Bias in performing clinical studies

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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.
For example, individuals with skin cancer might be more likely to recall using a tanning bed multiple times, since they know that tanning beds are a risk factor for skin cancer.
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. It’s also important to follow well-established protocols for data gathering, choosing the right tools and conditions, and regularly calibrating all standards and equipment.Another bias to be aware of is the Hawthorne effect.
This can occur when study participants change their behavior because they know they are being observed or studied. For example, let’s say you want to assess the adherence of health care professionals to hand hygiene practices.
It’s highly likely that their adherence will be greater than when they were not being observed. In an effort to avoid the Hawthorne effect, hidden observation can be applied; alternatively, a separate control group, independent of the variable being tested, could be used to compare responses.Next is the procedure bias, which occurs when one group is treated differently than another group.
For example, to study whether flossing prevents teeth cavities, researchers might do a more thorough dental exam for those that don’t floss, because they want to make sure they spot any cavities.
And they might do a less thorough dental exam for those that do floss, because they don’t expect to find any cavities. This could cause an underreporting of the outcome, so cavities, in the flossing group, and that would lead to an overestimation of the protective effect of flossing on cavities.
Finally, the observer-expectancy bias refers to how an observer’s perceived expectations for the studied treatment can either consciously or subconsciously influence the participant’s behavior or the actual outcome of that treatment.
For example, think of an observer measuring the blood pressure of a participant whom they know are in the experimental group, so they received a new form of treatment.
In light of the facts, the observer might expect the participant’s blood pressure to be lower than that of a participant in the control group who received a placebo.
And this might cause the observer to round down the reading of the sphygmomanometer to the nearest whole number. All right, now, both observer-expectancy bias and procedure bias can be prevented by conducting “double blind” studies, where both the researchers administering the intervention and the participants receiving it don’t know which participant is in the control group and who’s in the experimental group.
This will help ensure that any difference between the results of both groups is most likely due to the intervention that’s being tested and not external factors.
All right, as a quick recap... Some common types of bias in performing clinical studies include recall bias, where there’s a difference in the accuracy or completeness of the data retrieved from one group versus the other, and is common in retrospective studies, so it can be avoided by using written records.
Measurement bias occurs when data is collected using imperfect or inadequate methods, like inaccurate devices, and can be reduced with careful planning, choosing the right tools and conditions, and using calibrated, standardized methods.
Hawthorne effect refers to participants changing their behavior due to being aware of being observed, and can be prevented by using hidden observation or control groups.
Finally, procedure bias occurs when researchers treat participants of each group differently, whereas observer- expectancy bias occurs when the expectations or beliefs of the observer influence the outcome of an intervention; both procedure and observer- expectancy bias can be prevented by conducting “double blind” studies.