Advanced Field Epi:Manual 2 - Diagnostic Tests - Revision history

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← Revisi sebelumnya		Revisi per 10 Mei 2015 04.23
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	= Diagnostic tests = <!--T:1-->		= Diagnostic tests = <!--T:1-->

@@ Baris 512: / Baris 512: @@
 <!--T:127-->
-A comparison of two herd-tests for Johne's disease in sheep yields the following results (from Sergeant et al., ([#_ENREF_18 2002])):
+A comparison of two herd-tests for Johne's disease in sheep yields the following results (from Sergeant et al., ([#18 2002])):
 {| style="border-spacing:0;"
@@ Baris 558: / Baris 558: @@
 <!--T:137-->
 === Proportional agreement of positive and negative results ===
-In some circumstances, particularly where the marginal totals of the 2-by-2 table are not balanced, ''kappa'' is not always a good measure of the true level of agreement between two tests ([#_ENREF_6 Feinstein and Cicchetti, 1990]). For example, in the first example above, kappa was only 0.74, compared to an overall proportion of agreement of 0.94 In these situations, the proportions of positive and negative agreement have been proposed as useful alternatives to ''kappa'' ([#_ENREF_3 Cicchetti and Feinstein, 1990]). For this example, the proportion of positive agreement was 0.78, compared to 0.96 for the proportion of negative agreement, suggesting that the main area of disagreement between the tests is in positive results and that agreement among negatives is very high.
+In some circumstances, particularly where the marginal totals of the 2-by-2 table are not balanced, ''kappa'' is not always a good measure of the true level of agreement between two tests ([#6 Feinstein and Cicchetti, 1990]). For example, in the first example above, kappa was only 0.74, compared to an overall proportion of agreement of 0.94 In these situations, the proportions of positive and negative agreement have been proposed as useful alternatives to ''kappa'' ([#3 Cicchetti and Feinstein, 1990]). For this example, the proportion of positive agreement was 0.78, compared to 0.96 for the proportion of negative agreement, suggesting that the main area of disagreement between the tests is in positive results and that agreement among negatives is very high.
 == Estimation of true prevalence from apparent prevalence == <!--T:138-->
-When we apply a test in a population, the proportion of positive results observed is the apparent prevalence. However, depending on test performance, apparent prevalence may not be a good indicator of the true level of disease in the population (the true prevalence). However, if we can estimate the sensitivity and specificity of the test, we can also estimate the true prevalence from the apparent (test-positive) prevalence (AP) using the formula ([#_ENREF_16 Rogan and Gladen, 1978]):
+When we apply a test in a population, the proportion of positive results observed is the apparent prevalence. However, depending on test performance, apparent prevalence may not be a good indicator of the true level of disease in the population (the true prevalence). However, if we can estimate the sensitivity and specificity of the test, we can also estimate the true prevalence from the apparent (test-positive) prevalence (AP) using the formula ([#16 Rogan and Gladen, 1978]):
@@ Baris 625: / Baris 625: @@
 === Calculating herd sensitivity and herd specificity === <!--T:157-->
-The herd-level sensitivity (''SeH'') and specificity (''SpH'') with a cut-off of 1 reactor to declare a herd infected can be calculated as ([#_ENREF_13 Martin et al., 1992]):
+The herd-level sensitivity (''SeH'') and specificity (''SpH'') with a cut-off of 1 reactor to declare a herd infected can be calculated as ([#13 Martin et al., 1992]):
 <!--T:158-->
@@ Baris 799: / Baris 799: @@
 <!--T:205-->
-Regardless of the methods used for estimating sensitivity and specificity, a number of important principles must be considered when evaluating tests, as for any other epidemiological study ([#_ENREF_8 Greiner and Gardner, 2000]):
+Regardless of the methods used for estimating sensitivity and specificity, a number of important principles must be considered when evaluating tests, as for any other epidemiological study ([#8 Greiner and Gardner, 2000]):
 <!--T:206-->
@@ Baris 824: / Baris 824: @@
 <!--T:212-->
-Gold-standard methods for estimating sensitivity and specificity of diagnostic tests and their limitations are discussed in more detail by Greiner and Gardner ([#_ENREF_8 2000]).
+Gold-standard methods for estimating sensitivity and specificity of diagnostic tests and their limitations are discussed in more detail by Greiner and Gardner ([#8 2000]).
@@ Baris 843: / Baris 843: @@
 <!--T:218-->
-Although these methods don't rely on a gold standard for comparison, they do depend on a number of important assumptions. Violation of these assumptions could render the resulting estimates invalid. Non-gold-standard methods for estimating sensitivity and specificity of diagnostic tests have been described in more detail by Hui and Walter ([#_ENREF_9 1980]), Staquet et al. ([#_ENREF_19 1981]) and Enøe et al. ([#_ENREF_4 2000]).
+Although these methods don't rely on a gold standard for comparison, they do depend on a number of important assumptions. Violation of these assumptions could render the resulting estimates invalid. Non-gold-standard methods for estimating sensitivity and specificity of diagnostic tests have been described in more detail by Hui and Walter ([#9 1980]), Staquet et al. ([#19 1981]) and Enøe et al. ([#4 2000]).
