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Free MOOSE Flow Diagram Generator for Meta-Analysis of Observational Studies

Generate the MOOSE study selection flow diagram for your meta-analysis of observational studies in epidemiology. Live auto-calculation, 300/600 dpi PNG and fully editable PowerPoint exports. No data leaves your browser.

In Simple Words: When you synthesise results from multiple observational studies — cohorts, case-control studies, cross-sectional surveys, or ecological analyses — you are conducting a meta-analysis of observational studies. Unlike meta-analyses of randomised trials (which use PRISMA), these studies have particular sources of heterogeneity and confounding that require specific reporting. The MOOSE statement (Meta-analysis Of Observational Studies in Epidemiology) was published in JAMA in 2000 and defines the reporting standard for this study type.

This tool generates the MOOSE study selection flow diagram showing how many studies were identified, screened, assessed, and included in the meta-analysis. The counts auto-calculate at each stage from the numbers you enter.

When to Use the MOOSE Flow Diagram

Use MOOSE whenever you are conducting a meta-analysis of observational studies — specifically studies with cohort, case-control, or cross-sectional designs. MOOSE is also used for meta-analyses of surveillance data, registry-based studies, and ecological analyses. It is distinct from PRISMA 2020, which is designed for meta-analyses of interventional studies (primarily randomised trials).

If your review includes observational studies but does not pool effect estimates, use PRISMA 2020. For the statistical analysis of pooled observational data, see the meta-analysis calculator and the heterogeneity calculator.

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Worked Examples

Example 1 — Meta-analysis of occupational exposures and lung cancer

A meta-analysis of silica dust exposure and lung cancer risk searched PubMed (n=1,500), Embase (n=1,200), and Web of Science (n=300) — 3,000 articles identified. After 850 duplicates removed, 2,150 were screened. 1,800 were excluded at title/abstract (non-occupational, no lung cancer outcome, wrong population). 350 were assessed in full text. 210 were excluded: wrong exposure (80), no adjusted odds ratio (70), conference abstract (60). 140 studies in meta-analysis. Enter 3,000 identified, 850 duplicates, 1,800 screened excluded, 210 full-text excluded — the diagram auto-calculates 140 included.

Example 2 — Meta-analysis of dietary factors and colorectal adenoma risk

A meta-analysis of prospective cohort and case-control studies of dietary fibre and colorectal adenoma searched three databases yielding 980 records. After 200 duplicates removed and 650 excluded at screening, 130 were assessed in full. 91 were excluded (wrong outcome: 45, unadjusted estimates only: 30, ecological design: 16). 39 studies entered the meta-analysis. The MOOSE flow diagram captures this four-stage selection process in exactly the same structure.

Assumptions and Requirements

  • The MOOSE flow applies to study-level rather than participant-level counts. Unlike PRISMA or STROBE, the numbers in a MOOSE flow diagram represent studies or publications, not individual participants. State this clearly in your figure caption.
  • Exclusion reasons must be specific to observational study methodology. Common MOOSE-specific exclusion reasons include “unadjusted estimates only,” “ecological design,” “no measure of association reported,” and “insufficient data for meta-analysis.”
  • Grey literature and hand-searching should be documented. MOOSE checklist item 6 requires documentation of grey literature searches. If you searched conference proceedings, dissertations, or contacted authors, include these in the identified count.

Common Mistakes

  • Using PRISMA instead of MOOSE for observational meta-analyses. PRISMA was designed for interventional studies. While some journals accept PRISMA for any systematic review, MOOSE is the methodologically correct template for meta-analyses of observational data and reviewers with epidemiology expertise will notice.
  • Failing to document the heterogeneity assessment in the diagram. The MOOSE checklist requires reporting of heterogeneity analysis. While the flow diagram itself does not contain statistical outputs, the figures section should include a forest plot generated with the meta-analysis calculator.
  • Confounding as an exclusion reason. Do not exclude studies from a meta-analysis because they did not adjust for specific confounders — instead, adjust for confounding in the meta-regression, document unadjusted studies, and perform sensitivity analyses. Excluding because “the exposure is confounded” is not valid.
  • Reporting study counts rather than effect estimates. The MOOSE flow counts studies included, but the Analysis box implicitly refers to studies contributing to the meta-analytic pool — some included studies may contribute to only one of several pooled outcomes.

Frequently Asked Questions

Is MOOSE still current — has it been updated since 2000?
MOOSE 2000 (Stroup et al., JAMA) remains the standard reference for meta-analyses of observational studies. There has been active discussion about an update, and some journals accept PRISMA-S (search reporting) as a supplement, but MOOSE is the primary checklist and its flow diagram is the expected figure for this study type.
Should I register my observational meta-analysis in PROSPERO?
Yes — PROSPERO accepts registrations for systematic reviews of observational studies, including those planning a meta-analysis. Registration strengthens the credibility of your study by demonstrating prospective planning and preventing selective reporting of outcomes.
My meta-analysis includes both RCTs and observational studies — which diagram do I use?
Use PRISMA 2020. A mixed-design review that synthesises both RCTs and observational studies should follow PRISMA 2020 as the primary template; many authors add a note describing the observational study subset using MOOSE criteria. Check your target journal’s policy.
Can I use this for a dose-response meta-analysis?
Yes — dose-response meta-analyses of observational data should use MOOSE. The study selection flow is identical regardless of whether you are pooling relative risks, odds ratios, or dose-response slopes. The statistical specifics of the dose-response modelling are documented in Methods, not the flow diagram.

Related Free Tools

References

  1. Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283:2008–2012. PMID 10789670.
  2. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. PMID 33782057.
  3. Higgins JPT, Thomas J, Chandler J, et al. Cochrane Handbook for Systematic Reviews of Interventions version 6.4. Cochrane; 2023. Available from: www.training.cochrane.org/handbook.

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