Provide a clear statistical analysis plan, including model assumptions, diagnostics, and missing data handling procedures.

For predictive modeling, report validation strategy, calibration metrics, and clinical utility interpretation.

Describe sampling design, weighting procedures, and population representativeness for survey based studies.

When reporting health equity analyses, define subgroup criteria and specify interaction testing approaches.

Include data availability statements and repository links for reproducibility where possible.

For randomized trials, report allocation concealment, interim analyses, and stopping rules if applicable.

If using machine learning, discuss bias mitigation and explainability approaches for clinical audiences.

Provide code, scripts, or algorithm descriptions so statistical methods can be replicated.

Use reporting guidelines such as CONSORT, STROBE, or PRISMA when relevant.

Include a brief statement on how results inform health decision making or policy.

Document variable definitions, data cleaning decisions, and transformation steps that influence model estimates.

When using Bayesian approaches, describe prior selection and sensitivity to prior assumptions.

For longitudinal analyses, specify follow up intervals, censoring rules, and handling of time varying covariates.

Explain how external validation datasets were selected and describe differences from the primary cohort.

Provide a rationale for subgroup analyses and report multiplicity adjustments when applicable.

For causal inference methods, include clear definitions of treatment, outcome, and confounder sets.

Describe any imputation procedures and include diagnostics for imputed datasets.

When reporting simulation studies, specify parameter settings, number of iterations, and evaluation metrics.

For clustered data, report intra class correlation estimates and variance decomposition where appropriate.

Ensure figures include confidence intervals or uncertainty bands to support interpretation.

When reporting prediction models, describe how cut points or thresholds were chosen for clinical use.

For algorithm comparisons, report computational resources and runtime considerations for reproducibility.

Include a brief limitations section that addresses data constraints and modeling assumptions explicitly.

Document any software packages or libraries used for specialized methods or visualization.

For health economics components, specify cost sources, discount rates, and sensitivity analyses.

When using registry or administrative data, describe coding systems and validation procedures.

Provide a glossary of key statistical terms if the manuscript targets a multidisciplinary readership.

If using adaptive designs, explain decision rules and timing of adaptations clearly.

For meta analyses, describe heterogeneity measures and model selection criteria.

Report transparency on data exclusions and justify any major exclusions from analysis.

Discuss how results might inform guidelines or policy decisions beyond statistical significance.

For data linkage studies, report linkage rates and error checks to support validity.

Include ethics approvals and data sharing permissions in a dedicated section for clarity.

Clear statistical reporting improves the interpretability of health evidence for clinicians, policymakers, and research funders.

We encourage authors to document assumptions and sensitivity analyses so conclusions remain robust across populations.

Transparent reporting of data provenance and governance supports reproducibility and ethical compliance in health statistics.

Well structured manuscripts accelerate peer review and help readers apply statistical insights to real world health decisions.

Describe cohort selection, inclusion criteria, and data exclusions to reduce ambiguity in analytic interpretation.

Provide uncertainty measures such as confidence intervals or credible intervals for key estimates and model outputs.

Explain how missing data were handled and why chosen strategies were appropriate for the study design.

When presenting predictive models, report calibration, discrimination, and decision curve metrics where relevant.

Define statistical terminology clearly for multidisciplinary readers who apply methods in clinical settings.

Summaries that connect statistical findings to health outcomes improve translation to policy and practice.

Report software versions and packages to support reproducibility across analytic environments.

When combining datasets, document linkage procedures and quality checks for matching accuracy.

Highlight ethical safeguards for patient privacy, especially when working with linked or sensitive datasets.

Include brief rationale for study design choices to support reviewer understanding and methodological transparency.

Use tables and figures to communicate effect sizes, uncertainty, and subgroup comparisons clearly.

If external validation is performed, describe population differences and implications for generalizability.

Describe any model tuning or hyperparameter selection to support reproducibility in machine learning workflows.

If data access is restricted, describe the approval process for qualified researchers and expected timelines.

For time series analyses, describe seasonality handling and any interventions or policy changes considered.

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

Include data dictionary summaries or variable definitions for key covariates to improve interpretability.

Manuscripts benefit from concise discussion of clinical relevance and potential implications for health systems.

Provide transparency about funding sources and potential conflicts of interest affecting analytic decisions.

Ensure titles and abstracts reflect the statistical contribution and health domain application accurately.

A clear narrative of methods to results supports readers who are translating findings into practice.

Explain how sampling weights or survey design elements were applied in national or regional datasets.

Sensitivity analyses for key assumptions increase confidence in the robustness of conclusions.

When applicable, provide code or pseudo code to clarify analytic steps for replication.

Include brief discussion of how statistical uncertainty affects decision thresholds or policy interpretation.

For diagnostic accuracy studies, report sensitivity, specificity, and confidence intervals with clear thresholds.

Describe any weighting adjustments or post stratification steps used for population level inference.

If models are updated over time, explain monitoring plans and criteria for recalibration.

Summarize key data limitations and how they might influence interpretation of results.

For health services research, describe care setting context and organizational factors influencing outcomes.

When using hierarchical models, report variance components and interpret them for applied audiences.

Report transformation or normalization steps used for biomarkers or laboratory measures.

If external covariates are used, describe data sources and synchronization with primary datasets.

Include rationale for choosing parametric versus nonparametric methods where applicable.

Describe any bootstrap or resampling approaches used for inference or model stability checks.

For survival models, report proportional hazards checks or alternative modeling choices.

Explain how competing risks were handled in event time analyses and report subdistribution results if used.

For cluster randomized trials, describe cluster selection and intraclass correlation assumptions.

When presenting subgroup findings, include interaction tests and avoid over interpretation of small samples.