 <!--T:219-->
@@ Baris 849: / Baris 849: @@
 ==== Maximum likelihood estimation  ==== <!--T:220-->
-Maximum likelihood methods use standard statistical methods to estimate sensitivity and specificity of multiple tests from a comparison of the results of multiple tests applied to the same individuals in multiple populations with different prevalence levels ([#_ENREF_9 Hui and Walter, 1980]; [#_ENREF_4 EnÃ¸e et al., 2000]; [#_ENREF_15 Pouillot et al., 2002]). Key assumptions for this approach are:
+Maximum likelihood methods use standard statistical methods to estimate sensitivity and specificity of multiple tests from a comparison of the results of multiple tests applied to the same individuals in multiple populations with different prevalence levels ([#9 Hui and Walter, 1980]; [#4 EnÃ¸e et al., 2000]; [#15 Pouillot et al., 2002]). Key assumptions for this approach are:
 <!--T:221-->
@@ Baris 858: / Baris 858: @@
 ==== Bayesian estimation ==== <!--T:222-->
-Bayesian methods have been developed that allow the estimation of sensitivity and specificity of one or two tests that are compared in single or multiple populations ([#_ENREF_11 Joseph et al., 1995]; [#_ENREF_4 Enøe et al., 2000]; [#_ENREF_10 Johnson et al., 2001]; [#_ENREF_2 Branscum et al., 2005]). These methods allow incorporation of any prior knowledge on the likely sensitivity and specificity of the test(s) and of disease prevalence as probability distributions, expressing any uncertainty about the assumed prior values. Methods are also available for evaluation of correlated tests, but these require inclusion of additional tests and/or populations to ensure that the Bayesian model works properly ([#_ENREF_7 Georgiadis et al., 2003]).
+Bayesian methods have been developed that allow the estimation of sensitivity and specificity of one or two tests that are compared in single or multiple populations ([#11 Joseph et al., 1995]; [#4 Enøe et al., 2000]; [#10 Johnson et al., 2001]; [#2 Branscum et al., 2005]). These methods allow incorporation of any prior knowledge on the likely sensitivity and specificity of the test(s) and of disease prevalence as probability distributions, expressing any uncertainty about the assumed prior values. Methods are also available for evaluation of correlated tests, but these require inclusion of additional tests and/or populations to ensure that the Bayesian model works properly ([#7 Georgiadis et al., 2003]).
 <!--T:223-->
@@ Baris 864: / Baris 864: @@
 ==== Comparison with a known reference test ==== <!--T:224-->
-Sensitivity and specificity can also be estimated by comparison with a reference test of known sensitivity and/or specificity ([#_ENREF_19 Staquet et al., 1981]). These methods cover a variety of circumstances, depending on whether sensitivity or specificity or both are known for the reference test. Key assumptions are conditional independence of tests, and that the sensitivity and/or specificity of the reference test is known.
+Sensitivity and specificity can also be estimated by comparison with a reference test of known sensitivity and/or specificity ([#19 Staquet et al., 1981]). These methods cover a variety of circumstances, depending on whether sensitivity or specificity or both are known for the reference test. Key assumptions are conditional independence of tests, and that the sensitivity and/or specificity of the reference test is known.
 <!--T:225-->
@@ Baris 876: / Baris 876: @@
 ==== Estimation from routine testing data ==== <!--T:228-->
-Where a disease is rare, and truly infected animals can be eliminated from the data, it is possible to estimate test specificity from routine testing results, such as in a disease control program ([#_ENREF_17 Seiler, 1979]). In this situation, test-positives are routinely subject to follow-up, so that truly infected animals are identified and removed from the population. It is also possible to identify and exclude tests from known infected herds or flocks. Specificity can then be estimated as:
+Where a disease is rare, and truly infected animals can be eliminated from the data, it is possible to estimate test specificity from routine testing results, such as in a disease control program ([#17 Seiler, 1979]). In this situation, test-positives are routinely subject to follow-up, so that truly infected animals are identified and removed from the population. It is also possible to identify and exclude tests from known infected herds or flocks. Specificity can then be estimated as:
 <!--T:229-->
@@ Baris 885: / Baris 885: @@
 <!--T:231-->
-For example: The flock-specificity of pooled faecal culture for the detection of ovine Johne's disease was estimated from laboratory testing records in New South Wales ([#_ENREF_18 Sergeant et al., 2002]). In this analysis, there were nine test-positive flocks out of 227 flocks eligible for inclusion in the analysis. After exclusion of results for seven known infected flocks, there were 2/220 flocks positive, resulting in an estimated minimum flock-specificity of 99.1% (95% Binomial CI: 96.9% - 99.9%). In fact one or both of these flocks could have been infected, and the true flock-specificity could be higher than the estimate of 99.1%.
