Proposed Special Issue

Launch themed collections in health statistics.

Special issues provide a venue for coordinated work on emerging statistical challenges in health research.

We welcome proposals on topics such as causal inference, data integration, health equity analytics, or pandemic modeling.

Special issues are ideal for coordinated themes such as causal inference in epidemiology, AI for health analytics, or trial methodology.

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.

Include a theme description, list of potential contributors, and a realistic timeline for submissions and review.

Guest editors should outline how they will coordinate peer review and maintain quality standards.

Proposals should outline the theme, potential contributors, and a timeline for solicitation and review.

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.

When reporting health disparities, describe how social determinants and contextual factors are measured.

Guest editors collaborate with the editorial office to ensure rigorous peer review and ethical compliance.

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.

The editorial office supports special issue planning and can be reached at [email protected].

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.