Avoid Confirmation Bias in Clinical Research
Clinical research gets its credibility from the belief that rigorous application of the scientific method is used to produce repeatable, empirically proven results. However, it is well known that different kinds of biases can skew the clinical data that the pharmaceutical industry relies upon to make sound research and development decisions. Fortunately, there are ways to minimize this problem.
Understand Confirmation Bias and Its Consequences
The first and most important step in avoiding confirmation bias is learning to identify and understand the phenomenon. Ted Kaptchuk, MD, writing in the British Medical Journal (BMJ) notes that, “Facts do not accumulate on the blank slate of researcher’s minds, and data simply do not speak for themselves.” In other words, no matter how much researchers strive to be objective observers of the facts, the human element can still creep in and compromise objectivity. He notes that this confirmation bias occurs when facts that agree with a scientist’s preconceived ideas receive more weight than facts that contradict them.
One of the most important concepts to grasp about confirmation bias is that it is not simply an academic problem; there are real consequences that could affect pharmaceutical and other healthcare-related industries. An article published in Medscape Journal of Medicine notes that this bias influences pharmaceutical research in many ways, such as the fact that it is much more difficult to publish industry-funded trials that fail to find a statistically significant benefit to a certain treatment or drug. When pharmaceutical companies do not have full access to all research about a given medication, making clinical or business decisions about that treatment can prove difficult.
Avoiding Confirmation Bias in Medical Research
The dismissal of evidence that appears to go against preconceived notions is an issue of concern in pharmaceutical research. As an example, an article from the New England Journal of Medicine notes a recent review of trials on anti-depressants and found that almost all negative studies on these drugs went unpublished, giving the “false impression” that 93% of these trials showed positive results. However, when all trials were taken into consideration, it was found that around 51% of them had positive results, while 49% had negative results. In short, in order to get a full picture of the efficacy of a certain drug, all clinical data should be taken into account, and not just the data that proves the benefits of a certain medication.
Fortunately, there are four important ways to avoid such biases during the course of research.
1. Blind Study and Analysis
Blind studies and blind analysis can be useful tools for avoiding confirmation bias.
In an article in Nature, Professors Robert MacCoun and Samuel Perlmutter advocate the use of both blind studies (where, for example, both researchers and patients are shielded from information about who is receiving a treatment versus a placebo) and blind analysis. This is a method in which, before the researcher can access the final results of the data, analytical decisions must be made and computer analysis programs must be carefully checked for potential problems to ensure their reliability. These methods of “hiding” data from researchers are meant to help reduce confirmation bias that researchers may consciously or unconsciously harbor.
2. Establish Independent Checks
A system of checks on scientific papers can also help to maintain objectivity. Professor Alaa Althubaiti, of the College of Medicine at the King Saud bin Abdulaziz University in Riyadh, recommends that research should be subject to multiple and independent checks from other researchers and organizations to provide them with the kind of objective feedback and confirmation they need to maintain high standards for data quality. These independent checks can help avoid the potential biases of any one researcher or group of researchers.
3. Challenge Assumptions
On a more fundamental level, training researchers to constantly challenge their own assumptions and preconceptions while engaged in clinical research is also important. Dr. Kaptchuk acknowledges the importance of this by noting that, “Research data must necessarily undergo a tacit quality control system of scientific skepticism” in order to maintain its reliability.
4. Acknowledge Confirmation Bias
One of the simplest ways to avoid the pitfalls of confirmation bias is simply to recognize that the problem exists. Dr. Kaptchuk notes that at times, the simple awareness that confirmation bias can or does occur is enough to instill a certain amount of self-regulation and self-awareness that can help researchers to produce scientific research that can be relied upon — by the pharmaceutical industry and other important healthcare stakeholders.
Confirmation bias in clinical research is difficult to avoid because personal beliefs and assumptions are inescapable no matter how hard scientists strive for objectivity. The good news is that there are ways to help minimize this problem, not only through blind studies and analysis, but also through independent checks, a rigorous challenging of assumptions, and the simple acknowledgement that confirmation biases exist.
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