+For example: The flock-specificity of pooled faecal culture for the detection of ovine Johne's disease was estimated from laboratory testing records in New South Wales ([#18 Sergeant et al., 2002]). In this analysis, there were nine test-positive flocks out of 227 flocks eligible for inclusion in the analysis. After exclusion of results for seven known infected flocks, there were 2/220 flocks positive, resulting in an estimated minimum flock-specificity of 99.1% (95% Binomial CI: 96.9% - 99.9%). In fact one or both of these flocks could have been infected, and the true flock-specificity could be higher than the estimate of 99.1%.
 ==== Modelling approaches ==== <!--T:232-->
@@ Baris 897: / Baris 897: @@
 <!--T:235-->
-For example, this approach was used to estimate sensitivity and specificity for ELISA for ''Toxoplasma gondii'' infection in Dutch sheep ([#_ENREF_14 Opsteegh et al., 2010]). ELISA results from 1,179 serum samples collected from sheep at slaughterhouses in the Netherlands were log transformed and normal distributions fitted to the infected and uninfected components. The resulting theoretical distributions allowed determination of a suitable cut-point with estimated sensitivity of 97.8% and specificity of 96.4%.
+For example, this approach was used to estimate sensitivity and specificity for ELISA for ''Toxoplasma gondii'' infection in Dutch sheep ([#14 Opsteegh et al., 2010]). ELISA results from 1,179 serum samples collected from sheep at slaughterhouses in the Netherlands were log transformed and normal distributions fitted to the infected and uninfected components. The resulting theoretical distributions allowed determination of a suitable cut-point with estimated sensitivity of 97.8% and specificity of 96.4%.
 <!--T:236-->
@@ Baris 903: / Baris 903: @@
 ==== Simulation modelling of longitudinal testing results ==== <!--T:237-->
-An alternative approach, using simulation modelling, has been used where no comparative test data was available, but results of repeated testing over time were available. In this example, the sensitivity of an ELISA for bovine Johne's disease was estimated from repeated herd-testing results over a 10-year period using a simulation model. Age-specific data from up to 7 annual tests in 542 dairy herds were used to estimate ELISA sensitivity at the first-round test. The total number of infected animals present at the first test was estimated from the number of reactors detected at that test, plus the estimated number of animals that failed to react at that test, but reacted (or would have reacted if they had not died or been previously culled) at a subsequent test, based on reactor rates at subsequent tests. Reactor rates were adjusted for an assumed ELISA specificity of 99.8% to ensure estimates were not biased by imperfect ELISA specificity ([#_ENREF_12 Jubb et al., 2004]). Age-specific estimates of ELISA sensitivity ranged from 1.2% in 2-year-old cattle to 30.8% in 10-year-old cattle, with an overall age-weighted average of 13.5%.
+An alternative approach, using simulation modelling, has been used where no comparative test data was available, but results of repeated testing over time were available. In this example, the sensitivity of an ELISA for bovine Johne's disease was estimated from repeated herd-testing results over a 10-year period using a simulation model. Age-specific data from up to 7 annual tests in 542 dairy herds were used to estimate ELISA sensitivity at the first-round test. The total number of infected animals present at the first test was estimated from the number of reactors detected at that test, plus the estimated number of animals that failed to react at that test, but reacted (or would have reacted if they had not died or been previously culled) at a subsequent test, based on reactor rates at subsequent tests. Reactor rates were adjusted for an assumed ELISA specificity of 99.8% to ensure estimates were not biased by imperfect ELISA specificity ([#12 Jubb et al., 2004]). Age-specific estimates of ELISA sensitivity ranged from 1.2% in 2-year-old cattle to 30.8% in 10-year-old cattle, with an overall age-weighted average of 13.5%.
 <!--T:238-->

@@ Baris 858: / Baris 858: @@
 ==== Bayesian estimation ==== <!--T:222-->
-Bayesian methods have been developed that allow the estimation of sensitivity and specificity of one or two tests that are compared in single or multiple populations ([#_ENREF_11 Joseph et al., 1995]; [#_ENREF_4 EnÃ¸e et al., 2000]; [#_ENREF_10 Johnson et al., 2001]; [#_ENREF_2 Branscum et al., 2005]). These methods allow incorporation of any prior knowledge on the likely sensitivity and specificity of the test(s) and of disease prevalence as probability distributions, expressing any uncertainty about the assumed prior values. Methods are also available for evaluation of correlated tests, but these require inclusion of additional tests and/or populations to ensure that the Bayesian model works properly ([#_ENREF_7 Georgiadis et al., 2003]).
+Bayesian methods have been developed that allow the estimation of sensitivity and specificity of one or two tests that are compared in single or multiple populations ([#_ENREF_11 Joseph et al., 1995]; [#_ENREF_4 Enøe et al., 2000]; [#_ENREF_10 Johnson et al., 2001]; [#_ENREF_2 Branscum et al., 2005]). These methods allow incorporation of any prior knowledge on the likely sensitivity and specificity of the test(s) and of disease prevalence as probability distributions, expressing any uncertainty about the assumed prior values. Methods are also available for evaluation of correlated tests, but these require inclusion of additional tests and/or populations to ensure that the Bayesian model works properly ([#_ENREF_7 Georgiadis et al., 2003]).
