Bias: Bias is defined as “any process at any stage of inference which tends to produce results or conclusions that differ systematically from the truth”. Bias can arise at three stages of a study:
Following types of bias are seen in studies:
i) Selection bias: Selection bias occurs when the study population is not a representative sample of the target population. Randomization reduces selection bias. Sampling bias is a type of selection bias.
Berkson’s bias is a type of selection bias. It is based on the fact that hospitalized patients are more likely to have higher-risk exposures and more severe disease than the general population. If you look only at hospital data, you may get the false impression that there is a strong association between a risk factor and a disease. This can affect the results of a case-control study.
It can be avoided by selecting a comparable control group, for example selecting both case and control groups from hospitalized patients rather than selecting cases from hospitalized patients and controls from the general population.
ii) Attrition bias: This is a type of selection bias that occurs when loss to follow-up or withdrawal differs between the exposure and control groups.
iii) Measurement/ information/ observation or classification bias: This bias occurs due to inaccurate collection or recording of data or variables.
iv) Memory or recall bias: This is a type of measurement bias typically seen in case-control studies. Subjects in the disease group may remember exposures more accurately than subjects in the control group.
v) Confounding bias: Confounding occurs when a factor is associated with the outcome but is not a causal factor. This associated factor may be incorrectly identified as the cause. Randomization decreases the risk of confounding.
vi) Lead time bias: This bias relates to screening tests and survival. Even if people die at the same time with or without early diagnosis, screening detects the disease earlier. This can create the impression that patients live longer, when in reality they are simply diagnosed earlier.
vii) Observer bias: Observer bias occurs when the observer is aware of the patient’s treatment group, or when subjective differences between observers affect judgment, data collection, or data analysis. It can be prevented by blinding or RCTs.
viii) Hawthorne effect: When participants know they are being watched, they may change their behavior. For example, hand-washing compliance increases when staff know they are being observed.
ix) Ascertainment bias: Ascertainment bias can occur when outcomes are monitored or screened more intensely in exposed individuals than in unexposed individuals, or when outcomes are recorded differently between groups. It is related to observer, sampling, measurement, and selection bias.
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