Lecture 7: Misclassification

1. Lecture 7: Misclassification Matthew Fox Advanced Epidemiology

2. Non-differential misclassification biases … What does that mean?

3. How bad does the non-differential misclassification have to be? Could it ever go past the null or only to?

4. We know what to do about measured confounding. What do we do about information bias (and selection bias, unmeasured confounders for that matter)

5. Last class • 3 concepts of interaction • Effect measure modification • If no EMM on one scale, often EMM on another • Interdependence • Risk in double exposed isn’t explained by the sum of the two exposure effects alone • Statistical interaction • In logistic regression = multiplicative interaction

6. Today: Misclassification • Exposure misclassification • Disease misclassification • Rules on the impact of misclassifications • Misclassification of covariates • What to do about it?

7. The F test • Take 2 minutes and count all the Fs • The necessity of training farm hands for first class farms in the fatherly handling of farm livestock is foremost in the minds of effective farm owners. Since the forefathers of the farm owners trained the farm hands for first class farms in the fatherly handling of farm livestock, the farm owners feel they should carry on with the former family tradition of training farmhands of first class farms in the effective fatherly handling of farm live stock, however futile, because of their belief that it forms the basis of effective farm management efforts. • Answer: 48 or 49

8. Exposure — terms (1a) • Predictive values • Truth is in the numerator • E is the truth, T is the test • Positive predictive value • Probability of being truly exposed, given a + test • Pr(E+|Test+) • Negative predictive value • Probability of being truly unexposed, given - test • Pr(E-|Test-)

9. Exposure — terms (1b) • Classification values • Truth is the denominator • E is the truth, T is the test • Sensitivity • Probability of being correctly classified as E+ • Pr(T+|E+) • False negative • Probability of being wrongly classified as unexposed • 1-sensitivity

10. Exposure — terms (1b) • Classification values • Truth is the denominator • E is the truth, T is the test • Specificity • Probability of being correctly classified as E- • Pr(T-|E-) • False positive • Probability of being incorrectly classified as E+ • 1-specificity

11. Relation between predictive and classification measures When prevalence is 100%, the PPV is 1 and the NPV is 0. When prevalence is 0%, the NPV is 1 and the PPV is 0.

12. Exposure Misclassification

13. Exposure — terms (2) • Non-differential exposure misclassification • Rates of E misclassification doesn’t depend on D • Se of E classification is same in the D+ and D- AND • Sp of E classification is same in the D+ and D- • Non-differential misclassification of a dichotomous exposure creates an expected bias of effect estimates towards the null

14. Exposure — terms (3) • Differential exposure misclassification • Rates of E misclassification do depend on D • Se of E classification is not same in the D+ and D- OR • Sp of E classification is same in the D+ and D- • Differential exposure misclassification of a dichotomous exposure creates an unpredictable bias of the effect estimates

15. Exposure — terms (4) • Exposure misclassification • Non-differential CANNOT explain a non-null result • Differential CAN explain a non-null result • Exposure classification errors are inevitable • Incomplete knowledge of dose, duration, induction • Errors in interview, data coding, data entry • Mistakes in inference • Strive to make the errors non-differential

16. Some misclassification is easily seen as structural: Recall bias D ER E E = alcohol use during pregnancy, D = birth defect, ER = Measure of alcohol use after giving birth D EM E E = blood lipids, D = cancer, ER = Measure of blood lipids after cancer occurs

17. Exposure Misclassification (5) • Non-differential misclassification requires that se0=se1=se and sp0=sp1=sp.

18. Exposure Misclassification (5) • Non-differential misclassification requires that se0=se1=se and sp0=sp1=sp.

19. Exposure Misclassification (6) • Given an observation and estimates of sensitivities and specificities, recalculate truth • Obtain estimates of Se and Sp from literature, pilot studies, or substudy with gold standard measurement • Se and Sp not necessarily non-differential

20. Ex. 1: Non-differential (1)

21. Ex. 1: Non-differential (2)

22. Exception #1 to the mantra • Misclassification is haphazard, not random • Random implies intent, but these mistakes are made without intent, or haphazardly. We model haphazard mistakes as occurring at random. • Misclassification operates on individuals • With some probability, ND misclassification may bias AWAY from the null • But the EXPECTATION is towards the null

23. Example of Exception #1 • Study truth as shown, Se=Sp=0.9 (non-differential) • Expectation is as shown • Apply misclassification probabilities • Apply probabilities to each individual • Calculate RR • Repeat 10,000 times • Back-calculate truth given Se and Sp

24. Distribution of observed OR Towards null Away from null Truth

25. Exception #2 example

26. Exception #2 to the mantra • When exposure has two or more categories • Bias from non-differential exposure misclassification for a given comparison may be AWAY from the null • The estimates of effect within the categories will be biased towards one another

27. Disease Misclassification

28. Ex. 1: Non-differential (1)

29. Ex. 1: Non-differential Disease

30. Ex. 3: Non-Differential Disease Misc

31. Equations (1)

32. Equations (2)

33. Except #3 to the mantra:Equations (3)

34. Ex. 3: Revisited

35. With imperfect SP interval becomes narrower, but RR biased to null With case-control, sampling of controls increases width of interval

36. Misclassification of a confounder • Non-differential misclassification of a confounder yields residual confounding • The estimate of effect is biased away from the truth in the direction of the confounding • For weak effects, resources may be better spent accurately measuring a strong confounder than accurately measuring the index and reference conditions

37. Covariate misclassification

38. Covariate misclassification (3)

39. Crude RR = 4 Covariate misclassification (4) Crude RR = 4, Adjusted = 3.4, Misc adjusted = 3.6

40. Exception #4: Non-differential misclassification and R(I)

42. Methodology/Principal Findings • 2003 National Survey of Children Health • Parental report of whether child has ever been diagnosed with asthma by a physician was D • Parental report of perception of neighborhood safety was E • In unadjusted models • OR for reporting asthma associated with living in neighborhoods perceived sometimes/never safe was 1.36 (95% CI: 1.21, 1.53) vs. neighborhoods perceived always safe. • Adjusting for covariates attenuated OR • OR 1.25, 95% CI 1.08, 1.43

43. Exception #5: Dependent errors

44. Exception #6: Dependent errors

45. Misclassification can be shown as a structural problem

46. Non-differential, non-dependent misclassification of A and D Note that we study A* and Y*, so there is an unblocked backdoor path

47. Differential, non-dependent misclassification of A and D

48. Non-differential, dependent misclassification of A and D

49. Differential, dependent misclassification of A and D

50. The NO-SHOTS Study • In kids with WHO-defined severe pneumonia, is treatment failure at 48 hours when given oral amoxicillin equivalent to injectable penicillin? • Non-blinded, Equivalency RCT • Among children aged 3-59 months • Half in hospital, half at home • 1,702 children randomized 1:1 • Equivalence defined as a RD 95% CI +/- 5% • Results: • -RD: 0.4% (95% CI: -4.2% to 3.3%)