QUIPS Risk of Bias Tool
Assess risk of bias in prognostic factor studies using the QUIPS framework (Hayden et al., 2013) with 6 domains, traffic-light visualization, summary bar charts, and publication-ready PNG export.
How to Use
Add your prognostic factor studies and enter their names. Click each colored circle to cycle through judgments: + Low risk, ! Moderate risk, − High risk, ? N/A. Assess each domain using the QUIPS prompting items (see the guidance below), then set an Overall judgment per study yourself. Hayden et al. (2013) suggest a study may be rated low risk of bias overall when all domains, or the most important domains you defined in advance, are rated low; pre-specify your rule in the protocol. Export the table as PNG, SVG, PDF, or Word for your manuscript, or the data as CSV / Excel.
QUIPS domain prompting items (assessment guidance)
D1: Study Participation
Bias concern: The study sample may not adequately represent the population of interest.
- Adequate participation in the study by eligible persons.
- Description of the source population or population of interest.
- Description of the baseline study sample.
- Adequate description of the sampling frame and recruitment.
- Adequate description of the period and place of recruitment.
- Adequate description of inclusion and exclusion criteria.
D2: Study Attrition
Bias concern: Loss to follow-up may be related to key characteristics, biasing results.
- Adequate response rate for study participants.
- Description of attempts to collect information on participants who dropped out.
- Reasons for loss to follow-up are provided.
- Adequate description of participants lost to follow-up.
- No important differences between participants who completed the study and those who did not.
D3: Prognostic Factor Measurement
Bias concern: The prognostic factor may be measured differently across participants.
- A clear definition or description of the prognostic factor is provided.
- Method of prognostic factor measurement is adequately valid and reliable.
- Continuous variables are reported or appropriate cut points are used.
- The method and setting of prognostic factor measurement is the same for all participants.
- Adequate proportion of the study sample has complete data for the prognostic factor.
- Appropriate methods of imputation are used for missing prognostic factor data.
D4: Outcome Measurement
Bias concern: The outcome may be measured differently across participants.
- A clear definition of the outcome is provided.
- Method of outcome measurement used is adequately valid and reliable.
- The method and setting of outcome measurement is the same for all participants.
D5: Study Confounding
Bias concern: Important confounders may not be accounted for.
- All important confounders are measured.
- Clear definitions of the important confounders measured are provided.
- Measurement of all important confounders is adequately valid and reliable.
- The method and setting of confounding measurement are the same for all participants.
- Appropriate methods are used if imputation is used for missing confounder data.
- Important potential confounders are accounted for in the study design.
- Important potential confounders are accounted for in the analysis.
D6: Statistical Analysis and Reporting
Bias concern: The analysis or selective reporting may distort the association.
- Sufficient presentation of data to assess the adequacy of the analytic strategy.
- Strategy for model building is appropriate and is based on a conceptual framework or model.
- The selected statistical model is adequate for the design of the study.
- There is no selective reporting of results.
Prompting items per Hayden JA et al., Ann Intern Med. 2013;158:280-286.
Load sample data to see how the tool works, or clear all fields to start fresh.
| Study | D1 Study Participation | D2 Study Attrition | D3 Prognostic Factor Measurement | D4 Outcome Measurement | D5 Study Confounding | D6 Statistical Analysis and Reporting | Overall Overall | |
|---|---|---|---|---|---|---|---|---|
QUIPS (Hayden JA et al., 2013)
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How to Use This Tool
Add Your Studies
Click Add Study to create a row for each prognostic factor study included in your systematic review. Enter the study identifier (e.g., Author Year) and your progress will be automatically saved to browser local storage so you can return later without losing work.
Rate Study Participation
Evaluate whether the study population was adequately described, whether the sample was representative of the population of interest, and whether inclusion and exclusion criteria were appropriate. Assign Low, Moderate, or High risk of bias based on the prompting items for this domain.
Rate Study Attrition
Assess the completeness of follow-up data. Consider the proportion of participants with complete data, whether reasons for loss to follow-up were reported, and whether baseline characteristics differed between completers and non-completers. High attrition (typically above 20%) without adequate handling raises concern.
Rate Prognostic Factor and Outcome Measurement
For the prognostic factor domain, evaluate whether the factor was measured validly, reliably, and consistently across participants. For outcome measurement, assess whether the outcome was defined clearly and measured using a valid method with appropriate blinding to the prognostic factor status.
Rate Confounding and Statistical Analysis
For confounding, evaluate whether important confounders were identified, measured, and accounted for in the analysis. For statistical analysis, assess whether the analytical strategy was appropriate, whether model assumptions were satisfied, and whether results were reported completely including effect estimates and precision.
