How to Use easystats in a Medical Research Workflow
A practical guide for medical researchers using easystats for model checks, interpretation, effect sizes, visualization, and result review.
Before using this in research
The goal is not to adopt another tool. The goal is to reduce verified research time without weakening the evidence trail.
Best for
Biomedical, medical, and academic researchers who use R for statistical analysis and need a structured way to check models, interpret parameters, estimate effect sizes, create visual summaries, and review reported results.
First step
Start with a clearly defined research question, outcome, predictors, model type, and analysis plan. Then use easystats as a support layer for model diagnostics, interpretation, visualization, and reporting checks rather than as a substitute for statistical judgment.
A safer workflow
1
Fit the statistical model that matches the study design, outcome type, and assumptions, using the appropriate R modeling function before applying easystats tools.
2Use easystats packages to inspect model performance, assumptions, diagnostics, and potential issues that could affect interpretation.
3Summarize parameters, confidence intervals, p values, effect sizes, and visual outputs in a format that supports transparent reporting for manuscripts or internal review.
4Cross-check tables, figures, and written claims against the model output before submission, noting any limitations, sensitivity analyses, or assumptions that affect the conclusions.
Watch-outs
Do not treat automated summaries as proof that a model is valid; assess study design, measurement quality, missing data, confounding, and model assumptions separately.
Make sure the selected model and effect size are appropriate for the outcome, data structure, and clinical or biomedical question.
Avoid overinterpreting visually attractive outputs without checking uncertainty, sample size, robustness, and consistency with the prespecified analysis plan.
Evidence checks
Verify that model diagnostics and assumption checks are documented and consistent with the statistical method used.
Confirm that reported estimates, confidence intervals, p values, and effect sizes match the final model output.
Check that manuscript conclusions are supported by the analysis results and clearly distinguish primary findings from exploratory or sensitivity analyses.
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