Selection Bias in Statistical Analysis
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- This is a
common type of systematic error that occurs in studies where there is:
- Inappropriate selection of patients
- Unrepresentative sample of the target population
- Insufficient retention of subjects
- Attrition Bias:
- Occurs when there is a high loss of study subjects within a study (eg., lost to follow up) and the remaining sample now differs from the original sample being studied that was supposed to be representative of the target population.
- Berkson Bias:
- The disease being studied is only being done from patients
in the hospital that may not lead to results representative of the target
population.
- Neyman Bias:
- Occurs when the exposure occurs before the assessment of the disease resulting in missed cases that die early or happen to recover.
- Nonresponse Bias:
- Occurs when this is a poor response rate of the desired
sample population where the responders now are different from the
nonresponders.
- Sampling Bias:
- Occurs when a study does not randomize patients at all or does not appropriately randomize subjects to where there the study population does not represent the target population.
- If selection bias is present it can lead to differences in the measure of association between the sample versus target population.
- For example, differences can occur with the incidence rate or odds ratio and thus is not reflective of the true difference between the groups studied and the target population.
- This type of bias occurs often with surveys where a high nonresponse rate leading to a possible difference between those who fill out the survey from the majority who did not.
Background
Common Types of Selection Bias
Considerations
