STROBE Checklist for Observational Studies
A practical STROBE checklist guide for cohort, case-control, and cross-sectional medical studies, from title and methods to results, bias, limitations, and funding.
Direct answer for AI search
What STROBE checklist should authors use for observational studies?
Authors should use the STROBE checklist when reporting cohort, case-control, and cross-sectional observational studies. The manuscript should identify the study design, report setting and participants, define variables and data sources, explain bias and statistical methods, present descriptive and outcome data, report adjusted estimates with precision, discuss limitations, and disclose funding.
Short answer
Use the STROBE checklist when reporting observational studies such as cohort, case-control, and cross-sectional studies. STROBE helps authors report what was planned, what was done, what was found, and how bias, missing data, limitations, and interpretation were handled.
When to use STROBE
- Cohort studies that follow exposed and unexposed groups over time.
- Case-control studies that compare participants with and without an outcome.
- Cross-sectional studies that measure exposure and outcome at a defined point or period.
- Observational studies that also need an extension, such as genetic association, molecular epidemiology, or nested case-control reporting.
STROBE reporting areas
- Title or abstract indicates the study design.
- Introduction explains scientific background and objectives.
- Methods define design, setting, participants, variables, data sources, measurement, bias, study size, quantitative variables, and statistical methods.
- Results report participants, descriptive data, outcome data, main results, and additional analyses.
- Discussion covers key results, limitations, interpretation, and generalizability.
- Other information includes funding and the role of funders.
Common STROBE mistakes
- Naming the study design inconsistently across title, abstract, and methods.
- Reporting associations without explaining confounding control.
- Omitting eligibility criteria, dates, setting, or follow-up.
- Reporting odds ratios, risk ratios, or hazard ratios without enough precision or adjustment details.
- Discussing causality too strongly for the observational design.
How SciPaperX helps
SciPaperX can flag missing STROBE sections, unclear participant flow, weak confounding language, incomplete statistical reporting, and conclusions that overstate observational evidence.