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Bias in interpreting results of clinical studies

Transcript

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.

Now, while interpreting the results of a clinical study, there are different types of biases that can occur, including the confounding bias, lead-time bias, and length-time bias.

Starting with the confounding bias, this can occur when there’s an independent factor, known as confounder, that is related to both the exposure and the outcome.

And this can result in an over- or underestimation of the observed association between the exposure and the outcome.

For example, if a clinical study is trying to figure out if there’s an association between obesity and colorectal cancer, the association might be distorted by the consumption of carcinogenic products like red and processed meat, if obese participants also happen to consume larger amounts of red and processed meat than nonobese participants.

To prevent confounding bias from distorting the results, confounders simply need to be identified and eliminated. Potential confounders can be identified by conducting multiple, repeated studies.

Confounding bias can also be reduced in crossover studies, where participants are paired to themselves and act as their own controls.

The ideal method, though, to reduce confounding bias is randomization, where each person is randomly assigned to one of the study groups.

This can help make sure all potential confounders, both identified and unknown, are equally distributed between the study groups.

An alternative could be matching or selecting participants with similar characteristics, such as according to their age, sex, BMI, or smoking status.

That way, participants assigned in each study group would be as similar as possible in terms of potential confounders.

Now, be sure you don’t confuse confounding bias with effect modification, where the participants are divided into subgroups that are stratified by a specific factor.