Iowa State University College of Veterinary Medicine Principles of diagnostic sampling – the “ bead game” Pilot training school in PRRS diagnostics, 2012.

Iowa State University College of Veterinary Medicine

Principles of diagnostic sampling – the “ bead game”

Pilot training school in PRRS diagnostics, 2012 Zagreb, Croatia

Dr. Derald Holtkamp

September 25, 2012


Exercises

1. Detecting one or more positive pigs in an expected NEGATIVE population– Role of sample size and target prevalence

2. False positive test results in a truly negative population

3. Estimating Sensitivity (SE) & Specificity (SP) by testing "known status" samples (if time permits)


Exercises



3. Estimating Sensitivity (SE) & Specificity (SP) by testing "known status" samples


What you need to know to determine the sample size needed to find at least

one positive animal in a herd

1. Target prevalence (TP)2. Confidence level (CL)3. Population size (PS)

Sample size needed = f(TP, CL, PS) – Cannon RM, Roe RT. Livestock disease surveys: A field manual

for veterinarians. AGPS, Canberra, 1982.


Table of sample sizes needed based on Cannon and Roe

Prevalence Estimate

Confidence Level 100 1000 4000 10000

>1% 70% 71 114 120 12180% 81 149 158 16090% 91 206 224 22895% 96 259 289 29599% 100 369 434 449

>10% 70% 12 13 13 1380% 16 17 17 1790% 21 23 23 2395% 26 30 30 3099% 37 44 45 45

>25% 70% 6 6 6 680% 7 7 7 790% 9 10 10 1095% 11 12 12 1299% 16 17 18 18

>50% 70% 3 3 3 380% 4 4 4 490% 5 5 5 595% 6 6 6 699% 8 8 8 8

Population Size (Detecting One or More Positives)


Exercises





Slide courtesy of D. Polson


Exercises





Properties and Definitions of Diagnostic Tests

TRUE Positive TRUE Negative

Test Positive

a b

(true positive) (false positive)

Test Negative

c d

(false negative) (true negative)

Sensitivity = a / ( a + c ) False Negatives = c / ( a + c )

Specificity = d / ( b + d ) False Positives = b / ( b + d )

Positive Predictive Value = a / ( a + b ) Negative Predictive Value = d / ( c + d )

Accuracy = ( a + d ) / ( a + b + c + d )

True Prevalence = ( a + c ) / ( a + b + c + d ) Assay Prevalence = ( a + b ) / ( a + b + c + d )

Prevalence Estimate = a / ( a + b + c + d )

Likelihood Ratio of a Positive Test = Sensitivity / ( 1 - Specificity ) Likelihood Ratio of a Negative Test = ( 1 - Sensitivity ) / Specificity

Sensitivity is the percent of TRUE positives that are TEST positives. Specificity is the percent of TRUE negatives that are TEST negatives.

False Positive Rate is the percent of FALSE positives that are TEST positives. False Negative Rate is the percent of FALSE negatives that are TEST negatives.

Positive predictive value (PPV) is the probability of a test positive being a true positive. Negative predictive value (NPV) is the probability of a test negative being a true negative.

Accuracy is the percent of all test results that agree with the true status of the sample.

True Prevalence is the percent of all samples that are true positives. Prevalence Estimate is the percent of all samples that are tested positive.

Likelihood Ratio of a Positive Test is the odds of a positive test result in a true positive sample.

Likelihood Ratio of a Negative Test is the odds of a negative test result in a true negative sample.

Sensitivity and specificity are absolute properties of a test. PPV and NPV are relative, and vary depending on the true prevalence of positive samples. As the prevalence decreases, PPV decreases and NPV increases.


Acknowledgements• Dale Polson, Boehringer Ingelheim Vetmedica Inc., U.S

Iowa State University College of Veterinary Medicine Principles of diagnostic sampling – the “ bead game” Pilot training school in PRRS diagnostics, 2012.

Documents

population size ps sample

positive pigs

polson slide

roe slide

false positive test

positive animal

status samples

target prevalence tp