its sensitivity and specificity are known. However, to our knowledge, no study has compared the statistical properties of these methods, despite their long application in diagnostic accuracy studies.Aim: To compare the correction methods developed by Staquet et al. and Brenner.Methods: Simulations techniques were employed to compare the methods under assumptions that the new test and the reference standard are conditionally independent or dependent given the true disease status of an individual. Three clinical datasets were analysed to understand the impact of using each method to inform clinical decision-making.Results: Under the assumption of conditional independence, the Staquet et al. correction method outperforms the Brenner correction method irrespective of the prevalence of disease and whether the performance of the reference standard is better or worse than the index test. However, when the prevalence of the disease is high (> 0.9) or low (< 0.1), the Staquet et al. correction method can produce illogical results (i.e. results outside [0,1]). Under the assumption of conditional dependence; both methods failed to estimate the sensitivity and specificity of the index test especially when the covariance terms between the index test and the reference standard is not close to zero.Conclusion: When the new test and the imperfect reference standard are conditionally independent, and the sensitivity and specificity of the imperfect reference standard are known, the Staquet et al. correction method outperforms the Brenner method. However, where the prevalence of the target condition is very high or low or the two tests are conditionally dependent, other statistical methods such as latent class approaches should be considered. [ABSTRACT FROM AUTHOR]

The area under the receiver operating characteristic (ROC) curve (AUC) is used as a performance metric for quantitative tests. Although multiple biomarkers may be available for diagnostic or screening purposes, diagnostic accuracy is often assessed individually rather than in combination. In this paper, we consider the interesting problem of combining multiple biomarkers for use in a single diagnostic criterion with the goal of improving the diagnostic accuracy above that of an individual biomarker. The diagnostic criterion created from multiple biomarkers is based on the predictive probability of disease, conditional on given multiple biomarker outcomes. If the computed predictive probability exceeds a specified cutoff, the corresponding subject is allocated as 'diseased'. This defines a standard diagnostic criterion that has its own ROC curve, namely, the combined ROC (cROC). The AUC metric for cROC, namely, the combined AUC (cAUC), is used to compare the predictive criterion based on multiple biomarkers to one based on fewer biomarkers. A multivariate random-effects model is proposed for modeling multiple normally distributed dependent scores. Bayesian methods for estimating ROC curves and corresponding (marginal) AUCs are developed when a perfect reference standard is not available. In addition, cAUCs are computed to compare the accuracy of different combinations of biomarkers for diagnosis. The methods are evaluated using simulations and are applied to data for Johne's disease (paratuberculosis) in cattle. [ABSTRACT FROM AUTHOR]

When no gold standard is available to evaluate a diagnostic or screening test, as is often the case, an imperfect reference standard test must be used instead. Furthermore, the errors of the test and its reference standard may not be independent. Some authors have opined that positively dependent errors will lead to overestimation of test performance. Although positive dependence does increase agreement between the test and the reference standard, it is not clear if test accuracy will necessarily be overestimated in this situation, and the case of negatively associated test errors is even less clear. To examine this issue in more detail, we derive the apparent sensitivity, specificity, and overall accuracy of a test relative to an imperfect reference standard and the bias in these parameters. We demonstrate that either positive or negative bias can occur if the reference standard is imperfect. The type and magnitude of bias depend on several components: the disease prevalence, the true test sensitivity and specificity, the covariance between the false-negative test errors among the true disease cases, and the covariance between the false-positive test errors among the true noncases. If, for example, sensitivity and specificity are 0.8 for both the test and reference standard and the errors have a moderate positive dependence, test sensitivity is then underestimated at low prevalence but overestimated at high prevalence, while the opposite occurs for specificity. We illustrate these ideas through general numerical calculations and an empirical example of screening for breast cancer with magnetic resonance imaging and mammography. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Problems arising with the estimation of sensitivity and specificity when two imperfect diagnostic tests are applied are widely discussed. Effects on the estimation of prevalence may be of importance as well. Different methods of dealing with two or more imperfect tests and unknown reference standard are contrasted with regard to their implications on prevalence estimation: discrepant analysis, composite reference standards, and latent class models. Study Design and Setting: Prospective epidemiological multicenter study to determine the prevalence of respiratory syncytial virus in children with lower respiratory tract infections. A subsample of 1,003 patients from a hospital population and from a practice population is considered. Virus isolation, polymerase chain reaction, and rapid antigen test had been applied. Results: Prevalence estimates obtained under various assumptions ranged from 0.263 to 0.386 in the hospital population and from 0.214 to 0.277 in the practice population. Conclusion: Estimation procedures involving a resolver test applied to some but not all cells are at risk of introducing a serious bias in prevalence estimation as well as in the estimation of test accuracy parameters. Estimation via latent class modeling may be more useful, but care should be taken regarding the underlying assumptions. [Copyright &y& Elsevier]

Objectives: The objective of this study was to study the impact of ignoring uncertainty by forcing dichotomous classification (presence or absence) of the target disease on estimates of diagnostic accuracy of an index test., Study Design and Setting: We evaluated the bias in estimated index test accuracy when forcing an expert panel to make a dichotomous target disease classification for each individual. Data for various scenarios with expert panels were simulated by varying the number and accuracy of "component reference tests" available to the expert panel, index test sensitivity and specificity, and target disease prevalence., Results: Index test accuracy estimates are likely to be biased when there is uncertainty surrounding the presence or absence of the target disease. Direction and amount of bias depend on the number and accuracy of component reference tests, target disease prevalence, and the true values of index test sensitivity and specificity., Conclusion: In this simulation, forcing expert panels to make a dichotomous decision on target disease classification in the presence of uncertainty leads to biased estimates of index test accuracy. Empirical studies are needed to demonstrate whether this bias can be reduced by assigning a probability of target disease presence for each individual, or using advanced statistical methods to account for uncertainty in target disease classification., (Copyright © 2019 Elsevier Inc. All rights reserved.)