Bayesian sample size determination for estimating binomial parameters from data subject to misclassification
We investigate the sample size problem when a binomial parameter is to be estimated, but some degree of misclassification is possible. The problem is especially challenging when the degree to which misclassification occurs is not exactly known. Motivated by a Canadian survey of the prevalence of tox...
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| Veröffentlicht in: | Applied statistics 2000, Vol.49 (1), p.119-128 |
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| container_end_page | 128 |
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| container_start_page | 119 |
| container_title | Applied statistics |
| container_volume | 49 |
| creator | Rahme, E. Joseph, L. Gyorkos, T. W. |
| description | We investigate the sample size problem when a binomial parameter is to be estimated, but some degree of misclassification is possible. The problem is especially challenging when the degree to which misclassification occurs is not exactly known. Motivated by a Canadian survey of the prevalence of toxoplasmosis infection in pregnant women, we examine the situation where it is desired that a marginal posterior credible interval for the prevalence of width w has coverage 1-α, using a Bayesian sample size criterion. The degree to which the misclassification probabilities are known a priori can have a very large effect on sample size requirements, and in some cases achieving a coverage of 1-α is impossible, even with an infinite sample size. Therefore, investigators must carefully evaluate the degree to which misclassification can occur when estimating sample size requirements. |
| doi_str_mv | 10.1111/1467-9876.00182 |
| format | Article |
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W.</creatorcontrib><title>Bayesian sample size determination for estimating binomial parameters from data subject to misclassification</title><title>Applied statistics</title><description>We investigate the sample size problem when a binomial parameter is to be estimated, but some degree of misclassification is possible. The problem is especially challenging when the degree to which misclassification occurs is not exactly known. Motivated by a Canadian survey of the prevalence of toxoplasmosis infection in pregnant women, we examine the situation where it is desired that a marginal posterior credible interval for the prevalence of width w has coverage 1-α, using a Bayesian sample size criterion. The degree to which the misclassification probabilities are known a priori can have a very large effect on sample size requirements, and in some cases achieving a coverage of 1-α is impossible, even with an infinite sample size. Therefore, investigators must carefully evaluate the degree to which misclassification can occur when estimating sample size requirements.</description><subject>A priori knowledge</subject><subject>Arithmetic mean</subject><subject>Average coverage criterion</subject><subject>Bayesian estimation</subject><subject>Bayesian method</subject><subject>Binomial distribution</subject><subject>Binomials</subject><subject>Classification</subject><subject>Data collection</subject><subject>Decision theory</subject><subject>Diagnostic test</subject><subject>Disease prevalence rates</subject><subject>Distribution</subject><subject>Error rates</subject><subject>Estimate reliability</subject><subject>Estimation</subject><subject>Exact sciences and technology</subject><subject>Interval estimators</subject><subject>Mathematics</subject><subject>Medical diagnostic tests</subject><subject>Misclassification</subject><subject>Parametric inference</subject><subject>Pregnancy</subject><subject>Prevalence</subject><subject>Probability and statistics</subject><subject>Regression analysis</subject><subject>Sample size</subject><subject>Samples</subject><subject>Sciences and techniques of general use</subject><subject>Statistics</subject><issn>0035-9254</issn><issn>1467-9876</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNqFUE1v1DAQjRBILIUzFw4-IG5p_ZXEOULVD1BVpC2IozVxbPDixMGTBZZfj9NUy5GRxk_2vDd6fkXxktFTluuMybopW9XUp5QyxR8Vm-PL42JDqajKllfyafEMcUdzMSo3RXgHB4seRoIwTMES9H8s6e1s0-BHmH0ciYuJWJz9kK_jV9L5MQ4eApkgwbAwkbgUB9LDDAT33c6amcyRDB5NAETvvLnf9Lx44iCgffGAJ8Xny4tP59flzcer9-dvb0pTMcZLBb01vel6KRvbVAaEA17ZWjAGrXQOWg4Nd52T0ipmKAdpetZz0XfMdQzESfFm3Tul-GOfrevFig0BRhv3qIVSUtVtm4lnK9GkiJis01PK30wHzaheUtVLhnrJUN-nmhUfVkWykzVHehdgFxOi0T-1ANnm45Cb55gz-Nws97QgazXjSn-bh7zs9YNPQAPBJRiNx38eBG0qWmeaXGm_fLCH_1nU27u789Xqq1W2wzmmo4zXiqpa5nG5jj3O9vdxDOm7rhvRVPrL7ZW-vm3U9rLZaiX-AuVPu1c</recordid><startdate>2000</startdate><enddate>2000</enddate><creator>Rahme, E.</creator><creator>Joseph, L.</creator><creator>Gyorkos, T. 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W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5112-8adecdcbd447e75ca3fa25e6311a94ffa92a72fbf44e81c02a4cd1d23db1fb1a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2000</creationdate><topic>A priori knowledge</topic><topic>Arithmetic mean</topic><topic>Average coverage criterion</topic><topic>Bayesian estimation</topic><topic>Bayesian method</topic><topic>Binomial distribution</topic><topic>Binomials</topic><topic>Classification</topic><topic>Data collection</topic><topic>Decision theory</topic><topic>Diagnostic test</topic><topic>Disease prevalence rates</topic><topic>Distribution</topic><topic>Error rates</topic><topic>Estimate reliability</topic><topic>Estimation</topic><topic>Exact sciences and technology</topic><topic>Interval estimators</topic><topic>Mathematics</topic><topic>Medical diagnostic tests</topic><topic>Misclassification</topic><topic>Parametric inference</topic><topic>Pregnancy</topic><topic>Prevalence</topic><topic>Probability and statistics</topic><topic>Regression analysis</topic><topic>Sample size</topic><topic>Samples</topic><topic>Sciences and techniques of general use</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rahme, E.</creatorcontrib><creatorcontrib>Joseph, L.</creatorcontrib><creatorcontrib>Gyorkos, T. 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W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bayesian sample size determination for estimating binomial parameters from data subject to misclassification</atitle><jtitle>Applied statistics</jtitle><date>2000</date><risdate>2000</risdate><volume>49</volume><issue>1</issue><spage>119</spage><epage>128</epage><pages>119-128</pages><issn>0035-9254</issn><eissn>1467-9876</eissn><coden>APSTAG</coden><abstract>We investigate the sample size problem when a binomial parameter is to be estimated, but some degree of misclassification is possible. The problem is especially challenging when the degree to which misclassification occurs is not exactly known. Motivated by a Canadian survey of the prevalence of toxoplasmosis infection in pregnant women, we examine the situation where it is desired that a marginal posterior credible interval for the prevalence of width w has coverage 1-α, using a Bayesian sample size criterion. 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| ispartof | Applied statistics, 2000, Vol.49 (1), p.119-128 |
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| source | RePEc; JSTOR Mathematics & Statistics; EBSCOhost Business Source Complete; Access via Wiley Online Library; JSTOR Archive Collection A-Z Listing; Oxford University Press Journals All Titles (1996-Current) |
| subjects | A priori knowledge Arithmetic mean Average coverage criterion Bayesian estimation Bayesian method Binomial distribution Binomials Classification Data collection Decision theory Diagnostic test Disease prevalence rates Distribution Error rates Estimate reliability Estimation Exact sciences and technology Interval estimators Mathematics Medical diagnostic tests Misclassification Parametric inference Pregnancy Prevalence Probability and statistics Regression analysis Sample size Samples Sciences and techniques of general use Statistics |
| title | Bayesian sample size determination for estimating binomial parameters from data subject to misclassification |
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