Bias in interpreting results of clinical studies

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.

So here, the factor is associated only with the outcome but not the exposure. As a result, exposure has a different outcome among different subgroups.

An example of effect modification could be that the association between obesity and colorectal cancer is more common in males than females.

In this case, the sex of the participants becomes an effect modifier. Another bias to be aware of while interpreting results is lead-time bias, where the earlier a disease is diagnosed, the longer affected individuals will seem to survive.

This most commonly occurs in the setting of screening studies. For example, let’s say a female is diagnosed with cervical cancer after developing symptoms at the age of 64, and dies at the age of 65.

This would mean that she survived for 1 year with the diagnosis of cervical cancer. In contrast, if her cancer was discovered during routine screening with a Pap smear when she was 60 years old, and she still dies at the age of 65, it would now appear as if she had a 5 year survival time.

s a result, early detection of the disease is confused with a longer survival time, but in fact we just started counting earlier.

So, in an effort to avoid lead-time bias, we could measure “back-end” survival, meaning we measure the severity of the disease at the time of diagnosis and adjust the survival accordingly.Finally, there’s length-time bias, where slowly progressive diseases are more likely to be detected earlier through screening than rapidly progressive ones, which are only detected after they become symptomatic.

As a result, there might be overestimation of the survival time of slowly progressive diseases. One way to prevent length-time bias would be to carry out a randomized, controlled trial, where participants are randomly assigned to go through a screening program or not.All right, as a quick recap… While interpreting the results of a clinical study, some common types of bias that can occur include confounding bias, lead-time bias, and length-time bias.

In confounding bias, there’s an independent factor, or confounder, that’s related to both the exposure and the outcome. This can result in an over- or underestimation of the observed association between the exposure and the outcome, and can be avoided by randomization or matching.

Conducting multiple or repeated studies may also help identify potential confounders. On the other hand, lead-time bias occurs when early diagnosis of a disease, typically by screening, falsely makes it look like there’s a longer survival time, and can be reduced by measuring “back-end” survival.

Finally, in length-time bias, there’s an overestimation of the survival of slowly progressive diseases, as they’re more commonly detected earlier through screening, and can be prevented by conducting randomized controlled