Bias is a systematic error in the design or execution of a study that may lead to invalid conclusions. Bias can have a big impact on the measurement of the association between exposure and outcome.
The two main types of bias are:
- Selection bias (e.g. loss to follow-up is different between the intervention and control group)
- Information bias (e.g. collected information is more accurate in the intervention group compared to the control group)
In contrast to the terminology that is often used in published literature, selection bias is not the right term to indicate confounding by indication. This is the case when the intervention group has a different level of disease severity compared to the control group.
To avoid selection bias you need to make sure you select the intervention and control groups carefully and you tried to minimize loss to follow-up. To avoid information bias you need to use high-quality and validated measures for outcomes (minimizing misclassification), mask the study hypothesis when interviewing patients, use blinding when possible, and standardize your follow-up procedures.
By thinking ahead about bias in your study protocol you can avoid getting into trouble later on.