1. ERRORS, CONFOUNDING, and INTERACTION
Hamid Heidarian
PhD of Epidemiology
Assistant Professor at Mashhad University of Medical Sciences

2. خطا، مخدوش کنندگی و برهمکنش

3. ارزشیابی مداخله 1 2 مشاهده داده اطلاعات 3 10 بينش 4 9 ایجاد فرضیه 5
Epidemiological
Sequence cycle 10
ارزشیابی
بينش 4 9
مداخله
ایجاد فرضیه 5
انجام مطالعه تجربی
استنتاج علمی
آزمون فرضیه 8 7 6 3

4. آیا همبستگی مشاهده شده در نمونه = همبستگی در جامعه است؟ خیر: خطای تصادفی خطای سیستماتیک بلی: آیا همبستگی = اثر است؟ مخدوش شدگی

5. خطای تصادفی

6. فارسی ! خطای تصادفی واریانس دقت اطمینان یا پایایی قابلیت تکرار
English
-/+
فارسی !
Random error -
خطای تصادفی
Variance
واریانس
Precision +
دقت
Reliability
اطمینان یا پایایی
Repeatability
قابلیت تکرار
Reproducibility
قابلیت بازتولید

7. What Is Random Error? Any factors that randomly affect results
Random error adds variability to the data but does not affect average
Sampling and measurement errors can produce random error.

8. Random Error
Frequency
The distribution of X with no random error X

9. Random Error The distribution of X with random error Frequency
The distribution of X with no random error X

10. Random Error The distribution of X with random error Frequency
The distribution of X with no random error
Notice that random error doesn’t
affect the average, only the
variability around the average. X

11. Association in Reality
There are tow types of random errors:
Association in Reality
Association in Study + -
correct
Type one
Type two

12. Indices of reliability
Qualitative variables:
Percent agreement
Kappa statistic
Quantitative variables:
Correlation graph and coefficients
Intraclass correlation coefficient
Mean difference and paired t-test
Coefficient of variability
Bland-Altman plot

13. How to reduce random error
How to reduce random error? Increase the sample size How to explain random error? Statistics: Confidence Interval P-value

14. Systematic Error

15. English
-/+
فارسی !
Systematic error -
خطای منظم
Bias
تورش
validity +
اعتبار- اعتماد - روایی - نااریبی

16. The average of hypothetically infinite number of results differ from the true value.
Systematic error does affect the average
Sampling and measurement errors can produce systematic Error.

17. Systematic Error Frequency
The distribution of X with no systematic error X

18. Systematic Error The distribution of X with systematic error Frequency
The distribution of X with no systematic error X

19. Systematic Error The distribution of X with systematic error Frequency
The distribution of X with no systematic error
Notice that systematic error does
affect the average; we call
this a bias. X

21. Selection bias
Selection Bias: is present when individuals have different probabilities of being included in the study according to relevant study characteristics: namely the exposure and the outcome of interest
Selection bias is a systematic error in the selection of study subjects – cases or controls in a case-control study, exposed or unexposed in a cohort study
Examples of selection bias
Self-selection bias
self-referral
non-response bias
healthy worker effect
Prevalence-incidence bias (Neyman’s bias)
Medical surveillance bias
diagnostic bias
Berkson’s bias (admission rate)

22. REFERENCE POPULATION Diseased + - + - Exposed STUDY SAMPLE + -
Exposed
The book illustrates this in Figure 4.2, where exposed cases have a higher probability of being selected for the study than other categories of individuals.
They describe a type of selection bias called “medical surveillance bias”. In a case-control study examining the association between oral contraceptive use to diabetes (a disease with “an important” subclinical component”) Since oral contraceptive use is likely to be associated with greater medical surveillance due to a higher than normal frequency of medical encounters, women who use OC’s are more likely to be diagnosed with diabetes than other women (not using OC’s). In a study comparing cases of diabetes and controls without diabetes, a spurious association with OC’s and diabetes may occur.
The effect of selection bias on the direction of measure of association is a function of which cell’s in a n x k table (eg., 2 x 2)) is/are subject to a spuriously higher or lower probability of selection.
STUDY SAMPLE

23. Avoiding and detecting selection bias
In case-control studies, Choose controls from the same “study-base” as cases.
In cohort studies, the rate of loss to follow-up indicates the potential for selection bias. Comparison of the characteristics of those lost to follow-up with those persons remaining under follow-up, may indicate the potential consequences of any selection bias.
the approach to case and control selection signals the potential for selection bias.
Studies based around particular institutions or facilities, i.e., a hospital, are particularly susceptible.

24. Information Bias
Information Bias: results from a systematic error in measurement thus leading to misclassification (in exposure or outcome category).
tendency for individuals selected for inclusion in the study to be erroneously placed in different exposure/outcome categories, thus leading to misclassification.
Results from either imperfect definitions of study variables or flawed data collection procedures.
These errors result in misclassification of exposure and/or outcome status for a significant proportion of study participants

25. REFERENCE POPULATION Cases Control Misclassification of EXPOSURE
Diseased + -
Exposed
Cases Control
STUDY SAMPLE

26. REFERENCE POPULATION Misclassification of OUTCOME Diseased + - + + -
Disease
Exposed
STUDY SAMPLE

27. Information Bias
These errors result in misclassification of exposure and/or outcome status
Terms: validity (sensitivity and specificity) refer to classification of both disease and exposure status

28. Information bias could be either Non-differential or Differential

29. Sensitivity and Specificity=100%No
Misclassification
Sensitivity or Specificity<100%
They are not equal in comparison groups
They are equal in comparison groups
Differential Misclassification
Non-differential Misclassification

30. Internal Validity
External Validity (generalizability)

31. Accuracy = validity + reliability

32. Confounding

33. Is Association = Effect?

34. Confounding is: association measure ≠ effect measure

35. Counterfactual or Potential Outcome Definition of effect measure: If we can imagine the experience of a cohort over the same interval under two different conditions, then we can ask what the incidence rate of any outcome would be under two different conditions.

36. Confounding
IAe
IAu
IAe
IBu

37. Criteria for a Confounding Factor
A Confounding factor must be a extraneous risk factor for the disease.
A Confounding factor must be associated with the exposure under study in the source population.
A Confounding factor must not be affected by the exposure or the disease.

38. E D C

39. Confounding is not a all or none situation
There are Negative, positive and qualitative confounding

40. Ways to deal with confounding:
Randomization
Matching
Restriction
Stratification
Modeling

41. Interaction
Synonyms:
Effect modification
Heterogeneity of effect

42. There are two definitions for interaction:

43. When the incidence rate of disease in the presence of two or more risk factors differs from the incidence rate expected to result from their individual effects.
The effect can be greater than what we would
expect (positive interaction, synergism) or less
Than what we would expect (negative interaction, antagonism).

44. The problem is to determine what we would expect to result from the individual effects of the exposures (additive vs multiplicative model).

47. 2. when the magnitude of a measure of association (between exposure and disease) meaningfully differs according to the values of the third variable.

50. THANK YOU