STROBE Compliance Checker
Audit observational study manuscripts against the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) 22-item checklist.
Workflow
- Read the full manuscript
- Identify the study design: cohort, case-control, or cross-sectional
- Walk through each item below, noting design-specific sub-items
- For each item, assign: Reported / Partial / Missing / N/A
- Quote the relevant manuscript text (with line/page reference) as evidence
- Output a compliance summary table + actionable fixes for Missing/Partial items
STROBE Checklist (22 Items)
Title and Abstract
| # | Topic | Requirement |
|---|
| 1a | Title | Indicate the study design with a commonly used term in the title or abstract |
| 1b | Abstract | Provide an informative and balanced summary of what was done and found |
Introduction
| # | Topic | Requirement |
|---|
| 2 | Background/rationale | Explain the scientific background and rationale for the investigation |
| 3 | Objectives | State specific objectives, including any prespecified hypotheses |
Methods
| # | Topic | Requirement |
|---|
| 4 | Study design | Present key elements of study design early in the paper |
| 5 | Setting | Describe setting, locations, relevant dates (recruitment, exposure, follow-up, data collection) |
| 6a | Participants | Cohort: eligibility criteria, sources/methods of selection, methods of follow-up. Case-control: eligibility, case ascertainment, control selection, rationale. Cross-sectional: eligibility, sources/methods of selection |
| 6b | Participants | Cohort/Case-control: For matched studies, give matching criteria and number matched |
| 7 | Variables | Clearly define all outcomes, exposures, predictors, confounders, effect modifiers; give diagnostic criteria |
| 8 | Data sources | For each variable, give sources of data and methods of assessment; describe comparability across groups |
| 9 | Bias | Describe any efforts to address potential sources of bias |
| 10 | Study size | Explain how the study size was arrived at |
| 11 | Quantitative variables | Explain how quantitative variables were handled; describe groupings and rationale |
| 12a | Statistical methods | Describe all statistical methods, including confounding control |
| 12b | Statistical methods | Methods for subgroups and interactions |
| 12c | Statistical methods | How missing data were addressed |
| 12d | Statistical methods | Cohort: how loss to follow-up was addressed. Case-control: how matching was addressed. Cross-sectional: sampling strategy methods |
| 12e | Statistical methods | Describe any sensitivity analyses |
Results
| # | Topic | Requirement |
|---|
| 13a | Participants | Report numbers at each stage of study (eligible, examined, confirmed, included, completed, analysed) |
| 13b | Participants | Give reasons for non-participation at each stage |
| 13c | Participants | Consider use of a flow diagram |
| 14a | Descriptive data | Characteristics of participants (demographic, clinical, social) and information on exposures/confounders |
| 14b | Descriptive data | Number of participants with missing data for each variable |
| 14c | Descriptive data | Cohort: Summarise follow-up time (average and total) |
| 15 | Outcome data | Cohort: numbers of outcome events or summary measures over time. Case-control: numbers in each exposure category. Cross-sectional: numbers of outcome events or summary measures |
| 16a | Main results | Unadjusted estimates and confounder-adjusted estimates with precision (95% CI). State which confounders and why |
| 16b | Main results | Report category boundaries when continuous variables were categorised |
| 16c | Main results | If relevant, translate relative risk into absolute risk for a meaningful time period |
| 17 | Other analyses | Report subgroup analyses, interactions, sensitivity analyses |
Discussion
| # | Topic | Requirement |
|---|
| 18 | Key results | Summarise key results with reference to study objectives |
| 19 | Limitations | Discuss limitations: sources of bias/imprecision, direction and magnitude of potential bias |
| 20 | Interpretation | Cautious overall interpretation considering objectives, limitations, multiplicity, similar studies |
| 21 | Generalisability | Discuss external validity of results |
Other Information
| # | Topic | Requirement |
|---|
| 22 | Funding | Source of funding and role of funders |
Design-Specific Attention
| Design | Extra Focus |
|---|
| Cohort | Items 6b, 12d (follow-up), 14c (follow-up time), 15 (events over time) |
| Case-control | Items 6a-6b (case/control selection rationale, matching), 12d (matching analysis), 15 (exposure categories) |
| Cross-sectional | Items 6a (selection), 12d (sampling strategy), 15 (summary measures) |
Common STROBE Gaps
| Frequently Missing | Fix |
|---|
| Item 9 (Bias) | Add a dedicated paragraph on bias sources and mitigation strategies |
| Item 10 (Study size) | State sample size justification or explain it was convenience-based |
| Item 12c (Missing data) | Describe complete-case, imputation, or sensitivity approach |
| Item 14b (Missing data counts) | Add missingness counts per variable to Table 1 or supplement |
| Item 16a (Unadjusted + adjusted) | Report both crude and adjusted estimates with 95% CIs |
| Item 19 (Limitations direction) | Discuss direction of bias (toward/away from null), not just list weaknesses |
Output Format
STROBE Compliance Report
Study design: [Cohort / Case-control / Cross-sectional]
Manuscript: [filename]
Summary: X/22 Reported | Y Partial | Z Missing | W N/A
MISSING ITEMS (priority fixes):
[Item #] [Topic] — [What's needed]
PARTIAL ITEMS (improvements needed):
[Item #] [Topic] — [What's present] → [What's missing]
FULLY REPORTED:
[Item #] [Topic] ✓
Extensions
- STROBE-Equity (2024): 10 additional items for reporting health equity data. Use alongside core STROBE when the study addresses health disparities.
- RECORD (REporting of studies Conducted using Observational Routinely-collected Data): Extension for electronic health record / claims database studies.
Related Skills
/manuscript — Overall manuscript writing and anti-pattern scanning/human-write — AI-flavored vocabulary detection