 <!--T:223-->

@@ Baris 840: / Baris 840: @@
 <!--T:217-->
-'''''Non gold-standard '''test evaluation makes no explicit assumptions about the disease state of the animals tested and relies on statistical methods to determine the most likely values for test sensitivity and/or specificity''
+{{hlbox|'''''Non gold-standard '''test evaluation makes no explicit assumptions about the disease state of the animals tested and relies on statistical methods to determine the most likely values for test sensitivity and/or specificity''}}
 <!--T:218-->
-Although these methods don't rely on a gold standard for comparison, they do depend on a number of important assumptions. Violation of these assumptions could render the resulting estimates invalid. Non-gold-standard methods for estimating sensitivity and specificity of diagnostic tests have been described in more detail by Hui and Walter ([#_ENREF_9 1980]), Staquet et al. ([#_ENREF_19 1981]) and EnÃ¸e et al. ([#_ENREF_4 2000]).
+Although these methods don't rely on a gold standard for comparison, they do depend on a number of important assumptions. Violation of these assumptions could render the resulting estimates invalid. Non-gold-standard methods for estimating sensitivity and specificity of diagnostic tests have been described in more detail by Hui and Walter ([#_ENREF_9 1980]), Staquet et al. ([#_ENREF_19 1981]) and Enøe et al. ([#_ENREF_4 2000]).
 <!--T:219-->
@@ Baris 849: / Baris 849: @@
 ==== Maximum likelihood estimation  ==== <!--T:220-->
-Maximum likelihood methods use standard statistical methods to estimate sensitivity and specificity of multiple tests from a comparison of the results of multiple tests applied to the same individuals in multiple populations with different prevalence levels ([#_ENREF_9 Hui and Walter, 1980]<nowiki>; </nowiki>[#_ENREF_4 EnÃ¸e et al., 2000]<nowiki>; </nowiki>[#_ENREF_15 Pouillot et al., 2002]). Key assumptions for this approach are:
+Maximum likelihood methods use standard statistical methods to estimate sensitivity and specificity of multiple tests from a comparison of the results of multiple tests applied to the same individuals in multiple populations with different prevalence levels ([#_ENREF_9 Hui and Walter, 1980]; [#_ENREF_4 EnÃ¸e et al., 2000]; [#_ENREF_15 Pouillot et al., 2002]). Key assumptions for this approach are:
 <!--T:221-->
-* <nowiki>The tests are independent, conditional on disease status (the sensitivity [specificity] of one test is the same, regardless of the result of the other test, as discussed in more detail in the section on series and parallel interpretation of tests);</nowiki>
+* The tests are independent, conditional on disease status (the sensitivity [specificity] of one test is the same, regardless of the result of the other test, as discussed in more detail in the section on series and parallel interpretation of tests);
 * Test sensitivity and specificity are constant across populations;
 * The tests are compared in two or more populations with different prevalence between populations; and
@@ Baris 858: / Baris 858: @@
 ==== Bayesian estimation ==== <!--T:222-->
-Bayesian methods have been developed that allow the estimation of sensitivity and specificity of one or two tests that are compared in single or multiple populations ([#_ENREF_11 Joseph et al., 1995]<nowiki>; </nowiki>[#_ENREF_4 EnÃ¸e et al., 2000]<nowiki>; </nowiki>[#_ENREF_10 Johnson et al., 2001]<nowiki>; </nowiki>[#_ENREF_2 Branscum et al., 2005]). These methods allow incorporation of any prior knowledge on the likely sensitivity and specificity of the test(s) and of disease prevalence as probability distributions, expressing any uncertainty about the assumed prior values. Methods are also available for evaluation of correlated tests, but these require inclusion of additional tests and/or populations to ensure that the Bayesian model works properly ([#_ENREF_7 Georgiadis et al., 2003]).
+Bayesian methods have been developed that allow the estimation of sensitivity and specificity of one or two tests that are compared in single or multiple populations ([#_ENREF_11 Joseph et al., 1995]; [#_ENREF_4 EnÃ¸e et al., 2000]; [#_ENREF_10 Johnson et al., 2001]; [#_ENREF_2 Branscum et al., 2005]). These methods allow incorporation of any prior knowledge on the likely sensitivity and specificity of the test(s) and of disease prevalence as probability distributions, expressing any uncertainty about the assumed prior values. Methods are also available for evaluation of correlated tests, but these require inclusion of additional tests and/or populations to ensure that the Bayesian model works properly ([#_ENREF_7 Georgiadis et al., 2003]).
