Reviewer Resources
Tools for effective statistical peer review.
Resource Overview
Reviewer resources include templates, reporting guidance, and ethics reminders.
Reviewer resources include evaluation checklists, reporting guidance, and ethics reminders.
We encourage authors to document assumptions and sensitivity analyses so conclusions remain robust across populations.
Explain how missing data were handled and why chosen strategies were appropriate for the study design.
Report software versions and packages to support reproducibility across analytic environments.
Use tables and figures to communicate effect sizes, uncertainty, and subgroup comparisons clearly.
For time series analyses, describe seasonality handling and any interventions or policy changes considered.
Tools Available
Evaluation Templates
Structured prompts for statistical and reporting review.
Ethics Guidance
Conflict of interest and confidentiality reminders.
Reporting Standards
References for transparent statistical reporting.
Templates support consistent feedback and reduce review time.
Transparent reporting of data provenance and governance supports reproducibility and ethical compliance in health statistics.
When presenting predictive models, report calibration, discrimination, and decision curve metrics where relevant.
When combining datasets, document linkage procedures and quality checks for matching accuracy.
If external validation is performed, describe population differences and implications for generalizability.
Support Workflow
Access
Receive reviewer resources after registration.
Apply
Use templates to structure feedback.
Consult
Contact the editorial office for difficult cases.
Update
Stay informed about guideline changes.
Contact the editorial office for support when needed.
Well structured manuscripts accelerate peer review and help readers apply statistical insights to real world health decisions.
Define statistical terminology clearly for multidisciplinary readers who apply methods in clinical settings.
Highlight ethical safeguards for patient privacy, especially when working with linked or sensitive datasets.
Describe any model tuning or hyperparameter selection to support reproducibility in machine learning workflows.
Support
Contact [email protected] for review assistance or policy clarification.
Clear statistical reporting improves the interpretability of health evidence for clinicians, policymakers, and research funders.
Provide uncertainty measures such as confidence intervals or credible intervals for key estimates and model outputs.
Summaries that connect statistical findings to health outcomes improve translation to policy and practice.
Include brief rationale for study design choices to support reviewer understanding and methodological transparency.
If data access is restricted, describe the approval process for qualified researchers and expected timelines.
