English homeMethodsTableOne for Medical Research: Use Cases, Alternatives, and Evidence-Checking Workflow
Method Guide
TableOne for Medical Research: Use Cases, Alternatives, and Evidence-Checking Workflow
A practical guide to using TableOne in medical research, including suitable use cases, alternatives, and checks for baseline tables.
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 need to summarize cohort characteristics, compare study groups, or review baseline tables for manuscripts, reports, or protocols.
First step
Start by confirming the study design, grouping variable, variable types, missing-data handling rules, and the reporting standards required by your journal, institution, or study protocol.
A safer workflow
1Define the cohort, comparison groups, and variables that belong in the baseline table before running TableOne.
2
Classify variables correctly as continuous, categorical, binary, or non-normal, and specify the summary statistics appropriate for each type.
3Generate the table, then review counts, missing values, labels, group sizes, and statistical tests against the analysis plan.
4Cross-check the final table with source data, code, and manuscript text so that reported baseline characteristics are consistent and reproducible.
Watch-outs
Do not treat automated baseline tables as final evidence; variable coding, missing values, and group definitions still require investigator review.
Avoid overinterpreting baseline p-values, especially in randomized studies or small samples where descriptive balance may be more informative.
Check whether TableOne is the right tool for the dataset and reporting context; alternatives may be needed for complex survey designs, weighted analyses, or highly customized journal formats.
Evidence checks
Verify that all variables in the table can be traced back to documented data fields, definitions, and preprocessing decisions.
Compare generated totals, subgroup counts, and missing-data summaries with independent data checks or analysis logs.
Ensure that table outputs, statistical methods, and manuscript claims align with the protocol, statistical analysis plan, and applicable reporting guidance.
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