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Sampling Strategies to Control Misclassification Bias in Longitudinal Udder Health Studies

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Presentation given at SVEPM 2017.

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Sampling Strategies to Control Misclassification Bias in Longitudinal Udder Health Studies

  1. 1. Sampling Strategies to Control Misclassification Bias in Longitudinal Udder Health Studies Denis Haine1 Ian Dohoo2 Daniel Scholl3 Henrik Stryhn2 Simon Dufour1 SVEPM — March 30, 2017 1 Faculté de médecine vétérinaire, Université de Montréal 2 Atlantic Veterinary College, University of Prince Edward Island 3 College of Agriculture & Biological Sciences, South Dakota State University
  2. 2. Bias in Longitudinal (Cohort) Studies?
  3. 3. Cohort Studies: Baseline and Follow-up t0 t1 1/21
  4. 4. Cohort Studies: Baseline and Follow-up t0 t1 Test - Test + No disease Disease 1/21
  5. 5. Cohort Studies: Baseline and Follow-up t0 t1 Test - Test + No disease Disease Incident Cases 1/21
  6. 6. Cohort Studies: Baseline and Follow-up t0 t1 Test - Test + No disease Disease TN FN FP TP Selection Bias 1/21
  7. 7. Cohort Studies: Baseline and Follow-up t0 t1 Test - Test + No disease Disease TN FN Misclassification Bias True Incidence Observed Incidence Based on Pekkanen et al. (2006), J. Clin. Epidemiol. 59, 281-289 1/21
  8. 8. Sensitivity and Specificity • Improve diagnostic • 2 tests • in parallel ( at 1 of 2 tests): Se; Sp • in series ( at both tests): Se; Sp • 3 tests • Analytical solution by modelling 2/21
  9. 9. Objectives • Estimate the impact of selection and misclassification biases • Incidence • Association • Effect of number of samplings 2/21
  10. 10. Material & Methods • Simulation of 100 cohorts • With 2 samplings at 1 month interval (S1 & S2) • Of 30 cows/herd, from 100 herds • For these 2 scenarios: S. aureus CNS Prevalence < 5% 10–30% Incidence 1 NIMI/100 quarters-month ∼30 NIMI/100 quarters-month Se1 ∼90% ∼60% Sp1 > 99% (100 CFU/ml) 95% (200 CFU/ml) 1 Zadoks et al., 2001; Dohoo et al., 2011; Dufour et al., 2012a; Dufour et al., 2012b. 3/21
  11. 11. S1 S2 S1 S2 Total Bias S1 S2 Selection Bias S1 S2 Misclassification Bias • With Se and Sp as Beta distributions. 4/21
  12. 12. S1 S2 Sampling: duplicate duplicate triplicate Interpretation2 : parallel series 2 out of 3 Se Sp Se Sp Se Sp S. aureus -0.10 0 +0.10 0 0 0 CNS -0.25 +0.05 +0.15 -0.05 0 +0.10 2 Dohoo et al., 2011. 4/21
  13. 13. • Poisson and logistic regressions • multi-level (quarter–cow–herd) • Monte Carlo Markov Chain (MCMC) with Stan3 • called via R • Cloud computing 3 Carpenter et al., 2017. 5/21
  14. 14. Incidence
  15. 15. 0 100 200 300 400 0.0 0.5 1.0 Cases per 100 quarters Density True incidence Total bias Selection bias only Misclassificiation bias only S. aureus Bias assessment 6/21
  16. 16. 0 100 200 300 400 0.0 0.5 1.0 Cases per 100 quarters Density True incidence Duplicate samples, single S1, parallel S2 Duplicate samples, parallel S1, single S2 Duplicate samples, parallel on S1 & S2 S. aureus Bias control by duplicate sampling 7/21
  17. 17. 0 100 200 300 400 0.0 0.5 1.0 Cases per 100 quarters Density True incidence Duplicate samples, single S1, series S2 Duplicate samples, series S1, single S2 Duplicate samples, series S1, parallel S2 Duplicate samples, series on S1 & S2 Duplicate samples, parallel S1, series S2 S. aureus Bias control by duplicate sampling 8/21
  18. 18. 0 100 200 300 400 0.0 0.5 1.0 Cases per 100 quarters Density True incidence Triplicate samples (S1 and S2) S. aureus Bias control by triplicate sampling 9/21
