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Approaches to test evaluation

Evan Sergeant AusVet Animal Health Services. Approaches to test evaluation. Comparing tests. Kappa – how well tests agree McNemar’s chi-sq – are tests significantly different?. Kappa. Expected no. both +ve = (157 x 155)/1122 = 21.7 Expected no. both -ve = (965 x 967)/1122 = 831.6

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Approaches to test evaluation

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  1. Evan Sergeant AusVet Animal Health Services Approaches to test evaluation

  2. Comparing tests • Kappa – how well tests agree • McNemar’s chi-sq – are tests significantly different?

  3. Kappa • Expected no. both +ve = (157 x 155)/1122 = 21.7 • Expected no. both -ve = (965 x 967)/1122 = 831.6 • Total Agreement = 1052 • Chance Agreement = 853.4 • K=(1052-853.4)/(1122-853.4) = 0.739

  4. McNemar Chi-Squared McNemar's Chi-squared test with continuity correction McNemar's chi-squared = 22.881, df = 1, p-value = 1.724e-06

  5. OJD AGID and ELISA • Enter data into epitools • Application of diagnostic tests > compare 2 tests • see kappa, McNemar’s and level of agreement

  6. Gold Standard Tests • Use tests with perfect sensitivity and/or specificity to identify the true disease status of the individual from which the samples were taken. • What are the advantages and disadvantages of this approach?

  7. Gold Standards Tests • Advantages • Known disease status, • Relatively simple calculations • Disadvantages • May not exist, or be prohibitively expensive • Rare diseases may only allow small sample size • Disease may not be present in the country? • Difficult to get representative (or even comparable) samples of diseased/non-diseased individuals

  8. Exercises • Calculate Se and Sp for OJD AGID using data provided in OJD_AGID_Data.xls • Calculate confidence limits using epitools

  9. Non-gold standard methods • Do not depend on determining true infection status of individual. • Rely on statistical approaches to calculate best fit values for Se and Sp. • Tests must satisfy some important assumptions.

  10. Comparison with a knownreference test • Assumptions • Independence of tests • Se/Sp of reference test is known. • For ~100% specific reference test, • Se(new test) = Number positive both tests / Total number positive to the reference test

  11. Culture vs Serology • Estimate sensitivity of culture and serology (as flock tests) • Serology followed-up by histopathology to confirm flock status • Both tests 100% specificity (as flock tests) • How would you estimate sensitivity for these test(s) • Which test has better Se? Is the difference significant?

  12. Example • Se (PFC) = 58/63 = 92% (83% - 97%) • Se (Serology) = 58/95 = 61% (51% - 70%)

  13. Estimation from routine testing data • test-positives are subject to follow-up and truly infected animals are identified and removed from the population • Can be used to estimate specificity when the disease is rare in the population of interest. • Sp = 1 – (Number of reactors / Total number tested)

  14. Se and Sp of equine influenza ELISA • During the equine influenza outbreak in Australia, horses were tested by PCR and serology: • to confirm infection; • to demonstrate seroconversion and/or absence of infection >30 days later; • As part of random and targeted surveillance for case detection, to confirm area status and for zone progression in presumed “EI free” areas. • How could you use the resulting data to estimate sensitivity and specificity of the ELISA?

  15. Equine influenza ELISA • 475 PCR-positive horses, 471 also positive on ELISA • 1323 horses from properties in areas with no infection, 1280 ELISA negative • Analyse in Epitools • Application of diagnostic tests> test evaluation against gold standard • Sergeant, E. S. G., Kirkland, P. D. & Cowled, B. D. 2009. Field Evaluation of an equine influenza ELISA used in New South Wales during the 2007 Australian outbreak response. Preventive Veterinary Medicine, 92, 382-385.

  16. Mixture modelling • Assumptions • observed distribution of test results (for a test with a continuous outcome reading such as an ELISA) is actually a mixture of two frequency distributions, one for infected individuals and one for uninfected individuals • Opsteegh, M., Teunis, P., Mensink, M., Zuchner, L., Titilincu, A., Langelaar, M. & van der Giessen, J. 2010. Evaluation of ELISA test characteristics and estimation of Toxoplasma gondii seroprevalence in Dutch sheep using mixture models. Preventive Veterinary Medicine.

  17. Latent Class Analysis • What is Latent Class Analysis? • Maximum Likelihood • Bayesian

  18. Maximum likelihood estimation • Assumptions • 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); • The tests are compared in two or more populations with different prevalence between populations; • Test sensitivity and specificity are constant across populations; and • There are at least as many populations as there are tests being evaluated. • TAGS software • Hui, S. L. & Walter, S. D. 1980. Estimating the error rates of diagnostic tests. Biometrics, 36, 167-171.

  19. TAGS • Open R – shortcut in root directory of stick • Open tags.R in text editor or word • Select all and copy/paste into R console • Type TAGS() and <Enter> to run • Hui Walter example • 2 tests for TB • Test 1 = Mantoux • Test 2 = Tine test

  20. Follow the prompts to enter data: • Data set = new • Name = test • Number of tests = 2, Number of populations = 2 • Reference population? = No (0) • Enter results for each population from table below • Best guesses use defaults • Bootstrap CI = Yes (1000 iterations) Data

  21. $Estimations pre1 pre2 Sp1 Sp2 Se1 Se2 Est 0.0268 0.7168 0.9933 0.9841 0.9661 0.9688 CIinf0.0159 0.6911 0.9797 0.9684 0.9495 0.9540 CIsup0.0450 0.7412 0.9978 0.9921 0.9774 0.9790

  22. Bayesian estimation • What is Bayesian estimation? • Combines prior knowledge/belief (what you think you know) with data to give best estimate • Incorporates existing knowledge on parameters (Se, Sp, prevalence) • “Priors” entered as probability (usually Beta) distributions • Uses Monte Carlo simulation to solve • Outputs also as probability distributions • Can get very complex • Assumptions • Independence of the tests • Appropriate prior distributions chosen. • Need information on prior probabilities • Some methods can adjust for correlated tests • Multiple tests in multiple populations

  23. Methods • EpiTools (only allows one population so must have good information on one or more test characteristics) • WinBUGS models

  24. Bayesian analysis surra data

  25. EpiTools • Run EpiTools > Estimating true prevalence > Bayesian estimation with two tests • Enter parameters: • Data from 2x2 table: 0, 39, 0, 251 • Prevalence = Beta(1,1) (uniform = don’t know) • Test 1 (CATT): Se = Beta(82, 20), Sp = Beta(160, 2) • Test 2 (ELISA): Se = Beta(76, 26), Sp = Beta(118, 4) • Starting values: 0, 38, 0, 245 • Other values as defaults and click submit

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