gtsummary vs tableone for Clinical and Biomedical Research

Compare gtsummary and tableone for creating baseline characteristics tables in clinical, biomedical, and academic research.

Choose the right R package for producing clear, reproducible baseline characteristics and summary tables for manuscripts, reports, and clinical research workflows.

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, and prepare publication-ready tables from clinical or observational data.

First step

Start by defining the table you need: a quick Table 1 for cohort description, a manuscript-ready summary table, or a reproducible reporting workflow that may need customization and export.

A safer workflow

1Use tableone when you need a focused, established way to generate baseline characteristics tables and standardized mean differences for clinical study groups.
2Use gtsummary when you need flexible, publication-oriented summary tables that integrate well with tidyverse workflows and can be customized for manuscripts or reports.
3Check how each package handles your variable types, missing data, group comparisons, labels, and statistical tests before using the output in a paper.
4Keep the table generation code in your analysis script so results can be reproduced when the dataset, cohort definition, or model changes.

Watch-outs

Do not choose a package only because the default table looks acceptable; confirm that the statistics and tests match your study protocol or analysis plan.
Automated Table 1 output still needs review for clinically meaningful grouping, correct denominators, missingness, and variable labeling.
Export formatting may require additional work depending on whether your final target is Word, HTML, PDF, or a journal-specific table format.

Evidence checks

Verify package documentation for supported summary statistics, hypothesis tests, standardized mean differences, and export options.
Compare output on a small subset of your own data to confirm that continuous, categorical, binary, and missing values are handled as expected.
Review the final table against your study protocol, statistical analysis plan, or journal requirements before submission.

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A safer workflow

1Use tableone when you need a focused, established way to generate baseline characteristics tables and standardized mean differences for clinical study groups.

2Use gtsummary when you need flexible, publication-oriented summary tables that integrate well with tidyverse workflows and can be customized for manuscripts or reports.

3Check how each package handles your variable types, missing data, group comparisons, labels, and statistical tests before using the output in a paper.

4Keep the table generation code in your analysis script so results can be reproduced when the dataset, cohort definition, or model changes.

Watch-outs

Do not choose a package only because the default table looks acceptable; confirm that the statistics and tests match your study protocol or analysis plan.

Automated Table 1 output still needs review for clinically meaningful grouping, correct denominators, missingness, and variable labeling.

Export formatting may require additional work depending on whether your final target is Word, HTML, PDF, or a journal-specific table format.

Evidence checks

Verify package documentation for supported summary statistics, hypothesis tests, standardized mean differences, and export options.

Compare output on a small subset of your own data to confirm that continuous, categorical, binary, and missing values are handled as expected.

Review the final table against your study protocol, statistical analysis plan, or journal requirements before submission.