Generate Chart and Export
Review the traffic light table showing per-study, per-domain judgments and the summary bar chart displaying the proportion of studies at each risk level across all six domains. Download both visualizations as high-resolution PNGs suitable for journal submission.
Choosing an appraisal tool by study design is covered in our overview of bias assessment tools by study design.
We appraise prognostic factor studies with QUIPS as part of the review.
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Key Takeaways for QUIPS Assessment
Six domains target distinct bias sources
QUIPS evaluates Study Participation, Study Attrition, Prognostic Factor Measurement, Outcome Measurement, Study Confounding, and Statistical Analysis and Reporting. Each domain has its own set of prompting items that guide assessors through the evaluation. Unlike numeric scales that collapse quality into a single score, QUIPS preserves domain-level information that is more useful for sensitivity analyses and GRADE assessments.
Prognostic vs etiologic studies require different tools
QUIPS is designed for studies investigating whether a factor predicts an outcome over time, not whether an exposure causes an outcome. If the study question is causal (does smoking cause lung cancer?), ROBINS-I or RoB 2 may be more appropriate. If the question is prognostic (does tumor size predict survival?), QUIPS provides the relevant domain structure. The distinction is subtle but affects which biases are most important to assess.
High attrition is a critical concern in prognosis research
Prognostic studies often involve long follow-up periods, making loss to follow-up a pervasive threat. When attrition exceeds 20% and is differential (related to the prognostic factor or outcome), the remaining sample may no longer represent the original cohort. QUIPS prompting items for the Attrition domain ask whether attrition reasons were reported, whether completers differed from non-completers, and whether missing data were handled appropriately.
Confounding is the most complex domain
Unlike randomized trials where randomization balances confounders, observational prognostic studies must identify, measure, and statistically adjust for all important confounders. The QUIPS Confounding domain asks whether key confounders were defined, measured validly, and accounted for in the analysis. Residual confounding from unmeasured variables remains a limitation that cannot be fully addressed through statistical adjustment alone.
Relationship to GRADE for prognosis
The GRADE framework adapted for prognosis (Iorio et al., 2015) starts with high certainty for prospective cohort studies and allows downgrading based on risk of bias, inconsistency, indirectness, imprecision, and publication bias. QUIPS domain ratings directly inform the risk of bias downgrading decision. A pattern of High risk across the Confounding and Attrition domains may justify downgrading certainty by one or two levels.
When to use QUIPS vs RoB 2 or ROBINS-I
Use QUIPS when included studies examine the independent association between a prognostic factor and an outcome (e.g., does baseline depression severity predict treatment response?). Use RoB 2 for randomized trials of interventions. Use ROBINS-I for non-randomized studies that compare the effects of two or more interventions. The study design matters less than the research question: a cohort study asking a prognostic question should be assessed with QUIPS regardless of whether it also reports intervention effects.
Assessing Bias in Prognostic Factor Studies: The QUIPS Framework
Systematic reviews of prognostic factors require specialized tools that address the unique methodological challenges of prognosis research. The QUIPS tool (Quality In Prognosis Studies) provides a structured, domain-based framework developed by Hayden et al. (2013) and endorsed by the Cochrane Prognosis Methods Group. Its six domains (Study Participation, Attrition, Prognostic Factor Measurement, Outcome Measurement, Confounding, and Statistical Analysis) each target a distinct source of bias, with prompting items that help two independent reviewers reach reproducible judgments. In its development and testing across prognostic systematic reviews (Hayden et al., 2013), the tool showed acceptable inter-rater reliability when assessors received adequate training.
The Study Participation domain evaluates whether the enrolled sample adequately represents the population of interest. Selection bias occurs when the study sample differs systematically from the target population in ways that affect the prognostic association. The Study Attrition domain is particularly critical in prognosis research because longitudinal studies often span years or decades, creating multiple opportunities for participant dropout. When attrition is related to both the prognostic factor and the outcome (informative censoring), the observed association may be biased in either direction. Sensitivity analyses using multiple imputation or inverse probability weighting can partially address this concern.
Prognostic factor measurement must be valid, reliable, and consistently applied across all participants at a relevant time point. If the factor is measured with substantial error (low reliability), the observed association will be attenuated toward the null, potentially causing reviewers to underestimate the true prognostic value. The outcome measurement domain similarly requires that the outcome is defined clearly, measured with a validated instrument, and assessed without knowledge of the prognostic factor status. Differential misclassification of the outcome based on knowledge of the prognostic factor can bias the association in either direction.
The confounding domain represents the most complex assessment in QUIPS. Unlike randomized trials where randomization balances measured and unmeasured confounders, observational prognostic studies must identify all important confounders a priori and adjust for them analytically. Key confounders typically include age, sex, disease severity, comorbidities, and treatment received. When studies fail to adjust for known confounders, the observed prognostic association may reflect confounding rather than a true independent predictive relationship. Directed acyclic graphs (DAGs) can help review teams identify which variables are true confounders versus mediators or colliders.
