1. Presenter: Dr Himani Moderator: Dr P.R.Deshmukh
Biases
Presenter: Dr Himani
Moderator: Dr P.R.Deshmukh

2. Framework
Introduction
Terms to understand
Types of bias
Selection bias and types of selection bias
Information bias and types of information bias
How to control bias
Biases specific for case control study
Biases specific for cohort study
Biases specific for Clinical trial
Biases specific for screening programmes
Confounding

3. Introduction
Quality of clinical study depends on internal & external factors.
Studies have internal validity when reported differences between exposed & unexposed individuals can only be attributed to exposure under investigation.

4. (200 pts in SFGH clinic in July 1988)tr
infer
infer
Truth in universe
Truth in the study
Findings in study
Error
Research question
Errors
Study plan
Actual study
Errors
Error
Design
implement
Intended Sample
(200 pts in SFGH clinic in July 1988)
Target population
All iv drug absuers in
san Francisco
Actual subjects
100 pts who get studied
Phenomenon of interest (proportion infected by AIDS virus)
Actual measurements
Proportion with +ve response to ELISA test
Intended variables (proportion with Abs to AIDS virus)
External validity
Internal validity

5. Sources of Error
Random Error
Systematic Error

6. Random Error
Random error is when a value of the sample measurement diverges- due to chance alone- from that of true population value
Sources of Random error
Individual biological variation
Sampling Error
Measurement Error

7. Systematic Error/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.
Sources of Error
Basic measurement technique is wrong
Variations between observers or subjects
Systematically differentiating between 2 groups:
being compared at the point of selection or
making measurements

8. Types of bias Selection bias Information bias/Measurement bias
Bias due to confounding

9. Examples of Random Error, Bias, and confounding in the same study
In a cohort study the babies of women who bottle feed and women who breastfeed are compared:
Observation:
Incidence of gastroenteritis, as recorded in medical records, is lower in the babies who are breast feed

10. Example of Random Error
By chance, there are more episodes of gastroenteritis in the bottle-fed group in the study sample, producing a type 1 error. (When in truth breast feeding is not protective against gastroenteritis).
Or, also by chance, no difference in risk was found, producing a type 2 error (When in truth breast feeding is protective against gastroenteritis).

11. Example of Bias
The medical records of bottle-fed babies only are less complete (perhaps bottle fed babies go to the doctor less) than those of breast fed babies, and thus record fewer episodes of gastro-enteritis in them only.

12. Example of confounding
The mothers of breast-fed babies are of higher social class, and the babies thus have better hygiene, less crowding and perhaps other factors that protect against gastroenteritis. Crowding and hygiene are truly protective against gastroenteritis, but we mistakenly attribute their effects to breast feeding. This is called confounding.

13. Selection bias
Selection bias is a systematic error resulting from the way the subjects are either selected in a study or else are selectively lost to follow up.
Selection bias can cause an overestimate or underestimate of association.

14. Diseased Non diseased Exposed 100 200 Unexposed 400
Study of asbestos exposure and lung cancer, the exposure is distributed among cases and controls in target population:
Diseased
Non diseased
Exposed
100
200
Unexposed
400
True OR in target population = (100*400)/ (100*200) =2

15. If the selection probabilities for all the cells in the table are equal at 90%, the 2*2 table of selection probabilities would be:
Diseased
Non diseased
Exposed
100*0.90= 90
200*0.90 = 180
Unexposed
100*0.90 = 90
400*0.90= 360
OR= (90*360)/ (90*180) =2

16. Diseased Non diseased Exposed 100*0.90= 90 200*0.90 = 180 Unexposed
If selection probabilities are unequal, and non proportional, then selection bias will occur
Diseased
Non diseased
Exposed
100*0.90= 90
200*0.90 = 180
Unexposed
100*0.90 = 90
400*0.70= 280
OR= (90*280)/ (90*180) = 1.6

17. Self-Selection Bias Common source of selection bias
Volunteers induced bias
Individuals who volunteer for study possess different characteristics than average general population
Example: A case control study explored an association of family history of heart disease and presence of heart disease in subjects. Volunteers were recruited. Subjects with heart disease may be more likely to participate if they have family history

18. OR= 2.25 OR= 3.0 Exposed Diseased Y N 300 200 Exposed Diseased Y N
240 (80%)
120 (60%)
120
(60%)
180
Self selection bias
True
OR= 2.25
OR= 3.0

19. Berksons’ Bias Hospital selective bias
Patients with two concurrent diseases or health problems are more likely to be admitted to hospital than those with single condition
Example: people who have peptic ulcer and also who smoke are more likely to be admitted to hospital than people who have either of them.
A Case-Control study trying to evaluate relationship between smoking and peptic ulcer may find a stronger association between 2 .

