Bias in Biomedical Literature: A Reader’s Guide to Critical Appraisal

Bias in biomedical literature can greatly affect healthcare choices. If not identified, they may lead to inadequate or even dangerous medical practices. Whether you’re a clinician, student, or researcher, recognizing the prevalent biases in biomedical literature is essential for making informed decisions. This guide will help you understand how these biases infiltrate research, identify warning signs, and enhance your critical reading skills.

The Impact of Bias on Healthcare

Bias in medical studies refers to systematic errors that lead to inaccurate findings. These flaws often occur during the design, execution, or reporting phases of research. When left unidentified, they can mislead patients, doctors, and policymakers^1,2. This post will explore some types of bias you may encounter.

Selection Bias

Selection bias occurs when the participants chosen for a study don’t fully represent the target population. When this happens, the study’s results and observations might not apply to the larger group³.

How Does It Happen?

This issue primarily surfaces from the non-random selection of participants and challenges with retention. For example, using non-random sampling methods (allocation bias), like convenience or volunteer sampling, along with participant dropout (attrition bias), can create study samples that don’t effectively reflect the target population, resulting in findings that may be distorted or not easily applicable to the broader community(3).  

Red Flags

• Choosing participants in ways that aren’t random or thoroughly explained
• Not keeping the allocation under wraps can make it easier for investigators or participants to guess the group assignments
• Noticeable differences in the baseline characteristics between the comparison groups, basically indicating that they weren’t reasonably equivalent from the outset
• High or uneven rates of loss to follow-up or participant withdrawal between groups
• Recruitment from specialized settings, like tertiary care centers, or from specific populations that don’t fully represent the broader target population
• Strict or unclear criteria not only limit the applicability of findings but also may exclude important subgroups.
• Insufficient reporting of recruitment methods, randomization procedures, or attrition, making it impossible to judge the risk of bias^3,4

Learn More about Selection Bias

• The Catalogue of Bias provides real-world examples of selection bias in biomedical literature
• See Hegedus and Moody for even more causes of selection bias

Information Bias

Information bias in biomedical research occurs when systematic errors are made in measuring, classifying, or recording critical study variables, like exposures, outcomes, or confounders. This can result in distorted associations or even invalid conclusions from the study^1,5.

How Does It Happen?

Information bias can stem from participants misremembering events (recall bias), interviewers subtly influencing answers (interviewer bias), or researchers interpreting outcomes based on their expectations (observer bias). In addition, misclassification, where participants are wrongly labeled regarding exposures or outcomes, is common and can exaggerate or dilute associations. Likewise, faulty equipment or simple data entry mistakes further add to the problem. Recognizing these sources helps readers interpret study findings more critically and accurately^1,5.

Red Flag

• Dependence on self-reported data lacking objective verification (such as surveys on previous exposures or actions)
• Inadequately described, unverified, or inconsistent measurement tools or procedures
• Lack of blinding of outcome assessors or interviewers, increasing the risk of observer bias
• Inadequate detail about how exposures and outcomes were measured or classified in the methods section
• Evidence of systematic differences in data collection or measurement across study groups (differential misclassification)^1,5

Learn More about Information Bias

• Refer to the Catalogue of Bias for examples of Information Bias in biomedical literature
• Read Information Bias in Health Research: Definition, Pitfalls, and Adjustment Methods by Althubaiti

Spin Bias

Spin bias in biomedical research is the unintentional twisting of study results’ interpretations due to misleading reporting. This may happen when findings are presented in a way that highlights positive outcomes while downplaying those that aren’t significant or stretching conclusions beyond what the evidence supports⁶.

How Does It Happen?

Spin bias in biomedical research arises when authors prioritize statistically significant secondary or subgroup analyses while neglecting non-significant primary outcomes (outcome reporting bias). It also occurs when causal language in observational data discussions falsely implies direct cause-and-effect relationships, creating rhetorical biases (biases of rhetoric). Omitting nonsignificant results or study limitations can further distort the narrative, emphasizing positive findings. Industry or funding sponsors may amplify spin by urging authors to present results favorably (sponsorship bias). Even well-intentioned researchers may feel pressured by the publish-or-perish culture to simplify methods or results to fit journal space, creating an impression of conclusiveness^6–8.

Red Flags

• Present non-significant primary outcomes positively, describing a trial’s main endpoint with a p-value of 0.05 or higher as if it “trended” toward benefit or was “nearly significant”
• Emphasizing findings from secondary or subgroup analyses over the main result
• Inappropriate use of causal language in observational or diagnostic studies may incorrectly use terms like “leads to” or “results in,” implying unsupported cause-and-effect relationships
• Titles or abstracts make claims of “beneficial effects” or “dramatic improvements” that are not substantiated by the data in the main text
• Concealing nonsignificant findings or critical limitations by downplaying null results or overlooking methodological flaws and conflicts of interest in the discussion or abstract
• Overgeneralizing beyond the studied group; claiming findings apply to “all patients” or “standard care” despite strict eligibility and controlled environments
• Differences between the abstract and full text, like a “positive” interpretation in the abstract contrasted with muted or inconclusive results in the results section(8,9)

Learn More

• Check out the Catalogue of Bias to discover more about Spin Bias in biomedical literature
• O’Leary et al. provide a process for Identifying Spin Bias of Nonsignificant Findings in Biomedical Studies

Additional Biases to Know

Publication Bias

Studies indicate that research yielding positive or statistically significant results generally attracts more attention and is published more frequently than studies reporting null or negative outcomes. This phenomenon may distort the existing literature, suggesting that treatment effects are often more substantial than they truly are¹⁰.