QUIPS assessments feed directly into the GRADE framework adapted for prognosis (Iorio et al., 2015; Huguet et al., 2013), where risk of bias is one of five domains determining overall certainty of evidence. Reviewers should perform sensitivity analyses excluding studies with high risk on key domains, particularly confounding and attrition, to test the robustness of their pooled estimate. The forest plot generator can visualize these subgroup analyses, showing how the meta-analytic estimate changes when high-risk studies are removed. When heterogeneity is substantial, explore whether methodological quality differences explain variation across studies using the leave-one-out sensitivity analysis tool.
Selecting the correct bias assessment tool is essential for methodological rigor. For randomized trials, use RoB 2 assessment tool. For non-randomized intervention studies, use ROBINS-I assessment tool. For cohort and case-control study quality scoring, the Newcastle-Ottawa Scale offers a star-based system. QUIPS fills the specific niche of prognostic factor research, providing domain-level assessments that are more informative than a single numeric score and more aligned with the causal questions inherent to prognosis. When your systematic review includes both prognostic and etiologic questions, consider using QUIPS for the prognostic analyses and ROBINS-I for the causal inference analyses, documenting the rationale for tool selection in your methods section.
Frequently Asked Questions
What is QUIPS and when should I use it?
QUIPS (Quality In Prognosis Studies) is a validated tool developed by Hayden et al. (2013) for assessing risk of bias in studies that investigate prognostic factors. Use QUIPS when your systematic review includes cohort studies, case-control studies, or other observational designs that examine the association between a prognostic factor (exposure) and a health outcome over time. It is specifically designed for prognosis research rather than intervention studies.
What are the 6 QUIPS domains?
QUIPS evaluates bias across six domains: (1) Study Participation, which assesses selection and enrollment methods; (2) Study Attrition, which evaluates loss to follow-up and completeness of data; (3) Prognostic Factor Measurement, which judges whether the factor of interest was measured validly and reliably; (4) Outcome Measurement, which assesses whether the outcome was defined and measured appropriately; (5) Study Confounding, which evaluates whether important confounders were identified and accounted for; and (6) Statistical Analysis and Reporting, which judges whether the analytical methods were appropriate and fully reported.
How does QUIPS differ from ROBINS-I and RoB 2?
QUIPS is purpose-built for prognostic factor studies that examine associations between patient characteristics and outcomes over time. ROBINS-I assesses risk of bias in non-randomized studies of interventions (treatments), while RoB 2 evaluates randomized controlled trials. The key distinction is the study question: if the study asks whether a factor predicts an outcome (prognosis), use QUIPS. If it asks whether an intervention causes an effect, use ROBINS-I or RoB 2 depending on the design.
How do I determine the overall QUIPS judgment for a study?
QUIPS does not prescribe a formal algorithm for an overall judgment in the same way ROBINS-I does. Hayden et al. (2013) suggest that a study may be rated low risk of bias overall when all domains, or the most important domains defined in advance, are rated low. Review teams then set their own rule for moderate and high overall risk, so document your decision rule in your review protocol and apply it consistently. This tool leaves the overall judgment to you.
What are prompting items in QUIPS?
Each QUIPS domain includes a set of prompting items (also called consideration items) that guide the assessor through the evaluation. These items highlight specific methodological aspects to consider within each domain. For example, in the Study Participation domain, prompting items address the adequacy of the study population description, the representativeness of the sample, the inclusion and exclusion criteria, and the baseline characteristics. Assessors use these items to inform their overall domain judgment of Low, Moderate, or High risk of bias.
How does QUIPS relate to GRADE for prognosis?
The GRADE framework for prognosis (Iorio et al., 2015) uses QUIPS assessments as one of the factors when rating the overall certainty of prognostic evidence. Within GRADE for prognosis, risk of bias assessed by QUIPS can lead to downgrading the certainty of evidence from high to moderate, low, or very low. Systematic reviews of prognostic factors should present both the individual study QUIPS assessments and the overall GRADE rating to give readers a complete picture of evidence quality.
Related Research Tools
Assessing randomized trials instead? Use our Cochrane RoB 2 assessment tool with 5 domains and traffic-light visualization. For non-randomized intervention studies, apply the ROBINS-I bias assessment tool with 7 domains. For cohort and case-control study quality scoring, use the Newcastle-Ottawa Scale calculator. When you are ready to visualize your pooled results, generate publication-ready figures with our forest plot generator.
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Research Gold Team is our group of PhD methodologists and statisticians who deliver our systematic review, meta-analysis and statistical analysis work. The same team writes and checks the guides and tools on this site.
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