20. Incidence – prevalence bias
Survivorship bias, Neyman’s Bias
Estimate the risk of disease on basis of data collected at a given point in a series of survivors rather than on data gathered during a certain time period in a group of incident cases
Case-control and crossectional study
Example: case control study to assess the protective effect of physical exercise on MI
Survivorship bias, Neyman’s Bias
Estimate the risk of disease on basis of data collected at a given point in a series of survivors rather than on data gathered during a certain time period in a group of incident cases
Case-control and crossectional study
Example: case control study to assess the protective effect of physical exercise on MI

21. Healthy Worker Effect Form of selection bias
General population is often used in occupational studies of mortality, since data is readily available, and they are mostly unexposed
Example: A comparison between health status of military and civilian population may show a better health status of soldiers because during initial medical examination during which unfit persons are excluded

22. Bias due to loss to follow up
Differential loss to follow up in a prospective cohort study on oral contraceptives and thromboembolism
Without losses TE
Normal
OC+ 20
9,980
OC- 10
9,990
RR= 2 (truth)
After 40% loss to follow up
Final sample TE
Normal
OC+ 8
5,980
OC-
5,990
RR= 1 (biased)

23. Information bias/ measurement bias
inadequate means for obtaining information about subjects in the study are inadequate.
TYPES:
Non differential missclassification bias
Differential missclassification bias

24. Nondifferential misscclassification bias
When errors in exposure or outcome status occur with approximately equal frequency in groups being compared
Equally inaccurate memory of exposures in both groups. Example:Case-control study of heart disease and past activity
Recording and coding errors in records and databases. Example:ICD9 code used in discharge summaries
Using surrogate measures of exposure.
Non-specific or broad definitions of exposure or outcome. Example: “do you smoke?” vs (how much, how often, how long) to define exposure to tobacco

25. Example : A Case- Control study comparing CAD cases & controls for history of diabetes.
True relationship
CAD
Controls
Diabetes 40 10
No diabetes 60 90
OR= (40*90)/(10*60) = 6
With non-differential Misclassification (only half of the diabetics are correctly recorded as such in case and controls)
CAD
Controls
Diabetes 20 5
No diabetes 80 95
OR= (20*95)/ (5*80)= 4.75

26. Effect: with a dichotomous exposure( eg smoking vs non-smoking), non-differential misclassification minimizes differences & causes an underestimate of effect, i.e. “bias toward null”

27. Differential misclassification
When errors in classification of exposure or outcome are more frequent in one group
Differences in accurately remembering exposures (unequal). Example: mothers of children with birth defects will remember drugs taken during pregnancy
Interviewer or recorder bias. Example:interviewerhas subconscious better about hypothesis
More accurate information in one of the groups. Example:case-control study with cases from one facility and controls from another with differences in record keeping

28. Recall Bias
People with disease may remember exposures differently (more or less accurately) than those without disease
To minimize:
Use a control group that has a different disease
Use questionnaires that are constructed to maximize accuracy and completeness
For socially sensitive questions, such as alcohol and drug use, use self-administered questionnaire instead of an interviewer
If possible, assess past exposures from pre-existing records

29. Interviewer bias
Systematic differences in soliciting, recording, or interpreting information
Minimized by
Blinding the interviewers if possible
Using standarized questionnaires consisting of closed ended, eay to understand questions
Training all interviewer to adhere to the question and answer format strictly
Obtaining data or verifying data by examining pre existing records (eg medical records or employment records)

30. Biases in Case- Control study
Selection bias
Information bias
Bias due to confounding

31. Biases in cohort study Selection bias Follow up bias Information bias
Bias due to confounding
Post hoc bias

32. Biases in clinical Trial
Selection Bias
Ascertainment bias
Consent bias
Dilution bias
Attrition bias
Analytical bias
Publication bias
Choice of question bias
Choice of population bias
Technical bias
Chance bias

33. Ascertainment Bias
This occurs when the person reporting the outcome can be biased.
Example, of homeopathy study of histamine, showed an effect when researchers were not blind to the allocation but no effect when they were.
Multiple sclerosis treatment appeared to be effective when clinicians unblinded but ineffective when blinded

34. Most frequent danger in Cluster trials
Consent bias
This occurs when consent to take part in the trial occurs AFTER randomisation.
Most frequent danger in Cluster trials

35. Dilution bias
This occurs when the intervention or control group get the opposite treatment. This affects all trials where there is non-adherence to the intervention.
For example, in a trial of calcium and vitamin D about 4% of the controls are getting the treatment and 35% of the intervention group stop taking their treatment. This will ‘dilute’ any apparent treatment effect.