Citation Bias

Citation bias resembles publication bias; however, rather than only publishing positive outcomes, authors tend to cite only studies that report positive results, neglecting those with null or negative findings. This tendency to preferentially cite studies showing positive outcomes and infrequently cite negative or null studies perpetuates a distorted narrative¹¹.

Confounding

Confounding factors are unseen variables that can obscure the actual relationship between a treatment and its outcome¹². For instance, initial studies suggested that hormone replacement therapy (HRT) reduced the risk of cardiovascular disease (CVD) in women. Nevertheless, a meta-analysis showed that women undergoing HRT are also more likely to receive care aimed at preventing CVD¹³.

Appraising Studies

Bias is insidious and can infiltrate various research studies. This post highlights key warning signs to watch for, yet it remains crucial to appraise evidence carefully before making decisions. Luckily, numerous tools are available to assist in this process. Below are two of my favorites.

• The Critical Appraisal Skills Programme has created appraisal checklists for various types of healthcare studies
• Risk of Bias Tools provides a list of tools that can be used to appraise studies for potential bias

Learn More

The Catalogue of Bias contains detailed information on over 50 types of bias.

Final Thoughts

Controlling for all forms of bias is nearly impossible, and at times, it cannot be avoided. Nevertheless, power lies in awareness. Once you understand the common biases in biomedical literature, you gain the tools to critically evaluate research, spot red flags, and make better decisions for yourself and others. Above all, don’t let jargon or statistical smoke screens cloud your judgment; trust your instincts, ask the hard questions, and dig deeper.

AI Disclaimer

Generative artificial intelligence (GenAI) was used to plan, locate sources, and write parts of this post. View the chat here.

References

1. Barratt H, Kirwan M. Research Methods Appropriate To Public Health Practice, including Epidemiology, Statistical Methods and other methods of enquiry including Qualitative Research Methods: Research Methods Appropriate To Public Health Practice, including Epidemiology, Statistical Methods and other methods of enquiry including Qualitative Research Methods. In: Public Health Textbook [Internet]. London: Faculty of Public Health; 2018.
2. Tejpal T, Ashurst J. Understanding Bias in Research [Internet]. OpenAnesttesia. 2025.
3. Hegedus EJ, Moody J. Clinimetrics corner: the many faces of selection bias. J Man Manip Ther. 2010 Jun;18(2):69–73.
4. Higgins JPT, Savovic J, Page MJ, Elbers RG, Sterne JAC. Chapter 8: Assessing risk of bias in a randomized trial. In: Higgins J, Thomas J, Chandler J, Cumpston, M., Li, T, Page M, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions [Internet]. 6.5. Cochrane; 2024.
5. Althubaiti A. Information bias in health research: definition, pitfalls, and adjustment methods. J Multidiscip Healthc. 2016;9:211–7.
6. Chiu K, Grundy Q, Bero L. ‘Spin’ in published biomedical literature: A methodological systematic review. PLOS Biol. 2017 Sep 11;15(9):e2002173.
7. Seehusen DA, Koren KG. Impact of industry sponsorship on research outcomes. Am Fam Physician. 2013 Dec 1;88(11):746.
8. Lazarus C, Haneef R, Ravaud P, Boutron I. Classification and prevalence of spin in abstracts of non-randomized studies evaluating an intervention. BMC Med Res Methodol. 2015 Oct 13;15(1):85.
9. O’Leary R, La Rosa GRM, Vernooij R, Polosa R. Identifying spin bias of nonsignificant findings in biomedical studies. BMC Res Notes. 2023 May 3;16(1):50.
10. Dwan K, Gamble C, Williamson PR, Kirkham JJ. Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias — An Updated Review. PLOS ONE. 2013 Jul 5;8(7):e66844.
11. Urlings MJE, Duyx B, Swaen GMH, Bouter LM, Zeegers MP. Citation bias and other determinants of citation in biomedical research: findings from six citation networks. J Clin Epidemiol. 2021 Apr 1;132:71–8.
12. Brookhart MA, Stürmer T, Glynn RJ, Rassen J, Schneeweiss S. Confounding Control in Healthcare Database Research: Challenges and Potential Approaches. Med Care [Internet]. 2010;48(6).
13. Humphrey LL, Chan BKS, Sox HC. Postmenopausal Hormone Replacement Therapy and the Primary Prevention of Cardiovascular Disease. Ann Intern Med. 2002 Aug 20;137(4):273–84.