1. Bias
Defined as any systematic error in a study that results in an incorrect estimate of association between exposure and risk of disease.
To err is human. Most rigorously designed investigations will have potential for one or more types of biases relating to the manner in which the subjects are selected, the way in which the information is obtained, reported or interpreted
Unlike confounding, the effect of bias can not be easily evaluated, nor can bias be controlled at the stage of analysis

2. Bias
All sources of bias must be anticipated at the stage of planning of the study, and steps taken to minimize their influence at the time of collection of data
Since such errors may occur nevertheless, their role in explaining the observed association must be carefully evaluated

3. Types of bias
Selection bias
Observation (information bias)

4. Selection bias
It occurs when the patients included in the study are not representative of all eligible subjects
A common source of error in case-control studies and retrospective cohort studies, but also seen in prospective cohort studies
In the first two types of studies the exposure and disease are already present in the cases and hence a large scope for introduction of bias
If selection of cases and controls is based on different criteria, and if these in turn are related to exposure status, bias will result

5. Example
Hospital based case control study relating thromboembolism to OC use: Hospital admissions occurred more frequently amongst OC users with disease than non-users (referral bias) Colleagues referred those women who had TE and were OC users to the doctor who was testing the hypothesis

6. Another example
Estrogen use and the risk of uterine cancer: Women using estrogens experience uterine bleeding more often and hence get investigated more often than non-users of estrogen
Refusal or non-response in either study group also introduces a selection bias. If the response rate is different in cases and controls, and response rate is also related to exposure status, then bias will be introduced in any observed association between exposure and disease

7. Case control studies
What represents an unbiased control group is the most difficult question.
Control group should be one whose subjects would have been included as cases, had they had the disease. If the control group comes from a different section of community, the sampling of controls will be biased
Subject unwilling or subject not selected by the investigator: if this is linked to the exposure status, bias will be introduced.

8. Observation (information) bias
It results from systematic differences in the way data on exposure and outcome are obtained from various study groups
Recall bias: arises when individual with disease remember past exposures more vividly than the non-diseased or those with exposures report subsequent events in a different manner from those not so exposed

9. Observation (information) bias
Interviewer bias: systematic difference in soliciting, recording or interpreting information from study participants. It can occur with all study designs. It is however, a major problem with case control studies when the interviewer is not blinded to the disease status.

10. Observation (information) bias
Follow up losses: When the persons lost to follow up differ from those who remain in the cohort, with respect to both exposure and outcome, any observed association will be biased
Misclassification bias: incorrect categorization with respect to exposure or disease status: Random or non-differential: any true association will be diluted or be difficult to detect
Differential misclassification is a more serious problem and can influence the association in either direction

11. Information bias
Lead time bias: arising due to early detection of disease by using screening tests.
Example: cancer prostate diagnosed by PSA screening, or colon cancer diagnosed by routine colonoscopy

12. Control of bias: study design
Use of hospital controls in case control studies
Use well defined populations to minimize loss during follow up
Objective and uniform criteria for assessment of exposure and disease in both study groups
Blinding of interviewers, rigorous training
Use of clearly written protocols
Use of standard techniques for errors or missing data
Using dummy variables for exposure assessment