  19. 19. 0 5 10 15 20 10 20 30 40 50 Cases per 100 quarters Density True incidence Total bias Selection bias only Misclassificiation bias only CNS Bias assessment 10/21
  20. 20. Association
  21. 21. 0.0 0.1 0.2 0.3 0.0 2.5 5.0 7.5 10.0 Odds ratio Density True association Total bias Selection bias only Misclassificiation bias only S. aureus Bias assessment 11/21
  22. 22. 0.0 0.5 1.0 1.5 2.0 2.5 1 2 3 4 5 6 Odds ratio Density True association Total bias Selection bias only Misclassificiation bias only CNS Bias assessment 12/21
  23. 23. 0.0 0.5 1.0 1.5 2.0 1 2 3 4 5 6 Odds ratio Density True association Duplicate samples, single S1, parallel S2 Duplicate samples, parallel S1, single S2 Duplicate samples, parallel on S1 & S2 CNS Bias control by duplicate sampling 13/21
  24. 24. 0.0 0.5 1.0 1.5 2.0 2.5 1 2 3 4 5 6 Odds ratio Density True association Duplicate samples, single S1, series S2 Duplicate samples, series S1, single S2 Duplicate samples, series S1, parallel S2 Duplicate samples, series on S1 & S2 Duplicate samples, parallel S1, series S2 CNS Bias control by duplicate sampling 14/21
  25. 25. 0.0 0.5 1.0 1.5 1 2 3 4 5 6 Odds ratio Density True association Triplicate samples (S1 and S2) CNS Bias control by triplicate sampling 15/21
  26. 26. So Which Strategy?
  27. 27. Prevalence & Incidence Se Sp What? Low Excellent Excellent Nothing! High Fair Excellent Bias! • Misclassification bias (non-differential): • Bias towards null • Importance of Sp 16/21
  28. 28. (0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96)(0.96) (1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51)(1.51) 1.0 1.2 1.4 1.6 1.8 2.0 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 Specificity of test Apparentrelativerisk SENSITIVITY OF TEST 0.5 0.7 0.9 Cohort study Bias as a function of sensitivity and specificity Risk in population A = .10; Risk in population B = .05; True relative risk = 2.0 Copeland et al. (1977), Am. J. Epidemiol. 105(5), 488−495 17/21
  29. 29. 1 2 3 4 5 6 7 8 9 10 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Prevalence of exposure Apparentrelativerisk Se=0.80, Sp=0.99 Se=0.85, Sp=0.95 Se=0.90, Sp=0.90 Se=0.95, Sp=0.85 Se=0.99, Sp=0.80 True relative risk = 10 Apparent relative risk as a function of prevalence Flegal et al. (1986), Am. J. Epidemiol. 123(4), 736−751 18/21
  30. 30. • Improve Se at baseline ( test: rule out disease) • Improve Sp at follow-up (⊕ test: rule in disease) • Incorporate Se/Sp in modelling (Bayes)4 4 McInturff et al., 2004. 19/21
  31. 31. Conclusion • Increasing number of samples can (or cannot) prevent biases • Evaluate biases with R package https://github.com/dhaine/misclass 19/21
  32. 32. 1 devtools : : i n s t a l l _ g i t h u b ( ’ dhaine / misclass ’ ) 2 l i b r a r y ( misclass ) 3 s i m _ l i s t 1 ← vector ( ” l i s t ” , 100) 4 require ( pbapply ) 5 set.seed (123) 6 s i m _ l i s t ← r e p l i c a t e ( n = 100 , 7 expr = make_data(100 , 30 , ” saureus ” ) , 8 s i m p l i f y = FALSE) 9 check_incidence ( sim_list , 10 i t e r = 500 , 11 warmup = 100 , 12 chains = 4 , 13 cores = 4 , 14 seed = 123 , 15 nsimul = 100) 20/21
  33. 33. Thank you! denis.haine@umontreal.ca @denishaine https://github.com/dhaine/misclass https://github.com/dhaine/plotBias for bias plots shown in Discussion (and more) https://cran.r-project.org/package=episensr R package for quantitative bias analysis Images: Unsplash, Dairy Farmers of Canada 21/21

Presentation given at SVEPM 2017.

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