1. Bias, Confounding and Fallacies in Epidemiology
M. Tevfik DORAK

2. (Effect Modification)
BIAS
Definition
Types
Examples
Remedies
CONFOUNDING
FALLACIES
(Effect Modification)

3. Bias is one of the three major threats to internal validity:
What is Bias?
Bias is one of the three major threats to internal validity:
Bias
Confounding
Random error / chance

4. Systematic error in design or conduct of a study (Szklo et al, 2000)
What is Bias?
Any trend in the collection, analysis, interpretation, publication or review of data that can lead to conclusions that are systematically different from the truth (Last, 2001)
A process at any state of inference tending to produce results that depart systematically from the true values (Fletcher et al, 1988)
Systematic error in design or conduct of a study (Szklo et al, 2000)

5. Bias is systematic error
Errors can be differential (systematic) or non-differential (random)
Random error: use of invalid outcome measure that equally misclassifies cases and controls
Differential error: use of an invalid measures that misclassifies cases in one direction and misclassifies controls in another
Term 'bias' should be reserved for differential or systematic error

6. Random Error
Per Cent
Size of induration (mm)
WHO (www)

7. Systematic Error
Per Cent
Size of induration (mm)
WHO (www)

8. Chance vs Bias Chance is caused by random error
Bias is caused by systematic error
Errors from chance will cancel each other out in the long run (large sample size)
Errors from bias will not cancel each other out whatever the sample size
Chance leads to imprecise results
Bias leads to inaccurate results

9. Types of Bias Selection bias Information (misclassification) bias
Unrepresentative nature of sample
Information (misclassification) bias
Errors in measurement of exposure of disease
Confounding bias
Distortion of exposure - disease relation by some other factor
Types of bias not mutually exclusive
(effect modification is not bias)
This classification is by Miettinen OS in 1970s
See for example Miettinen & Cook, 1981 (www)

10. Selection Bias
Selective differences between comparison groups that impacts on relationship between exposure and outcome
Usually results from comparative groups not coming from the same study base and not being representative of the populations they come from

11. Selection Bias Examples
(www)

12. Selection Bias Examples
(www)

13. Selection Bias Examples
(www)

14. Selection Bias Examples
(www)

15. Selection Bias Examples Selective survival (Neyman's) bias
(www)

16. Selection Bias Examples
Case-control study:
Controls have less potential for exposure than cases
Outcome = brain tumour; exposure = overhead high voltage power lines
Cases chosen from province wide cancer registry
Controls chosen from rural areas
Systematic differences between cases and controls

17. Case-Control Studies:
Potential Bias
Schulz & Grimes, 2002 (www) (PDF)

18. Selection Bias Examples
Cohort study:
Differential loss to follow-up
Especially problematic in cohort studies
Subjects in follow-up study of multiple sclerosis may differentially drop out due to disease severity
Differential attrition  selection bias

19. Selection Bias Examples
Self-selection bias:
- You want to determine the prevalence of HIV infection
- You ask for volunteers for testing
- You find no HIV
- Is it correct to conclude that there is no HIV in this location?

20. Selection Bias Examples
Healthy worker effect:
Another form of self-selection bias
“self-screening” process – people who are unhealthy “screen” themselves out of active worker population
Example:
- Course of recovery from low back injuries in year olds
- Data captured on worker’s compensation records
- But prior to identifying subjects for study, self-selection has already taken place

21. Selection Bias Examples
Diagnostic or workup bias:
Also occurs before subjects are identified for study
Diagnoses (case selection) may be influenced by physician’s knowledge of exposure
Example:
- Case control study – outcome is pulmonary disease, exposure is smoking
- Radiologist aware of patient’s smoking status when reading x-ray – may look more carefully for abnormalities on x-ray and differentially select cases
Legitimate for clinical decisions, inconvenient for research

22. Types of Bias Selection bias
Unrepresentative nature of sample
** Information (misclassification) bias **
Errors in measurement of exposure of disease
Confounding bias
Distortion of exposure - disease relation by some other factor
Types of bias not mutually exclusive
(effect modification is not bias)

23. Information / Measurement / Misclassification Bias
Method of gathering information is inappropriate and yields systematic errors in measurement of exposures or outcomes
If misclassification of exposure (or disease) is unrelated to disease (or exposure) then the misclassification is non-differential
If misclassification of exposure (or disease) is related to disease (or exposure) then the misclassification is differential
Distorts the true strength of association

24. Information / Measurement / Misclassification Bias
Sources of information bias:
Subject variation
Observer variation
Deficiency of tools
Technical errors in measurement

25. Information / Measurement / Misclassification Bias
Recall bias:
Those exposed have a greater sensitivity for recalling exposure (reduced specificity)
- specifically important in case-control studies
- when exposure history is obtained retrospectively
cases may more closely scrutinize their past history looking for ways to explain their illness
- controls, not feeling a burden of disease, may less closely examine their past history
Those who develop a cold are more likely to identify the exposure than those who do not – differential misclassification
- Case: Yes, I was sneezed on
- Control: No, can’t remember any sneezing