 <!--T:223-->

@@ Baris 812: / Baris 812: @@
 <!--T:208-->
-'''''Gold standard '''test evaluation assumes comparison with the true disease status of an animal based on the results of a test (or tests) with perfect sensitivity and/or specificity''
+{{hlbox|'''''Gold standard '''test evaluation assumes comparison with the true disease status of an animal based on the results of a test (or tests) with perfect sensitivity and/or specificity''}}
 <!--T:209-->
@@ Baris 834: / Baris 834: @@
 <!--T:215-->
 Two drawbacks of this approach are: firstly that you cannot estimate sensitivity in this sample, since none of the animals are infected; and secondly that by using a defined (often geographically isolated) population there is a risk that specificity may be different in this population to what might be the case in the target population where the test is to be used.
 === Non-gold-standard methods === <!--T:216-->

@@ Baris 796: / Baris 796: @@
 <!--T:204-->
-â€œGold standardâ€ methods rely on the classification of individuals using a reference test (or tests) with perfect sensitivity and/or specificity to identify groups of diseased and non-diseased individuals in which the test can be evaluated. In contrast, â€œNon-gold-standardâ€ methods are used in situations where determination of the true infection status of each individual is not possible or economically feasible.
+"Gold standard" methods rely on the classification of individuals using a reference test (or tests) with perfect sensitivity and/or specificity to identify groups of diseased and non-diseased individuals in which the test can be evaluated. In contrast, "Non-gold-standard" methods are used in situations where determination of the true infection status of each individual is not possible or economically feasible.
 <!--T:205-->
@@ Baris 815: / Baris 815: @@
 <!--T:209-->
-For example: The â€œgold-standardâ€ test for bovine spongiform encephalopathy (BSE) is the demonstration of typical histological lesions in the brain of affected animals. However, false-negative results on histology will occur in animals in an early stage of infection. Therefore, if a screening test is evaluated by comparison with histology, specificity will be underestimated because some infected animals could react to the screening test but be histologically negative, resulting in mis-classification as false-positives. In addition, any infected but histologically-negative animals that are negative on the screening test will be mis-classified as true-negatives, resulting in over-estimation of the sensitivity.
+For example: The "gold-standard" test for bovine spongiform encephalopathy (BSE) is the demonstration of typical histological lesions in the brain of affected animals. However, false-negative results on histology will occur in animals in an early stage of infection. Therefore, if a screening test is evaluated by comparison with histology, specificity will be underestimated because some infected animals could react to the screening test but be histologically negative, resulting in mis-classification as false-positives. In addition, any infected but histologically-negative animals that are negative on the screening test will be mis-classified as true-negatives, resulting in over-estimation of the sensitivity.
 <!--T:210-->
-If a disease is rare, or if the â€œgold standardâ€ test is complex and expensive to perform, sample sizes for estimation of sensitivity are likely to be small, leading to imprecise estimates of sensitivity. If a disease does not occur in a country it is impossible to estimate sensitivity in a sample that is representative of the population in which it is to be applied. Conversely, if a disease does not occur in a country or region, it is relatively easy to estimate test specificity, based on a representative sample of animals from the population, because if the population is free of disease all animals in the population must also be disease-free.
+If a disease is rare, or if the "gold standard" test is complex and expensive to perform, sample sizes for estimation of sensitivity are likely to be small, leading to imprecise estimates of sensitivity. If a disease does not occur in a country it is impossible to estimate sensitivity in a sample that is representative of the population in which it is to be applied. Conversely, if a disease does not occur in a country or region, it is relatively easy to estimate test specificity, based on a representative sample of animals from the population, because if the population is free of disease all animals in the population must also be disease-free.
 <!--T:211-->
-Sometimes a new test may appear to be more sensitive (or specific) than the existing â€œgold standardâ€ test (for example, new DNA-based tests compared to conventional culture). In this situation, the new test will find more (or fewer) positives than the reference test and careful analysis is required to determine whether this is because it is more sensitive or less specific. Even then, it is often not possible to reliably estimate sensitivity or specificity because there is no fixed reference point, so it may only be possible to say that the new test is more sensitive (or specific) than the old test, without specifying a value.
+Sometimes a new test may appear to be more sensitive (or specific) than the existing "gold standard" test (for example, new DNA-based tests compared to conventional culture). In this situation, the new test will find more (or fewer) positives than the reference test and careful analysis is required to determine whether this is because it is more sensitive or less specific. Even then, it is often not possible to reliably estimate sensitivity or specificity because there is no fixed reference point, so it may only be possible to say that the new test is more sensitive (or specific) than the old test, without specifying a value.
 <!--T:212-->
@@ Baris 844: / Baris 844: @@
 <!--T:218-->
-Although these methods donâ€™t rely on a gold standard for comparison, they do depend on a number of important assumptions. Violation of these assumptions could render the resulting estimates invalid. Non-gold-standard methods for estimating sensitivity and specificity of diagnostic tests have been described in more detail by Hui and Walter ([#_ENREF_9 1980]), Staquet et al. ([#_ENREF_19 1981]) and EnÃ¸e et al. ([#_ENREF_4 2000]).
+Although these methods don't rely on a gold standard for comparison, they do depend on a number of important assumptions. Violation of these assumptions could render the resulting estimates invalid. Non-gold-standard methods for estimating sensitivity and specificity of diagnostic tests have been described in more detail by Hui and Walter ([#_ENREF_9 1980]), Staquet et al. ([#_ENREF_19 1981]) and EnÃ¸e et al. ([#_ENREF_4 2000]).
