Understanding Bias in Research

Conceptual Framework of Bias in Statistics

Definition of Bias

• Bias is an error that causes results to differ from the true value.^1,2
  • This ultimately leads to incorrect conclusions being made regarding the relationship between exposure and effect.²
  • It also weakens the accuracy of a study’s results, which makes it harder to extrapolate and apply these findings to other populations.²
• Bias can influence the accuracy of study results in different ways, depending on the direction in which it skews findings¹ (Table 1).

Importance of Recognizing Bias

• Failure to address bias can result in flawed policy and compromised patient outcomes.³
• Anesthesiology research can yield more accurate and clinically relevant findings by addressing bias through comprehensive design, data collection, and advanced statistical methods.

Classification of Bias

• Bias can occur at various stages of research, from study design and participant selection to data collection, analysis, and reporting.^1,2
• Classifying bias helps identify the source of error and allows researchers to create strategies for minimizing or eliminating its impact.
• The primary types of bias include selection bias, information bias, performance bias, attrition bias, confounding, and publication bias.¹

Primary Types of Biases (Table 2)

1. Selection Bias: Occurs when participants are not representative of the target population.^1,2

2. Information Bias: Results from systematic errors in data collection or measurement^1,2,4 (Table 3).

3. Performance Bias: Occurs when there are differences in care between groups (i.e., other than the intervention being studied)²

• To minimize performance bias, investigators can group patients by an anesthesiologist, assigning all patients treated by the same provider to one study group.²

4. Attrition Bias: Results from unequal loss to follow-up between study groups^4,5

• In anesthesiology trials, patients lost to follow-up due to complications may skew results.
• Sensitivity analyses can help address this bias by testing the impact of missing data.⁵

5. Confounding: Occurs when an external variable influences both the exposure and the outcome, creating a false association^1,2,4 (Table 4).

6. Publication Bias: Occurs when studies with positive or statistically significant results are more likely to be published than those with negative or null results^1,2

a. This leads to an overestimation of treatment effects.

Addressing Bias in Anesthesiology Research

• In anesthesiology research, minimizing bias is essential to produce accurate, reliable, and generalizable results.
• The strategies highlighted below (Figure 1) are essential for reducing different types of bias and enhancing the validity of research findings.