Evaluation of 30 commercial assays for the detection of antibodies to HIV in China using classical and Bayesian statistics

Liu, Pei; Yang, Haitao; Liu, Qiang; Jin, Hui; Xiao, Shan; Shi, Zhixu

doi:10.1016/j.jviromet.2010.09.001

Cited by 4 publications

(11 citation statements)

References 22 publications

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“…Bayesian methods have flourished despite these conditions, aided by the ease of use of programs such as WinBUGS for numerical estimation, while frequentist methods have suffered from a lack of off‐the‐shelf code for estimation and the increased interest in studying nonidentifiable models that are generally ill‐posed problems from the perspective of frequentist modeling. This state of affairs has also resulted in a lack of comparisons between frequentist and Bayesian methods in the literature with which researchers could assess the merits of each approach.…”

Section: Resultsmentioning

confidence: 99%

“…SAS code for misclassification in logistic regression is available . Some authors have included their WinBUGS code in an appendix , and code for multiple models from the papers of Branscum et al . and Choi et al .…”

Section: Special Topicsmentioning

confidence: 99%

“…SAS code for misclassification in logistic regression is available [5]. Some authors have included their WinBUGS code in an appendix [28,72,76,154,163,[253][254][255][256], and code for multiple models from the papers of Branscum et al [48] and Choi et al [24] is available at http://www.epi.ucdavis.edu/diagnostictests/software.html…”

Section: Computing the Modelmentioning

confidence: 99%

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Estimation of diagnostic test accuracy without full verification: a review of latent class methods

Collins

Huynh

2014

Statist. Med.

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The performance of a diagnostic test is best evaluated against a reference test that is without error. For many diseases, this is not possible, and an imperfect reference test must be used. However, diagnostic accuracy estimates may be biased if inaccurately verified status is used as the truth. Statistical models have been developed to handle this situation by treating disease as a latent variable. In this paper, we conduct a systematized review of statistical methods using latent class models for estimating test accuracy and disease prevalence in the absence of complete verification.

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Section: Resultsmentioning

confidence: 99%

Section: Special Topicsmentioning

confidence: 99%

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Estimation of diagnostic test accuracy without full verification: a review of latent class methods

Collins

Huynh

2014

Statist. Med.

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“…The occurrence of extreme proportion (i.e., the point estimate of HIV test sensitivity and/or specificity reaches 100%) presents challenges for the assessment of test accuracy. Because inherent problems of false negative and false positive are inevitable in HIV testing [8,9], these challenges are presented in two aspects: one is that the point estimate calculated by the current method is fixed for any value of sample size when extreme proportion occur [5,10]. Another is that some comprehensive indicators fail to reasonably assess the operational performance of HIV assays when the sample proportion is 100% [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Because inherent problems of false negative and false positive are inevitable in HIV testing [8,9], these challenges are presented in two aspects: one is that the point estimate calculated by the current method is fixed for any value of sample size when extreme proportion occur [5,10]. Another is that some comprehensive indicators fail to reasonably assess the operational performance of HIV assays when the sample proportion is 100% [10,11]. The study by Manak et al shows that it is difficult to interpret the results of HIV screening tests using the conventional simple estimates when a combination of two RDTs has 100% specificity.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Extreme Proportion Calculation: Median Estimate and Its Application to Evaluate Performance of HIV Assays With 100% Test Sensitivity or Specificity

Jin

Shao

Yang³

et al. 2021

Preprint

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BackgroundIn the evaluation of performance of HIV assays, extreme sample proportions often occur, with test sensitivity and/or specificity of 100%, which making it challenging to assess the assays accuracies. To overcome these challenges, we propose using median estimate as an evaluation indicator for such testing.MethodsBased on the principles of binomial distribution and confidence interval, median estimate was defined as \(p={0.5}^{\frac{1}{n}}\), which means that, when the sample size n is equal to the event number x, namely the sample proportion (e.g., test sensitivity) is 100%, the 50th percentile (median) of p (the estimate of population proportion) is \({0.5}^{\frac{1}{n}}\). After demonstrating the mathematical proof of the median estimate, the key programming commands of commercial software SAS and free software R were given. Subsequently, we developed an Excel-based calculation tool that allows users to fill in data in an Excel sheet without writing any program. Six cases of HIV screening and diagnostic tests and HIV infections incidence data were selected from related articles and World Health Organization reports published between 2009 and 2020.ResultsThe median estimates, which were proved in the range from \(\frac{n-1}{n}\) to 1 and within the confidence interval range, showed statistical plausibility. Six HIV testing cases were presented to illustrate its application and elaborate on the relationship between the median estimate and the conventional simple estimate. These cases demonstrate that, when extreme proportions occurred (i.e., false positive and/or false negative in testing were zero), the conventional simple estimates of sensitivity, specificity, positive predictive value, and negative predictive value were 100% regardless of the sample size and prevalence. In contrast, the corresponding median estimates varied depending on the sample size and prevalence.ConclusionsAs evaluation indicators of HIV assays with extreme proportions, median estimates were more effective than conventional simple estimates. However, simple estimates objectively expressed the results of HIV testing. Because the correlation between median estimates and simple estimates was seamless, the two types of indicators were complementary in the evaluation of testing with extreme proportions. Hence, using both types of estimates could help evaluate HIV assays with extreme proportions more comprehensively.

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Estimation of the sensitivity and specificity of assays for screening antibodies to HIV: A comparison between the frequentist and Bayesian approaches

Liu

Yang

Liu³

et al. 2012

Journal of Virological Methods

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Evaluation of 30 commercial assays for the detection of antibodies to HIV in China using classical and Bayesian statistics

Cited by 4 publications

References 22 publications

Estimation of diagnostic test accuracy without full verification: a review of latent class methods

Estimation of diagnostic test accuracy without full verification: a review of latent class methods

A New Approach for Extreme Proportion Calculation: Median Estimate and Its Application to Evaluate Performance of HIV Assays With 100% Test Sensitivity or Specificity

Estimation of the sensitivity and specificity of assays for screening antibodies to HIV: A comparison between the frequentist and Bayesian approaches

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Evaluation of 30 commercial assays for the detection of antibodies to HIV in China using classical and Bayesian statistics

Cited by 4 publications

References 22 publications

Estimation of diagnostic test accuracy without full verification: a review of latent class methods

Estimation of diagnostic test accuracy without full verification: a review of latent class methods

A New Approach for Extreme Proportion Calculation: Median Estimate and Its Application to Evaluate Performance of HIV&nbsp;Assays With 100% Test Sensitivity or Specificity

Estimation of the sensitivity and specificity of assays for screening antibodies to HIV: A comparison between the frequentist and Bayesian approaches

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A New Approach for Extreme Proportion Calculation: Median Estimate and Its Application to Evaluate Performance of HIV Assays With 100% Test Sensitivity or Specificity