 <!--T:219-->
@@ Baris 886: / Baris 886: @@
 <!--T:231-->
-For example: The flock-specificity of pooled faecal culture for the detection of ovine Johneâ€™s disease was estimated from laboratory testing records in New South Wales ([#_ENREF_18 Sergeant et al., 2002]). In this analysis, there were nine test-positive flocks out of 227 flocks eligible for inclusion in the analysis. After exclusion of results for seven known infected flocks, there were 2/220 flocks positive, resulting in an estimated minimum flock-specificity of 99.1% (95% Binomial CI: 96.9% - 99.9%). In fact one or both of these flocks could have been infected, and the true flock-specificity could be higher than the estimate of 99.1%.
+For example: The flock-specificity of pooled faecal culture for the detection of ovine Johne's disease was estimated from laboratory testing records in New South Wales ([#_ENREF_18 Sergeant et al., 2002]). In this analysis, there were nine test-positive flocks out of 227 flocks eligible for inclusion in the analysis. After exclusion of results for seven known infected flocks, there were 2/220 flocks positive, resulting in an estimated minimum flock-specificity of 99.1% (95% Binomial CI: 96.9% - 99.9%). In fact one or both of these flocks could have been infected, and the true flock-specificity could be higher than the estimate of 99.1%.
 ==== Modelling approaches ==== <!--T:232-->
@@ Baris 904: / Baris 904: @@
 ==== Simulation modelling of longitudinal testing results ==== <!--T:237-->
-An alternative approach, using simulation modelling, has been used where no comparative test data was available, but results of repeated testing over time were available. In this example, the sensitivity of an ELISA for bovine Johneâ€™s disease was estimated from repeated herd-testing results over a 10-year period using a simulation model. Age-specific data from up to 7 annual tests in 542 dairy herds were used to estimate ELISA sensitivity at the first-round test. The total number of infected animals present at the first test was estimated from the number of reactors detected at that test, plus the estimated number of animals that failed to react at that test, but reacted (or would have reacted if they had not died or been previously culled) at a subsequent test, based on reactor rates at subsequent tests. Reactor rates were adjusted for an assumed ELISA specificity of 99.8% to ensure estimates were not biased by imperfect ELISA specificity ([#_ENREF_12 Jubb et al., 2004]). Age-specific estimates of ELISA sensitivity ranged from 1.2% in 2-year-old cattle to 30.8% in 10-year-old cattle, with an overall age-weighted average of 13.5%.
+An alternative approach, using simulation modelling, has been used where no comparative test data was available, but results of repeated testing over time were available. In this example, the sensitivity of an ELISA for bovine Johne's disease was estimated from repeated herd-testing results over a 10-year period using a simulation model. Age-specific data from up to 7 annual tests in 542 dairy herds were used to estimate ELISA sensitivity at the first-round test. The total number of infected animals present at the first test was estimated from the number of reactors detected at that test, plus the estimated number of animals that failed to react at that test, but reacted (or would have reacted if they had not died or been previously culled) at a subsequent test, based on reactor rates at subsequent tests. Reactor rates were adjusted for an assumed ELISA specificity of 99.8% to ensure estimates were not biased by imperfect ELISA specificity ([#_ENREF_12 Jubb et al., 2004]). Age-specific estimates of ELISA sensitivity ranged from 1.2% in 2-year-old cattle to 30.8% in 10-year-old cattle, with an overall age-weighted average of 13.5%.
 <!--T:238-->

@@ Baris 772: / Baris 772: @@
-=== Demonstrate freedom or detecting disease? === <!--T:199-->
+=== Demonstrate freedom of detecting disease? === <!--T:199-->
-It is impossible to prove that a population is free from a particular disease without testing every individual with a perfect test. However, demonstrating "freedom" from disease in a population is essentially the same as sampling to provide a high level of confidence of detecting disease at specified (design) prevalence. If we donâ€™t detect disease, then we can state that we have the appropriate level of confidence that (if the disease is present) it is at prevalence lower than the ''design prevalence''. Provided we have selected appropriate design prevalence, it can then be argued that if the disease were present it would more than likely be at a higher level than the design prevalence, and therefore we can be confident that the population is probably free of the disease.
+It is impossible to prove that a population is free from a particular disease without testing every individual with a perfect test. However, demonstrating "freedom" from disease in a population is essentially the same as sampling to provide a high level of confidence of detecting disease at specified (design) prevalence. If we don't detect disease, then we can state that we have the appropriate level of confidence that (if the disease is present) it is at prevalence lower than the ''design prevalence''. Provided we have selected appropriate design prevalence, it can then be argued that if the disease were present it would more than likely be at a higher level than the design prevalence, and therefore we can be confident that the population is probably free of the disease.
