gtsummary Statistical Tables Cheat Sheet
A gtsummary reference for baseline characteristics, group comparisons, regression results, tests, header edits and report output.
What this reference covers
gtsummary helps organize descriptive statistics, group comparisons and model results into consistent tables. This reference connects variable types, analysis populations, missing-value rules, statistical tests, labels, headers, footnotes and report output.
Define the analysis before formatting
Decide whether the table is a baseline summary, a group comparison or a regression result. Write down the included population, grouping variable, time point, variable types, missing-data rule and target statistics before choosing a test or changing a label.
Read P values in context
A P value depends on the test, assumptions, sample size, denominator, missing values and any multiple-comparison rule. Model tables should retain estimates, intervals, units, adjustment variables, reference groups and model sample size.
Maintenance note
A generated table does not select the correct study design or causal interpretation. Compare the final table with the analysis plan and source results, and record the software version and output date. Content review date: 2026-08-23.
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gtsummary study guide
Define the population, variables, missing-data rule and statistical method first, then format labels and output only after the numbers are verified.
Before you start
- Prepare a PDF reader and a documented analysis population.
- Define grouping, continuous and categorical variables, time points and missing markers.
- Keep an analysis plan or statistical definition note for comparison with the generated table.
Quick start
- 01
State the table purpose
Identify a baseline, comparison or regression table and record its population, groups, time point and output measures.
- 02
Check variables and missingness
Verify types, missing markers and denominators before selecting descriptive statistics or comparisons.
- 03
Confirm the statistical method
Match tests to distributions, sample sizes and design, and interpret P values with their assumptions and comparison rules.
- 04
Format and review output
Change labels, headers, notes and precision after the values are checked, then have someone familiar with the analysis review the export.
Usage tips
- Do not infer a sound statistical method from a table's appearance.
- Show sample sizes and missing information along with percentages.
- Keep estimates, intervals, units and reference groups in model result tables.
Troubleshooting and uninstall
Why do table counts not match?
Check filters, missing-value handling, grouping ranges and the denominator used by each statistic, then verify the analysis population row by row.
Why is the regression reference group wrong?
Inspect factor levels and the model design matrix, set the intended baseline explicitly and regenerate the table.
Frequently asked questions
What tables is gtsummary commonly used for?
It is commonly used for baseline characteristics, grouped descriptions, comparisons and regression result tables with customized labels and notes.
Can a P value be interpreted by itself?
No. Review the test, assumptions, sample size, missing values, denominator and multiple-comparison rule together.
Why show missing information?
Missing records can change the population and denominator for each variable, affecting percentages and comparisons.
What is often missing from a regression table?
Common omissions include the reference group, estimate units, confidence interval, model sample size, adjustment variables and missing-data handling.