1. By Hatim Jaber MD MPH JBCM PhD 11-7-2017
Faculty of Medicine Epidemiology and Biostatistics ( ) الوبائيات والإحصاء الحيوي Lecture 14 Bias and Confounding By
Hatim Jaber
MD MPH JBCM PhD

2. Basic epidemiological concepts/ Epidemiological study types
Association and causation 
Bias and confounding
Screening tests and result interpretation
Communicable diseases Epidemiology
Transmission of infectious diseases 
Chronic Non-communicable Diseases Epidemiology
Risk factors of NCD   
Workplace Hazards – Radiation and Noise at workplace
Current global environmental problems, their causes, effects, and prevention measures.(1)
Current global environmental problems, their causes, effects, and prevention measures.(2)
Food contamination and food borne diseases(1) 
Food contamination and food borne diseases (2)

3. Presentation outline Measurements of the variables 11:00 to 11:10 Bias
Time
Measurements of the variables
11:00 to 11:10
Bias
11:10 to 11:20
Types of bias
11: 20 to 11:40
Cofounding
11:40 to 12:00
Types and ways to control them in various types of biases.
12:00 to 12:15

4. Analytical Methods Measures of association /strength of association
Testing hypothesis of association
Controlling confounders

5. To Show a Valid Statistical Association
We need to assess:
Bias: whether systematic error has been built into the study design
Confounding: whether an extraneous factor is related to both the disease and the exposure
Role of chance: how likely is it that what we found is a true finding

6. Measurements
At every step in our medical practice – whether asking the history of a symptom, looking for a sign or reviewing the investigations, we are in fact making ‘measurements’.
Measurements of the variables listed in any study has 3 categories:
exposure
outcome and
confounders

7. Research Variables
In general we should list out the following categories of variables related to our research objectives :
(a) The exposure variable (s)
(b) The outcome variable (s)
(c) The potential confounding variables and probable effect modifiers.

8. For example
in a study proceeding with the research objective of studying the association between alcohol use and oral cancer, the variables of study would be :
Exposure variable : Alcohol use
Outcome variable : Oral cancer
Potential Confounding variables and possible effect modifying variables : Age, Sex, tobacco use, oral hygiene, dental malformations and genetic background

9. Three reasons due to which our measurement process may become incorrect
● The basic technique of measurement may be incorrect, due to defective instruments, wrong techniques, inadequately trained data collectors, etc. This is the situation of “measurement error”.
● While selecting the two groups or else while making measurements on the two groups of subjects, which are to be compared, we start treating the two groups in a differential manner. This is known as systematic error or Bias.
● The observed association which we have shown between the exposure and the outcome variables, is actually due to a third, indirectly acting variable and not really due to an association between the exposure and outcome variables. This is the situation of confounding error

10. Internal validity Any one or more of the above erroneous situations:
(measurement error,
systematic error or
Bias,confounding error), if present, will lead to “lack of internal validity” of the epidemiological or research work.

11. Threats to Internal & External Validity
Keep in mind that in science it is always important to balance multiple concerns.
Ethics: Harm versus Benefit
Sampling: Representativeness versus Practicality
When choosing a research design, it is important to address whether:
You can address or eliminate alternative explanations for your results (Internal Validity).
You can generalize your results (External Validity).

12. Basic Measurement Technique
(a) The measurement process should be valid, i.e. the measurements which we are making and recording should correctly measure what we really intend to measure
(b) Secondly, the measurement process should have “Reliability” (Syn : Precision, consistency, replicability, repeatability)

13. Types of Association
Association may be grouped into following three types;
Spurious Association : When the observed association between suspected cause and effect may not be real. Example- Perinatal mortality being high in hospital deliveries than home deliveries implying hospital is unsafe. The cause of spurious association is poor control of Biases in study.
Indirect Association : It is a statistical association between a factor of interest and a disease due to presence of another factor known as Confounding Factor Example-Iodine deficiency and Altitude association with Endemic Goitre.
Direct Causal Association : One to one and multifactorial

14. THE DIFFERENCE BETWEEN BIAS AND CONFOUNDING
Bias creates an association that is not true,
but confounding describes an association that is true, but potentially misleading.

