Editors Guidelines
Guidance for editorial leadership in health statistics.
Editorial Responsibilities
Editors ensure scope alignment, methodological rigor, and fair decision making.
Editors evaluate methodological rigor, transparency, and relevance to health statistics practice.
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.
Core Focus Areas
Rigor
Assess statistical validity and transparency in reporting.
Relevance
Confirm alignment with health statistics practice.
Communication
Provide clear guidance to authors and reviewers.
Decision consistency and timely communication improve author experience and review efficiency.
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.
Decision Criteria
Editors evaluate submissions based on statistical validity and clarity.
- Sound study design and analytic methods
- Transparent reporting and reproducibility
- Relevance to health statistics practice
Editors may request statistical review for complex models or methods.
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.
Decision Flow
Initial Check
Confirm scope fit and basic reporting requirements.
Review Assignment
Select reviewers with appropriate expertise.
Decision
Provide clear outcomes and revision guidance.
Follow Up
Ensure revisions address statistical concerns.
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.
Support Editorial Excellence
Help maintain quality in health statistics publishing.