26. Information / Measurement / Misclassification Bias
Reporting bias:
Individuals with severe disease tends to have complete records therefore more complete information about exposures and greater association found
Individuals who are aware of being participants of a study behave differently (Hawthorne effect)

27. Controlling for Information Bias
- Blinding
prevents investigators and interviewers from knowing case/control or exposed/non-exposed status of a given participant
- Form of survey
mail may impose less “white coat tension” than a phone or face-to-face interview
- Questionnaire
use multiple questions that ask same information
acts as a built in double-check
- Accuracy
multiple checks in medical records
gathering diagnosis data from multiple sources

28. Types of Bias Selection bias Information (misclassification) bias
Unrepresentative nature of sample
Information (misclassification) bias
Errors in measurement of exposure of disease
** Confounding bias **
Distortion of exposure - disease relation by some other factor
Types of bias not mutually exclusive
(effect modification is not bias)

29. (www)

30. Cases of Down Syndrome by Birth Order
EPIET (www)

31. Cases of Down Syndrome by Age Groups
EPIET (www)

32. Cases of Down Syndrome by Birth Order
and Maternal Age
EPIET (www)

33. Confounding
A third factor which is related to both exposure and outcome, and which accounts for some/all of the observed relationship between the two
Confounder not a result of the exposure
e.g., association between child’s birth rank (exposure) and Down syndrome (outcome); mother’s age a confounder?
e.g., association between mother’s age (exposure) and Down syndrome (outcome); birth rank a confounder?

34. Confounding Exposure Outcome Third variable
To be a confounding factor, two conditions must be met:
Exposure
Outcome
Third variable
Be associated with exposure
- without being the consequence of exposure
Be associated with outcome
- independently of exposure (not an intermediary)

35. Confounding Birth Order Down Syndrome Maternal Age
Maternal age is correlated with birth order and a risk factor even if birth order is low

36. Confounding ? Maternal Age Down Syndrome Birth Order
Birth order is correlated with maternal age but not a risk factor in younger mothers

37. Confounding Coffee CHD Smoking
Smoking is correlated with coffee drinking and a risk factor even for those who do not drink coffee

38. Confounding ? Smoking CHD Coffee
Coffee drinking may be correlated with smoking but is not a risk factor in non-smokers

39. Confounding Alcohol Lung Cancer Smoking
Smoking is correlated with alcohol consumption and a risk factor even for those who do not drink alcohol

40. Not related to the outcome Not an independent risk factor
Confounding ?
Smoking
CHD
Yellow fingers
Not related to the outcome
Not an independent risk factor

41. Confounding ?
Diet
CHD
Cholesterol
On the causal pathway

42. Confounding
Imagine you have repeated a positive finding of birth order association in Down syndrome or association of coffee drinking with CHD in another sample. Would you be able to replicate it? If not why?
Imagine you have included only non-smokers in a study and examined association of alcohol with lung cancer. Would you find an association?
Imagine you have stratified your dataset for smoking status in the alcohol - lung cancer association study. Would the odds ratios differ in the two strata?
Imagine you have tried to adjust your alcohol association for smoking status (in a statistical model). Would you see an association?

43. Confounding
Imagine you have repeated a positive finding of birth order association in Down syndrome or association of coffee drinking with CHD in another sample. Would you be able to replicate it? If not why?
You would not necessarily be able to replicate the original finding because it was a spurious association due to confounding.
In another sample where all mothers are below 30 yr, there would be no association with birth order.
In another sample in which there are few smokers, the coffee association with CHD would not be replicated.

44. Confounding
Imagine you have included only non-smokers in a study and examined association of alcohol with lung cancer. Would you find an association?
No because the first study was confounded. The association with alcohol was actually due to smoking. By restricting the study to non-smokers, we have found the truth. Restriction is one way of preventing confounding at the time of study design.

45. Confounding
Imagine you have stratified your dataset for smoking status in the alcohol - lung cancer association study. Would the odds ratios differ in the two strata?
The alcohol association would yield the similar odds ratio in both strata and would be close to unity. In confounding, the stratum-specific odds ratios should be similar and different from the crude odds ratio by at least 15%. Stratification is one way of identifying confounding at the time of analysis.
If the stratum-specific odds ratios are different, then this is not confounding but effect modification.

46. Confounding
Imagine you have tried to adjust your alcohol association for smoking status (in a statistical model). Would you see an association?
If the smoking is included in the statistical model, the alcohol association would lose its statistical significance. Adjustment by multivariable modelling is another method to identify confounders at the time of data analysis.

47. Confounding
For confounding to occur, the confounders should be differentially represented in the comparison groups.
Randomisation is an attempt to evenly distribute potential (unknown) confounders in study groups. It does not guarantee control of confounding.
Matching is another way of achieving the same. It ensures equal representation of subjects with known confounders in study groups. It has to be coupled with matched analysis.
Restriction for potential confounders in design also prevents confounding but causes loss of statistical power (instead stratified analysis may be tried).

