Enhanced Inference for Finite Population Sampling-Based Prevalence Estimation with Misclassification Errors.
Epidemiologic screening programs often make use of tests with small, but nonzero probabilities of misdiagnosis. In this article, we assume the target population is finite with a fixed number of true cases, and that we apply an imperfect test with known sensitivity and specificity to a sample of indi...
| Publicado en: | American Statistician Vol. 78; no. 2; pp. 192 - 199 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
May2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=176695304&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 176695304 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: May2024 vid: 78 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 176695304 10.1080/00031305.2023.2250401 ppf: 192 ppct: 7 formats: tig: atl: Enhanced Inference for Finite Population Sampling-Based Prevalence Estimation with Misclassification Errors. aug: au: Ge, Lin Zhang, Yuzi Waller, Lance A. Lyles, Robert H. affil: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA su: Maximum likelihood statistics Inferential statistics Sampling errors Disease prevalence Sensitivity & specificity (Statistics) Confidence intervals Statistical sampling sug: subj: Marketing Research and Public Opinion Polling Maximum likelihood statistics Inferential statistics Sampling errors Disease prevalence Sensitivity & specificity (Statistics) Confidence intervals Statistical sampling keyword: Bias correction Credible interval Finite population correction Random sampling Sensitivity Specificity Bias correction Credible interval Finite population correction Random sampling Sensitivity Specificity ab: Epidemiologic screening programs often make use of tests with small, but nonzero probabilities of misdiagnosis. In this article, we assume the target population is finite with a fixed number of true cases, and that we apply an imperfect test with known sensitivity and specificity to a sample of individuals from the population. In this setting, we propose an enhanced inferential approach for use in conjunction with sampling-based bias-corrected prevalence estimation. While ignoring the finite nature of the population can yield markedly conservative estimates, direct application of a standard finite population correction (FPC) conversely leads to underestimation of variance. We uncover a way to leverage the typical FPC indirectly toward valid statistical inference. In particular, we derive a readily estimable extra variance component induced by misclassification in this specific but arguably common diagnostic testing scenario. Our approach yields a standard error estimate that properly captures the sampling variability of the usual bias-corrected maximum likelihood estimator of disease prevalence. Finally, we develop an adapted Bayesian credible interval for the true prevalence that offers improved frequentist properties (i.e., coverage and width) relative to a Wald-type confidence interval. We report the simulation results to demonstrate the enhanced performance of the proposed inferential methods. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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