Clinical studies are a type of scientific research study performed on human volunteers, or participants, to help determine the safety and effectiveness of a therapeutic intervention, such as a new medication, vaccine, device, or procedure.
Now, systematic errors due to inaccuracy in clinical studies might result in bias, which refers to an incorrect conclusion about the effects of the therapeutic intervention.
This conclusion may result in an inaccurate representation of the relationship between an exposure and an outcome, which means that the clinical study lacks internal validity.
In addition, these results can’t be applied to the general population, which means that they lack external validity.
There are different types of biases that can occur while performing clinical studies, including the recall bias, measurement bias, Hawthorne effect, procedure bias and observer- expectancy bias.
Starting with recall bias, which is common in case control studies. These are a type of retrospective clinical study that compares the history of two groups of people.
One group includes those that have a certain outcome, called cases, and the other group includes those that don’t have a certain outcome, called controls; to see if they’ve been exposed to different things that may have led to or protected from the outcome.
Now, recall bias means there can be a difference in the accuracy or completeness of the data retrieved between the cases versus the controls, and this could lead to an over- or underestimation of the exposure.
One reason for recall bias is that individuals in the case group remember exposures differently than those that are in the control group.
On the other hand, individuals without skin cancer might recall using a tanning bed fewer times than they actually did, simply because each visit blurs into the next and can be forgotten.
In this example, the case group might be over reporting their tanning bed use, whereas the control group might be underreporting their tanning bed use, which will make it seem like tanning beds are more harmful than they actually are.
To help reduce recall bias, researchers often use written records, like medical records, to verify the information collected from individual interviews, or they can study exposures that happened in the recent past.
It’s also important to distinguish recall bias from poor recall, which is when people have trouble remembering events because a long time has passed between the exposure and the outcome.
Then there’s measurement bias, which is a type of bias that can occur when researchers use imperfect or inadequate methods to collect data.
These could be due to inaccurate devices, environmental conditions in the laboratory, or using self-reported measurements.
An example of this is a study based on the measurements of a defective spirometer or an imprecise digital scale. As a result, the observed and reported measurements would differ from the actual values.
Now, measurement bias can be reduced or eliminated by carefully planning each step of the research procedure.