48. Confounding
Randomisation, matching and restriction can be tried at the time of designing a study to reduce the risk of confounding.
At the time of analysis:
Stratification and multivariable (adjusted) analysis can achieve the same.
It is preferable to try something at the time of designing the study.

49. Effect of randomisation on outcome of trials in acute pain
Bandolier Bias Guide (www)

50. Confounding Obesity Mastitis Age
In cows, older ones are heavier and older age increases the risk for mastitis. This association may appear as an obesity association

51. Confounding
If each case is matched with a same-age control, there will be no association (OR for old age = 2.6, P = )
(www)

52. No Confounding
(www)

53. Cases of Down Syndrome by Birth Order
and Maternal Age
If each case is matched with a same-age control, there will be no association. If analysis is repeated after stratification by age, there will be no association with birth order.
EPIET (www)

54. ** (Effect Modification) **
BIAS
Definition
Types
Examples
Remedies
CONFOUNDING
** (Effect Modification) **
FALLACIES

55. Confounding or Effect Modification
Birth Weight
Leukaemia
Sex
Can sex be responsible for the birth weight association in leukaemia?
- Is it correlated with birth weight?
- Is it correlated with leukaemia independently of birth weight?
- Is it on the causal pathway?
- Can it be associated with leukaemia even if birth weight is low?
- Is sex distribution uneven in comparison groups?

56. Confounding or Effect Modification
Birth Weight
Leukaemia
Sex
OR = 1.5
Does birth weight association differ in strength according to sex?
Birth Weight
Leukaemia
BOYS
OR = 1.8
GIRLS
Birth Weight
/ /
Leukaemia
OR = 0.9

57. Effect modification is similar to interaction in statistics.
In an association study, if the strength of the association varies over different categories of a third variable, this is called effect modification. The third variable is changing the effect of the exposure.
The effect modifier may be sex, age, an environmental exposure or a genetic effect.
Effect modification is similar to interaction in statistics.
There is no adjustment for effect modification. Once it is detected, stratified analysis can be used to obtain stratum-specific odds ratios.

58. Effect modifier Confounding factor Belongs to nature Belongs to study
Different effects in different strata
Simple
Useful
Increases knowledge of biological mechanism
Allows targeting of public health action
Confounding factor
Belongs to study
Adjusted OR/RR different from crude OR/RR
Distortion of effect
Creates confusion in data
Prevent (design)
Control (analysis)

59. (Effect Modification)
BIAS
Definition
Types
Examples
Remedies
CONFOUNDING
(Effect Modification)
** FALLACIES **

60. Fallacies HISTORICAL FALLACY ECOLOGICAL FALLACY (Cross-Level Bias)
BERKSON'S FALLACY
(Selection Bias in Hospital-Based CC Studies)
HAWTHORNE EFFECT
(Participant Bias)
REGRESSION TO THE MEAN (Davis, 1976)
(Information Bias)

61. HOW TO CONTROL FOR CONFOUNDERS?
IN STUDY DESIGN…
RESTRICTION of subjects according to potential confounders (i.e. simply don’t include confounder in study)
RANDOM ALLOCATION of subjects to study groups to attempt to even out unknown confounders
MATCHING subjects on potential confounder thus assuring even distribution among study groups

62. HOW TO CONTROL FOR CONFOUNDERS?
IN DATA ANALYSIS…
STRATIFIED ANALYSIS using the Mantel Haenszel method to adjust for confounders
IMPLEMENT A MATCHED-DESIGN after you have collected data (frequency or group)
RESTRICTION is still possible at the analysis stage but it means throwing away data
MODEL FITTING using regression techniques

63. Effect of blinding on outcome of trials of acupuncture for chronic back pain
Bandolier Bias Guide (www)

64. WILL ROGERS' PHENOMENON Assume that you are tabulating survival for patients with a certain type of tumour. You separately track survival of patients whose cancer has metastasized and survival of patients whose cancer remains localized. As you would expect, average survival is longer for the patients without metastases. Now a fancier scanner becomes available, making it possible to detect metastases earlier. What happens to the survival of patients in the two groups? The group of patients without metastases is now smaller. The patients who are removed from the group are those with small metastases that could not have been detected without the new technology. These patients tend to die sooner than the patients without detectable metastases. By taking away these patients, the average survival of the patients remaining in the "no metastases" group will improve. What about the other group? The group of patients with metastases is now larger. The additional patients, however, are those with small metastases. These patients tend to live longer than patients with larger metastases. Thus the average survival of all patients in the "with-metastases" group will improve. Changing the diagnostic method paradoxically increased the average survival of both groups! This paradox is called the Will Rogers' phenomenon after a quote from the humorist Will Rogers ("When the Okies left California and went to Oklahoma, they raised the average intelligence in both states").
(www)
See also Festenstein, 1985 (www)

65. Cause-and-Effect Relationship
Grimes & Schulz, (www) (PDF)

67. M. Tevfik DORAK Paediatric & Lifecourse Epidemiology Research Group
School of Clinical Medical Sciences (Child Health)
Newcastle University
England, U.K.