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

16. 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

17. 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)

18. 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

19. Systematic error built into the study design
BIAS
Systematic error built into the study design
Selection Bias
Information Bias

20. 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. Types of Selection Bias
Berksonian bias – There may be a spurious association between diseases or between a characteristic and a disease because of the different probabilities of admission to a hospital for those with the disease, without the disease and with the characteristic of interest
Berkson J. Limitations of the application of fourfold table analysis to hospital data. Biometrics 1946;2:47-53

24. Types of Selection Bias (cont.)
Response Bias – those who agree to be in a study may be in some way different from those who refuse to participate
Volunteers may be different from those who are enlisted

25. Types of Information Bias
Interviewer Bias – an interviewer’s knowledge may influence the structure of questions and the manner of presentation, which may influence responses
Recall Bias – those with a particular outcome or exposure may remember events more clearly or amplify their recollections

26. Types of Information Bias (cont.)
Observer Bias – observers may have preconceived expectations of what they should find in an examination
Loss to follow-up – those that are lost to follow-up or who withdraw from the study may be different from those who are followed for the entire study

27. Information Bias (cont.)
Hawthorne effect – an effect first documented at a Hawthorne manufacturing plant; people act differently if they know they are being watched
Surveillance bias – the group with the known exposure or outcome may be followed more closely or longer than the comparison group

28. Information Bias (cont.)
Misclassification bias – errors are made in classifying either disease or exposure status

29. Types of Misclassification Bias
Differential misclassification – Errors in measurement are one way only
Example: Measurement bias – instrumentation may be inaccurate, such as using only one size blood pressure cuff to take measurements on both adults and children

30. Misclassification Bias (cont.)
Nondifferential (random) misclassification – errors in assignment of group happens in more than one direction
This will dilute the study findings BIAS TOWARD THE NULL

31. Controls for Bias
Be purposeful in the study design to minimize the chance for bias
Example: use more than one control group
Define, a priori, who is a case or what constitutes exposure so that there is no overlap
Define categories within groups clearly (age groups, aggregates of person years)
Set up strict guidelines for data collection
Train observers or interviewers to obtain data in the same fashion
It is preferable to use more than one observer or interviewer, but not so many that they cannot be trained in an identical manner

32. Controls for Bias (cont)
Randomly allocate observers/interviewer data collection assignments
Institute a masking process if appropriate
Single masked study – subjects are unaware of whether they are in the experimental or control group
Double masked study – the subject and the observer are unaware of the subject’s group allocation
Triple masked study – the subject, observer and data analyst are unaware of the subject’s group allocation
Build in methods to minimize loss to follow-up

33. 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

34. Available at: http://ebp. lib. uic
Available at: Accessed on Oct 18, 2011.

35. Confounding
Definition : A confounding variable is one which throws into confusion, an observed association between an exposure and an outcome variable

36. A confounder variable should have the following properties
(i) Be associated with the exposure of interest.
(ii) Be (independent of the exposure), related to the outcome of the interest.
(iii) It should not be in the direct chain or link between the exposure and outcome; its associations with exposure and outcome are indirect and independent.
(iv) It exerts its effect because it is differentially distributed in the two groups

37. Confounding: A Fundamental Problem of Causal Inference
Confounding is bias due to inherent (unobservable) differences in risk between exposed and unexposed populations, i.e., a lack of comparability.
Confounding is usually not a major source of bias in randomized trials (assuming sample size is large enough) because randomization tends to equalize inherent risks between treatment groups
(treated group = exposed, untreated = unexposed)

38. Confounding May lead to observation of association when none exists
May obscure an association that exists
Information on potential confounders should be collected in the study and used in analysis, otherwise they cannot be excluded as alternate explanations for findings
Confounding factors must be considered during study design .

