Test procedure and sample size determination for a proportion study using a double-sampling scheme with two fallible classifiers.
Double sampling is usually applied to collect necessary information for situations in which an infallible classifier is available for validating a subset of the sample that has already been classified by a fallible classifier. Inference procedures have previously been developed based on the partiall...
| Publicado en: | Statistical Methods in Medical Research Vol. 28; no. 4; pp. 1019 - 1044 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135694762&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135694762 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Apr2019 vid: 28 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 135694762 135694762 NLM29233082 135694762 10.1177/0962280217744239 NLM29233082 135694762 ppf: 1019 ppct: 25 formats: tig: atl: Test procedure and sample size determination for a proportion study using a double-sampling scheme with two fallible classifiers. aug: au: Qiu, Shi-Fang Zeng, Xiao-Song Tang, Man-Lai Poon, Wai-Yin affil: Department of Statistics, Chongqing University of Technology, Chongqing, China sug: subj: Models, Statistical Probability Sample Norway Algorithms Probability Human Sample Size Validation Studies Comparative Studies Evaluation Research Multicenter Studies Questionnaires ab: Double sampling is usually applied to collect necessary information for situations in which an infallible classifier is available for validating a subset of the sample that has already been classified by a fallible classifier. Inference procedures have previously been developed based on the partially validated data obtained by the double-sampling process. However, it could happen in practice that such infallible classifier or gold standard does not exist. In this article, we consider the case in which both classifiers are fallible and propose asymptotic and approximate unconditional test procedures based on six test statistics for a population proportion and five approximate sample size formulas based on the recommended test procedures under two models. Our results suggest that both asymptotic and approximate unconditional procedures based on the score statistic perform satisfactorily for small to large sample sizes and are highly recommended. When sample size is moderate or large, asymptotic procedures based on the Wald statistic with the variance being estimated under the null hypothesis, likelihood rate statistic, log- and logit-transformation statistics based on both models generally perform well and are hence recommended. The approximate unconditional procedures based on the log-transformation statistic under Model I, Wald statistic with the variance being estimated under the null hypothesis, log- and logit-transformation statistics under Model II are recommended when sample size is small. In general, sample size formulae based on the Wald statistic with the variance being estimated under the null hypothesis, likelihood rate statistic and score statistic are recommended in practical applications. The applicability of the proposed methods is illustrated by a real-data example. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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