arXiv 21 Dec 2020 · Econometrics · 3 citations (OpenAlex)
arXiv:2012.11215 · PDF · DOI · OpenAlex · Extracted main text
New binary classification tests are often evaluated relative to a pre-established test. For example, rapid Antigen tests for the detection of SARS-CoV-2 are assessed relative to more established PCR tests. In this paper, I argue that the new test can be described as producing ambiguous information when the pre-established is imperfect. This allows for a phenomenon called dilation -- an extreme form of non-informativeness. As an example, I present hypothetical test data satisfying the WHO's minimum quality requirement for rapid Antigen tests which leads to dilation. The ambiguity in the information arises from a missing data problem due to imperfection of the established test: the joint distribution of true infection and test results is not observed. Using results from Copula theory, I construct the (usually non-singleton) set of all these possible joint distributions, which allows me to assess the new test's informativeness. This analysis leads to a simple sufficient condition to make sure that a new test is not a dilation. I illustrate my approach with applications to data from three COVID-19 related tests. Two rapid Antigen tests satisfy my sufficient condition easily and are therefore informative. However, less accurate procedures, like chest CT scans, may exhibit dilation.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Blackwell, D (1951) Comparison of Experiments, in | 1.000 | 8 | 3 | 100% |
| 2 | WHO (2020) Antigen-Detection in the Diagnosis of SARS-CoV-2 Infection Using Rapid Immunoassays, https://www.who.int/publications-detail-red… | 1.000 | 6 | 3 | 100% |
| 3 | Manski, C. F. and F. Molinari (2021) Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem | 0.874 | 5 | 2 | 100% |
| 4 | Stoye, J (2020) Bounding Disease Prevalence by Bounding Selectivity and Accuracy of Tests: The Case of COVID-19 | 0.811 | 4 | 2 | 100% |
| 5 | Joe, H (1997) Multivariate Models and Multivariate Dependence Concepts | 0.737 | 4 | 2 | 75% |
| 6 | Manski, C. F (2020) Bounding the Accuracy of Diagnostic Tests, with Application to COVID-19 Antibody Tests | 0.737 | 3 | 2 | 100% |
| 7 | Zhou, X.-H., N. A. Obuchowski, and D. K. McClish (2014) Statistical Methods in Diagnostic Medicine | 0.737 | 3 | 2 | 100% |
| 8 | Gietema, H. A., N. Zelis, J. M. Nobel, L. J. G. Lambriks, L. B. van… (2020) CT in Relation to RT-PCR in Diagnosing COVID-19 in The Netherlands: A Prospective Study | 0.693 | 5 | 1 | 100% |
| 9 | Kaiser, L., I. Eckerle, M. Schibler, and A. Berger (2020) Validation Report: SARS-CoV-2 Antigen Rapid Diagnostic Test, Tech | 0.693 | 5 | 1 | 100% |
| 10 | Ai, T., Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, Q. Tao, Z. Sun, an… (2020) Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases | 0.644 | 4 | 1 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Measuring Diagnostic Test Performance Using Imperfect Reference Tests: A Partial Identification Approach | 0.737 | 3 | 3 |