Reviewer Register
Join the IJHS reviewer community.
Who Should Apply
Researchers with expertise in biostatistics, epidemiology, or health analytics are encouraged to register.
Reviewers with expertise in biostatistics, epidemiology, or health analytics are encouraged to apply.
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.
Matching Areas
Methods
Causal inference, Bayesian modeling, or longitudinal analysis.
Data Domains
Clinical trials, public health surveillance, or health services.
Tools
R, Python, or specialized statistical software.
Provide subject keywords and methodological strengths to support manuscript matching.
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.
Registration Details
Provide subject keywords, methodological strengths, and recent publications to support manuscript matching.
Approved reviewers receive guidance on timelines and expectations.
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.
Registration Steps
Prepare
List expertise areas and recent publications.
Submit
Email registration details to the editorial office.
Review
Editors match expertise with manuscript needs.
Confirm
Approved reviewers receive guidance and timelines.
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.