 <!--T:200-->
 The selection of appropriate design prevalence is obviously critical if it is too low sample sizes will be excessive, while if it is too high the argument that it is an appropriate threshold for detection of disease is weaker. For infectious diseases it is common to use a value equal to or lower than values observes in endemic or outbreak situations.
 === Important factors to consider in group testing === <!--T:201-->

@@ Baris 619: / Baris 619: @@
 <!--T:155-->
-'''''Herd sensitivity''' (SeH) is the probability that an infected herd will give a positive result to a particular testing protocol, given that it is infected at a prevalence equal to or greater than the specified design prevalence.''
+{{hlbox|'''''Herd sensitivity''' (SeH) is the probability that an infected herd will give a positive result to a particular testing protocol, given that it is infected at a prevalence equal to or greater than the specified design prevalence.''
+'''''Herd specificity''' (SpH) is the probability that an uninfected herd will give a negative result to a particular testing protocol (HSP)''}}
@@ Baris 630: / Baris 628: @@
 <!--T:158-->
-'''SeH''' = 1 - (1 - (Prevï‚´Se + (1 - Prev)ï‚´(1-Sp)))<sup>m</sup> and
+'''SeH''' = 1 - (1 - (Prev x Se + (1 - Prev) x (1-Sp)))<sup>m</sup> and
 <!--T:159-->
@@ Baris 642: / Baris 640: @@
 <!--T:162-->
-'''SeH '''<nowiki>= 1 - (1 - Prev</nowiki>ï‚´Se)<sup>m</sup>
+'''SeH ''' = 1 - (1 - Prev</nowiki> x Se)<sup>m</sup>
 <!--T:163-->
@@ Baris 696: / Baris 694: @@
 <!--T:180-->
-In the above example, increasing the cut-point number of reactors for a positive result from 1 to 2 (i.e. if there are 0 or 1 animals test positive the group is considered â€œuninfectedâ€ while if 2 or more test positive it is infected) results in an increase in SpH to 74% but a reduction in SeH to 77% (from EpiTools: [http://epitools.ausvet.com.au/content.php?page=HerdSens3 http://epitools.ausvet.com.au/content.php?page=HerdSens3]).
+In the above example, increasing the cut-point number of reactors for a positive result from 1 to 2 (i.e. if there are 0 or 1 animals test positive the group is considered "uninfected" while if 2 or more test positive it is infected) results in an increase in SpH to 74% but a reduction in SeH to 77% (from EpiTools: [http://epitools.ausvet.com.au/content.php?page=HerdSens3 http://epitools.ausvet.com.au/content.php?page=HerdSens3]).
 <!--T:181-->
@@ Baris 706: / Baris 704: @@
 <!--T:183-->
-'''Table 4.: Number of diseased or infected animals which could remain in a group of 100,000 after a small number are tested and found to be negative using a test which has perfect sensitivity and specificity at the individual animal level for 95% and 99% confidence levels'''
+'''Table 4.3: Number of diseased or infected animals which could remain in a group of 100,000 after a small number are tested and found to be negative using a test which has perfect sensitivity and specificity at the individual animal level for 95% and 99% confidence levels'''
@@ Baris 715: / Baris 713: @@
 | style="border-top:0.75pt solid #00000a;border-bottom:0.75pt solid #00000a;border-left:none;border-right:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| <center>99%</center>
 |-
 | style="border-top:0.75pt solid #00000a;border-bottom:none;border-left:none;border-right:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| <center>100</center>
@@ Baris 721: / Baris 718: @@
 | style="border-top:0.75pt solid #00000a;border-bottom:none;border-left:none;border-right:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| <center>4,499</center>
 |-
 | style="border:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| <center>500</center>
@@ Baris 727: / Baris 723: @@
 | style="border:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| <center>915</center>
 |-
 | style="border:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| <center>1,000</center>
@@ Baris 733: / Baris 728: @@
 | style="border:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| <center>458</center>
 |-
 | style="border-top:none;border-bottom:0.75pt solid #00000a;border-left:none;border-right:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| <center>10,000</center>
 | style="border-top:none;border-bottom:0.75pt solid #00000a;border-left:none;border-right:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| <center>29</center>
 | style="border-top:none;border-bottom:0.75pt solid #00000a;border-left:none;border-right:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| <center>44</center>
 <!--T:189-->
 The situation is further complicated where the test procedure being used has poor sensitivity, which is the case for many tests in regular use.
@@ Baris 747: / Baris 742: @@
 {| style="border-spacing:0;"
 | style="background-color:#f2f2f2;border:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| '''Probability '''
-| style="background-color:#f2f2f2;border:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| <nowiki>= 1 - NPV</nowiki><sup>m</sup>
+| style="background-color:#f2f2f2;border:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| = 1 - NPV<sup>m</sup>
 |-
 | style="background-color:#f2f2f2;border:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"|
-| style="background-color:#f2f2f2;border:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| <nowiki>= 1 - [(1-Prev) </nowiki>ï‚´ Sp/((1-Prev) ï‚´ Sp + Prev ï‚´ (1-Se))]<sup>m</sup>
+| style="background-color:#f2f2f2;border:none;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"| = 1 - [(1-Prev)  x  Sp/((1-Prev)  x  Sp + Prev  x  (1-Se))]<sup>m</sup>
 <!--T:193-->
-|}
+Where '''''NPV'' '''is the negative predictive value of the test in the population of origin, '''''Se''''' and '''''Sp '''''are animal-level sensitivity and specificity respectively, '''''Prev''''' is true disease prevalence and '''''m '''''is the number of animals tested. As sample size increases the probability that the group will all test negative decreases, so that the overall risk associated with a group can be reduced by increasing the sample size. However, if all animals do test negatively the probability that one or more are actually infected increases (assuming that they are from an infected population), as shown in Figure 4.6.
