To succeed, a clinical research study must be fully impartial and free from human error. But researchers are humans, too – which means unintentional bias can seep into clinical research studys. This can, in many cases, potentially obscure trial results and render a study ineffective. Bias can occur during study design, execution, analysis, or reporting. Fortunately, researchers have tools to help eliminate bias and ensure accurate trial results.
Bias in clinical research studys
What Is Bias in clinical research studys?
Bias is “any process at any stage of inference which tends to produce results or conclusions that differ systematically from the truth,” according to researcher David L. Sackett in his 1979 paper, Bias in Analytic Research. Essentially, biases are the result of unintentional human inference – or interference – and they can cause unreliable results in clinical settings. Biases are not to be confused with random error, as the latter often results from weaknesses in methodological design or limited sampling variability.
Sources of Bias in clinical research studys
Sources of bias are defined by six distinct domains. The domains are selection, performance, detection, attrition, reporting, and other bias, the latter being a general catch-all term. Each of the following definitions are excerpted from Bias in Clinical Intervention Research published in the American Journal of Epidemiology.
Selection bias refers to systematic differences between baseline characteristics of the groups involved in the study. For example, groups with widely different demographic factors may also have different treatment outcomes. If researchers fail to address the demographic differences during participant selection, the study intervention may not be the sole reason for a specific outcome. Researchers typically address this issue through complete participant randomization with a large test group.
Performance bias refers to noticeable treatment differences between groups. This can, in some cases, contribute to a placebo effect. For example, if participants know they are receiving an active medication rather than a control, or placebo, medication, the placebo effect could change their treatment outcomes, if only temporarily. This nullifies otherwise trustworthy trial results.
The researchers who authored Bias in Clinical Intervention Research describe detection bias as a “systematic difference between groups in how outcomes are determined.” Unlike performance bias, detection bias applies to study outcome assessors, who may discover which patients received which intervention, leading to an error in outcome measurement.
Attrition bias refers to withdrawals from a study, which can create incomplete or unreliable outcome data. This type of bias may also refer to situations in which outcome reporters omit certain participants from study outcome reports, eliminating otherwise valuable data.
Reporting bias is perhaps one of the most substantial biases affecting clinical research studys. This type of bias involves differences between reported and unreported findings, either due to human error or nonstandard reporting practices. For example, a researcher may only report the most significant results of a study, a type of reporting bias known as “selective reporting bias.” For obvious reasons, this can dramatically reduce a study’s reliability.
“Other” bias is a catch-all category that includes biases not associated with the domains listed above.
Assessing and Reducing Bias
While bias in clinical settings is a problem, researchers have a number of tools to eliminate bias. Double-blind studies, or studies in which the participant and trial facilitator are unaware of assigned interventions, are an excellent example of bias reduction. This can help reduce the risk that a participant or facilitator would gain knowledge of which intervention was received, virtually eliminating performance or reporting bias. Additionally, trial registry websites are responsible for standardizing reporting protocols. This can help ensure that reputable publications report all outcomes – not just the outcomes that suit a certain study.
While bias is a real issue in clinical research study settings, impartial studies are possible. Facilitators must become familiar with the types of bias and work to design studies that prioritize reliable data.
Have you ever thought about participating in a clinical research study? You could be a part of history! And QPS Missouri is looking for new participants. Since opening its doors in 1994, QPS Missouri has conducted over 1,000 FDA-regulated studies, paying out over $35 million to local participants. Your local participation could have a global impact, as QPS is an international leader in contract research with facilities in North America, Europe, and Asia. Our mission is to accelerate the development of drugs worldwide by enabling breakthroughs in pharmaceutical innovation. This includes several pediatric studies across several age ranges. If you would like to join us in this crucial healthcare mission, consider applying for a clinical research study.
To get started, you simply need to fill out an online application. Within 48 business hours, a recruiting coordinator will contact you for your pre-screening assessment. To learn more, please visit the QPS Missouri website, review the study participation process, or check out our list of frequently asked questions.