Translations:Advanced Field Epi:Manual 2 - Diagnostic Tests/150/en: Perbedaan revisi

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<nowiki>The above example illustrates one potential problem with Rogan and Gladen formula, which is that in some circumstances negative estimates can be produced. However, a negative (<0) prevalence is clearly impossible, so for this scenario the assumptions about sensitivity and specificity must be incorrect. For example, if specificity was 90% (0.9), and you tested 150 </nowiki>animals, you would expect to have 0.1*150 or on average about 15 false positive results (even in an uninfected population). Therefore if only 4 positives were recorded, the specificity of the test must be much higher than 90% (a minimum estimate would be to assume all of the positives are false positives, so that specificity = 1 – apparent prevalence = 1 – 4% or 96%).
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<nowiki>The above example illustrates one potential problem with Rogan and Gladen formula, which is that in some circumstances negative estimates can be produced. However, a negative (<0) prevalence is clearly impossible, so for this scenario the assumptions about sensitivity and specificity must be incorrect. For example, if specificity was 90% (0.9), and you tested 150 </nowiki>animals, you would expect to have 0.1*150 or on average about 15 false positive results (even in an uninfected population). Therefore if only 4 positives were recorded, the specificity of the test must be much higher than 90% (a minimum estimate would be to assume all of the positives are false positives, so that specificity = 1 - apparent prevalence = 1 - 4% or 96%).

Revisi terkini pada 10 Mei 2015 14.26

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<nowiki>The above example illustrates one potential problem with Rogan and Gladen formula, which is that in some circumstances negative estimates can be produced. However, a negative (<0) prevalence is clearly impossible, so for this scenario the assumptions about sensitivity and specificity must be incorrect. For example, if specificity was 90% (0.9), and you tested 150 </nowiki>animals, you would expect to have 0.1*150 or on average about 15 false positive results (even in an uninfected population). Therefore if only 4 positives were recorded, the specificity of the test must be much higher than 90% (a minimum estimate would be to assume all of the positives are false positives, so that specificity = 1 - apparent prevalence = 1 - 4% or 96%).
Terjemahan<nowiki>The above example illustrates one potential problem with Rogan and Gladen formula, which is that in some circumstances negative estimates can be produced. However, a negative (<0) prevalence is clearly impossible, so for this scenario the assumptions about sensitivity and specificity must be incorrect. For example, if specificity was 90% (0.9), and you tested 150 </nowiki>animals, you would expect to have 0.1*150 or on average about 15 false positive results (even in an uninfected population). Therefore if only 4 positives were recorded, the specificity of the test must be much higher than 90% (a minimum estimate would be to assume all of the positives are false positives, so that specificity = 1 - apparent prevalence = 1 - 4% or 96%).

The above example illustrates one potential problem with Rogan and Gladen formula, which is that in some circumstances negative estimates can be produced. However, a negative (<0) prevalence is clearly impossible, so for this scenario the assumptions about sensitivity and specificity must be incorrect. For example, if specificity was 90% (0.9), and you tested 150 animals, you would expect to have 0.1*150 or on average about 15 false positive results (even in an uninfected population). Therefore if only 4 positives were recorded, the specificity of the test must be much higher than 90% (a minimum estimate would be to assume all of the positives are false positives, so that specificity = 1 - apparent prevalence = 1 - 4% or 96%).

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