39. Controlling confounders
The most important step is to be aware of the phenomena of confounding and to identify all Potential Confounding Variables (PCV) right at the time when the research question is being developed.
Once all PCV have been identified, action may be taken to control them either in planning stage or during analysis, by following methods :
At time designing of epidemiological study or while carrying study
Randomization
Restriction
Matching
At analysis stage
Stratification
Adjustment
Statistical modeling

40. Artificial Sweetener and Bladder Cancer
A case-control study was conducted to investigate the association between artificial sweetener and bladder cancer. Controls were selected from a group of people diagnosed with obesity related conditions. It is well known that obesity related conditions are associated with an increased likelihood of using artificial sweetener.
Could the association between artificial sweetener and bladder cancer be confounded by any external factors?

41. Confounding Can you think of any other factors?
Obesity related conditions
Artificial sweetener --- Bladder Cancer
Unhealthy lifestyle (consumption of
other artificial preservatives & carcinogens)
Can you think of any other factors?

42. Birth Order and Down Syndrome
A study was done to explore the association between birth order and Down syndrome. It was found with increasing birth order, there was also an increase in the occurrence of Down syndrome.
The prevalence of Down syndrome was 6/1000 live births at the first birth and 16/1000 live births, for birth of 5 or greater.

43. Birth Order and Down syndrome
Affected babies per 1000 live births
Kennith J. Rothman, Epidemiology and introduction, p 102

44. What do you think could affect this trend?
Mother’s age
Birth order  Down syndrome
Because mother age and birth order are highly correlated, we expect that mothers who give birth to their fifth baby might be considerably older than mothers giving birth to first baby. We also know that the risk of Down syndrome increases with maternal age.

45. Exercise on Confounding
A study was done to examine the association between Caffeine and Breast cancer. The following data was obtained:
What are the odds of caffiene intake in cases compared to controls?
Caffeine
Breast Cancer
No breast cancer
Yes 30 18 No 70 82
Total
100
OR= 30x82 = 1.95
70x18

46. Is age a confounder?
The investigators thought that the calculated OR was confounded by the effect of age. They stratified participants according to age:
Age < Age ≥ 40
OR=? OR=?
What do you conclude?
Caffeine Ca
No Ca
Yes 5 8 No 45 72
Total 50 80
Caffeine Ca
No Ca
Yes 25 10 No
Total 50 20
Remove gray parts~ matched
The relationship between caffiene intake and Breast cancer disappeared when the analysis was doen according to age groups ; that means age was the reason of breast cancer rather than caffiene intake
OR= 5x 72 = 1
45x8
OR= 25x10 = 1
25x10
Age confounds the association between caffeine intake and breast cancer

47. Exercise 2
Nurse’s Health study; a cohort study was done to determine the association between oral contraceptive use and ovarian cancer. The following data were obtained:
What is the crude RR?
Oral contraceptive
Ovarian Ca
No ovarian Ca
Total
Yes
350
200
550 No
125
325
475
400
875
Datum is singular and data plural
RR= 350÷550 = 1.65
125÷325

48. Is smoking a confounder?
The investigators thought that the calculated Risk ratio might be confounded by the effect of smoking. Data were stratified according to smoking status and relationship was studied :
Smokers Non-smokers
RR=? RR=?
OCP
Ovarian Ca
No ovarian Ca
Total
Yes 95 5
100 No
150 25
175
OCP
Ovarian Ca
No ovarian Ca
Total
Yes
298
152
450 No
100 50
150
This looks like interaction; we have not included interaction in the lecture; shall we includee it or it will be too much ? As the relation ship is coming in non smokers ; you may switch the data and one can show relationship enhanced in smokers
RR= 95÷100 = 1.1
150÷175
RR= 298÷450 = 0.99
100÷150

49. 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?

50. 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)

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

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

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

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

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

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

57. Confounding ?
Diet
CHD
Cholesterol
On the causal pathway

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

59. No Confounding
(www)

60. 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?

61. 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

62. Effect Modification
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.

63. 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)

64. 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

65. 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