36. Attrition Bias
Usually most trials lose participants after randomisation. This can cause bias, particularly if attrition differs between groups.
If a treatment has side-effects this may make drop outs higher among the less well participants, which can make a treatment appear to be effective when it is not
We can avoid some of the problems with attrition bias by using Intention to Treat Analysis, where we keep as many of the patients in the study as possible even if they are no long ‘on treatment

37. Biases in screening programmes
Volunteer bias
Lead time bias
Length time bias
Overdiagnosis bias

38. Lead time bias
Natural history of disease in hypothetical patient with colon cancer

39. Length time bias
Form of selection bias
Length time bias can occur when lengths of intervals are analysed by selecting intervals that occupy randomly chosen points in time or space
Example: fast growing tumor has shorter incubation period than slow growing tumor

40. Overdiagnosis Bias
Persons who initiate screening program have almost unlimited enthusiasm for the program. Even cytologists reading pap smears may become so enthusiastic that they may tend to overread the smears (false positive readings). Consequently the abnormal group will be diluted with women who are free of disease. If normal individuals in the screened group are more likely to be diagnosed as positive than are normal individuals in the unscreened group (eg identified as having cancer when in reality they do not), one could get a false impression of increased rates of detection and diagnosis of early-stage disease as a result of screening.

41. How to control bias
Selection bias
Sampling the cases and controls in the same way
Matching
Randomization
Using a population based sample

42. Control of measurement bias
Development of explicit, objective criteria for measuring environmental characteristics and health outcomes
Careful consistent data collection- for example, through use of standardized instruments; objectives, closed ended questionnaires; valid instruments
Careful consistent use of data instruments- for example, through use of standardized training and instruction manuals, blinding to the extent possible
Development and application of quality control/ quality assurance procedures
Use of multiple sources of data
Data cleaning and coding
Analysis and adjustment, if necessary, to take account of measurement bias

43. Confounding Mixing or blurring of effects
Researcher attempts to relate exposure to outcome but actually measures effect of 3rd factor, termed as confounding variable.
A confounding variable is associated with exposure, affects outcome, but not an intermediate link in chain of causation between exposure and outcome

44. For a variable to be a confounder
It should be a known risk factor for the disease or the outcome
It should be associated with the exposure
It should not be in direct chain or linked between the exposure and outcome
It should be differentially distributed in the two group

45. Hypothetical case control study to evaluate association between HTN and CHD
Exposed (HTN)
Cases (CHD+)
Control (CHD -)
Yes 30 18 No 70 82
Total
100
OR= 1.96
Table ; distribution of cases and control by age
Age (yr)
Cases (CHD+)
Controls (CHD-)
<40 50 80
≥40 20
Total
100
80% of control are younger than 40 yrs of age .
50% of cases are more than 40years.

46. Relationship of exposure to age
Total
Exposed (HTN+)
Not exposed
(HTN-)
%exposed
(% HTN+)
<40
130 13
117 10
≥40 70 35 50
<40 yrs, 10%exposed
>40yrs, 50%exposed
Calculation of OR after stratifying by age
Age yrs
Exposed (HTN)
Case (CHD+)
Control (CHD-)
Odds ratio
<40
Yes 5 8
5x72/45x8=360/360=1.0 No 45 72
Total 50 80
≥40 25 10
25x10/25x10=250/250=1.0 20

47. During design of epidemiological study:
Control of cofounding
During design of epidemiological study:
randomization
Restriction
matching
During analysis of study:
Stratification
Statistical modeling or multivariate analysis

48. References David L.Sackett. Biases in analytical research.
Gordis L, Epidemiology, Third Edition(2004), Elsevier Saunders.
Beaglehole R, Bonita R, Kjellstrom T, Basic Epidemiology, 2nd Edition(2006)WHO.
Bhalwar R, Vaidya R, Tilak R, Gupta R, Kunte R, Textbook of Public Health and Community Medicine. 1st Edition (2009).
Hulley SB, Cummings SR. Designing Clinical Research Williams & Wilkins.
Web P, Bain C, Pirozzo S. Essential epidemiology- An introduction for students and health professionals. Cambridge University Press2005.
Grimes DA, Schulz KF. Bias and causal association in observational research. Lancet2002; 359:248-52
Linienfeld, Stolley. Foundations of epidemiology. Third edition.