How to Avoid Bias in a Systematic Review Process

Introduction

A systematic review is only as trustworthy as its ability to minimize bias at every stage.

Bias—systematic errors that distort findings—can creep in during literature searching, study selection, data extraction, and analysis. Even subtle mistakes in planning or execution can affect the conclusions and mislead clinicians, researchers, or policymakers.

The good news? By applying rigorous, transparent methods, you can greatly reduce the risk of bias and produce results that stakeholders can rely on.

Types of Bias in Systematic Reviews

1. Selection Bias

Occurs when the process of including or excluding studies is influenced by factors unrelated to the research question.

Example: Only selecting studies with positive results.

2. Publication Bias

Happens when published studies are more likely to report significant results, leading to overestimation of effects.

Example: Unpublished “negative” trials being left out.

3. Data Extraction Bias

Arises from inconsistent or incorrect recording of study results.

Example: Misreading a statistical value and entering it incorrectly.

4. Reporting Bias

Occurs when certain outcomes are selectively reported in the review.

Example: Focusing only on beneficial effects and ignoring adverse events.

5. Analytical Bias

Introduced during synthesis or meta-analysis.

Example: Choosing inappropriate statistical models that favor certain results.

Key Strategies to Avoid Bias

1. Pre-register Your Protocol

Use PROSPERO or similar registries to publicly document your plan before starting.
Prevents post-hoc changes that could introduce bias.

2. Use Comprehensive Search Strategies

Search multiple databases (e.g., PubMed, Embase, Cochrane Library).
Include grey literature (conference abstracts, dissertations) to reduce publication bias.

3. Apply Clear Inclusion and Exclusion Criteria

Define them in advance and apply consistently.
Have at least two reviewers screen each study.

4. Standardize Data Extraction

Use a pilot-tested form.
Extract data in duplicate to minimize human error.

5. Assess Risk of Bias in Each Study

Use tools like Cochrane RoB 2, ROBINS-I, or Newcastle-Ottawa Scale.
Integrate risk-of-bias results into your interpretation.

6. Conduct Sensitivity Analyses

Test whether results change when excluding high-risk or low-quality studies.

7. Maintain Transparency

Report all methods and decisions in your PRISMA flow diagram and appendices.

Tools to Detect and Reduce Bias

Bias Type	Detection Tool/Method
Publication Bias	Funnel plots, Egger’s test
Risk of Bias	Cochrane RoB 2, ROBINS-I
Selection Bias	Dual screening, pre-defined criteria
Analytical Bias	Independent statistical verification

Common Mistakes That Introduce Bias

Only searching in one language (language bias).
Ignoring unpublished studies.
Making last-minute changes to inclusion criteria.
Not documenting reviewer disagreements.

Conclusion

Avoiding bias in a systematic review isn’t about eliminating all uncertainty—it’s about making your methods so rigorous and transparent that bias has little room to influence the results.

When done right, your review can stand as high-quality evidence that clinicians, policymakers, and researchers can trust.