AUG 16, 2023 3:00 AM PDT

Clinical Trial Overview: What is Bias and How Can We Reduce It?

WRITTEN BY: Katie Kokolus

As we complete our series about clinical trials, we will explore some language often used when discussing research studies involving human subjects.  Clinical trials highlight a critical component of the healthcare process.  Doctors and researchers conduct clinical trials to develop new cancer drugs and therapies and design novel screening tools to ensure earlier cancer diagnoses.  Some clinical trials aim to uncover approaches to enhance the quality of life for patients with uncurable cancers.  Other clinical trials seek strategies to prevent the onset and development of cancer in the first place.

If you haven’t already, check out the previous articles in this “Clinical Trial Overview” series!  We have covered the different types of clinical trials, the progressive phases clinical trials go through before gaining implementation in mainstream healthcare, eligibility criteria used to ensure patients volunteering for clinical trials remain safe, and how to find applicable clinical trials for yourself or a loved one. 

Let's talk about a few concepts you might come across to make sure you can fully comprehend all the great articles and news coverage you see regarding the exciting and innovative cancer clinical trials.

Bias describes flaws and imperfections in clinical trial design, methodology, or interpretation.  Researchers must understand and consider all potential sources of bias to limit it and ensure their clinical trials result in interpretable, valuable data.  When the study team does not control for sources of bias, they may reach inaccurate conclusions from the clinical trial. 

A degree of clinical trial bias can result from random error due to the chance.  The smaller the trial, the greater the potential effect of random error bias.   

Researchers use randomization to reduce bias in the design and implementation of clinical trials where groups of patients receive different interventions.  Randomization typically comes into play in later stage (some phase 2 and most phase 3) trials with large numbers of participants.  Patients receiving the new intervention under investigation make up the “interventional” group, and those receiving the current standard intervention make up the “control” group.  In a simple trial consisting of only these two groups, the effectiveness of the study intervention is assessed by comparing the outcomes of the interventional and control groups. 

Selection bias can come into play if researchers make errors in the randomization process.  To limit selection bias, the patients randomized into each group will resemble one another as closely as possible.  Different studies may consider various factors, including age, gender, cancer stage, and prior lines of treatment when randomizing patients. 

Another method to limit clinical trial bias involves “blinding.”  Participants do not know their group assignment in “single-blinded” clinical trial designs.  In this case, a patient may receive the new intervention under investigation, but may also receive the standard intervention.  This can prevent patients from over- or under-reporting symptoms or effects based on the intervention they receive.  Similarly, in “double-blinded” clinical trials, neither the patient nor the doctor knows the group assignment.  Blinding can limit bias related to inaccurate reporting and reduce the chances of factors unrelated to the intervention affecting the results. 

It remains nearly impossible to eliminate all biases in clinical trials.  However, when the clinical trial team carefully considers potential sources of bias in the design and implementation of the study, the likelihood of generating meaningful data rises.


Sources: Am J Clin Epidemiol, Am J Orthod Dentofacial Orthop, Eye

About the Author
Doctorate (PhD)
I received a PhD in Tumor Immunology from SUNY Buffalo and BS and MS degrees from Duquesne University. I also completed a postdoc fellowship at the Penn State College of Medicine. I am interested in developing novel strategies to improve the efficacy of immunotherapies used to extend cancer survivorship.
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