 <!--T:194-->
@@ Baris 772: / Baris 766: @@
 <!--T:197-->
-[[Image:]]
+[[Image:Effect of sample size on the probability.jpg]]
 <!--T:198-->
@@ Baris 779: / Baris 773: @@
 === Demonstrate freedom or detecting disease? === <!--T:199-->
-It is impossible to prove that a population is free from a particular disease without testing every individual with a perfect test. However, demonstrating â€œfreedomâ€ from disease in a population is essentially the same as sampling to provide a high level of confidence of detecting disease at specified (design) prevalence. If we donâ€™t detect disease, then we can state that we have the appropriate level of confidence that (if the disease is present) it is at prevalence lower than the ''design prevalence''. Provided we have selected appropriate design prevalence, it can then be argued that if the disease were present it would more than likely be at a higher level than the design prevalence, and therefore we can be confident that the population is probably free of the disease.
+It is impossible to prove that a population is free from a particular disease without testing every individual with a perfect test. However, demonstrating "freedom" from disease in a population is essentially the same as sampling to provide a high level of confidence of detecting disease at specified (design) prevalence. If we donâ€™t detect disease, then we can state that we have the appropriate level of confidence that (if the disease is present) it is at prevalence lower than the ''design prevalence''. Provided we have selected appropriate design prevalence, it can then be argued that if the disease were present it would more than likely be at a higher level than the design prevalence, and therefore we can be confident that the population is probably free of the disease.
 <!--T:200-->
@@ Baris 790: / Baris 784: @@
 <!--T:202-->
 * Individual and group level test characteristics (sensitivity and specificity) are not equivalent.
-* <nowiki>The number of animals to be tested in the group (sample size) is relatively independent of group size except for small groups (<~1000) or where sample size is more than about 10% of the group size. Alternative methods are available for small populations or where sample size is large relative to group size.</nowiki>
+* The number of animals to be tested in the group (sample size) is relatively independent of group size except for small groups (<~1000) or where sample size is more than about 10% of the group size. Alternative methods are available for small populations or where sample size is large relative to group size.
 * The number of animals required to be tested in the group depends much more on individual animal specificity than it does on sensitivity.
 * The number of animals to be tested in the group is linearly and inversely related to the expected prevalence of infected animals in the group.

@@ Baris 349: / Baris 349: @@
 Observed sensitivities and specificities of the two tests used in parallel or series are:
-<!--T:89-->
+{|
-Se<sub>series</sub> = 70/200 = 35%Se<sub>parallel</sub> = 150/200 = 75%
+|-
+| Se<sub>series</sub> = 70/200 = 35%||  Se<sub>parallel</sub> = 150/200 = 75%
-<!--T:90-->
+|-
-Sp<sub>series</sub> = 7770/7800 = 99.6%Sp<sub>parallel</sub> = 7620/7800 = 97.7%
+| Sp<sub>series</sub> = 7770/7800 = 99.6%||  Sp<sub>parallel</sub> = 7620/7800 = 97.7%
 <!--T:91-->
@@ Baris 369: / Baris 371: @@
 <!--T:95-->
-For example: ELISA and AGID are two serological tests for Johneâ€™s disease in sheep. Both tests measure antibody levels in serum. Therefore, in an infected animal, the ELISA is more likely to be positive in AGID-positive animals than in AGID-negative animals, so that the sensitivities of the two tests are correlated (not independent). This is illustrated in Table xx.5, where the sensitivities of both tests vary markedly, depending on the result of the other test. In contrast, serological tests such as ELISA and AGID are likely to be less correlated with agent-detection tests, such as faecal culture.
+For example: ELISA and AGID are two serological tests for Johne's disease in sheep. Both tests measure antibody levels in serum. Therefore, in an infected animal, the ELISA is more likely to be positive in AGID-positive animals than in AGID-negative animals, so that the sensitivities of the two tests are correlated (not independent). This is illustrated in Table 4.5, where the sensitivities of both tests vary markedly, depending on the result of the other test. In contrast, serological tests such as ELISA and AGID are likely to be less correlated with agent-detection tests, such as faecal culture.
@@ Baris 424: / Baris 426: @@
 |}
 === Application of series and parallel testing === <!--T:105-->
 Series testing is commonly used to improve the specificity, and hence the positive predictive value, of a testing regimen (at the expense of reduced sensitivity).