The tbl_regression() function takes a regression model object in R and returns a formatted table of regression model results that is publication-ready. It is a simple way to summarize and present your analysis results using R! Like tbl_summary(), tbl_regression() creates highly customizable analytic tables with sensible defaults.

This vignette will walk a reader through the tbl_regression() function, and the various functions available to modify and make additions to an existing formatted regression table.


Behind the scenes: tbl_regression() uses broom::tidy() to perform the initial model formatting, and can accommodate many different model types (e.g. lm(), glm(), survival::coxph(), survival::survreg() and others are supported models known to work with {gtsummary}). It is also possible to specify your own function to tidy the model results if needed.


Before going through the tutorial, install and load {gtsummary}.

# install.packages("gtsummary")

Example data set

In this vignette we’ll be using the trial data set which is included in the {gtsummary package}.

  • This data set contains information from 200 patients who received one of two types of chemotherapy (Drug A or Drug B).

  • The outcomes are tumor response and death.

  • Each variable in the data frame has been assigned an attribute label (i.e. attr(trial$trt, "label") == "Chemotherapy Treatment") with the labelled package, which we highly recommend using. These labels are displayed in the {gtsummary} output table by default. Using {gtsummary} on a data frame without labels will simply print variable names, or there is an option to add labels later.

Variable Class Label


character Chemotherapy Treatment


numeric Age


numeric Marker Level (ng/mL)


factor T Stage


factor Grade


integer Tumor Response


integer Patient Died


numeric Months to Death/Censor
Includes mix of continuous, dichotomous, and categorical variables

Basic Usage

The default output from tbl_regression() is meant to be publication ready.

  • Let’s start by creating a logistic regression model to predict tumor response using the variables age and grade from the trial data set.
# build logistic regression model
m1 <- glm(response ~ age + stage, trial, family = binomial)

# view raw model results
#>                Estimate Std. Error    z value   Pr(>|z|)
#> (Intercept) -1.48622424 0.62022844 -2.3962530 0.01656365
#> age          0.01939109 0.01146813  1.6908683 0.09086195
#> stageT2     -0.54142643 0.44000267 -1.2305071 0.21850725
#> stageT3     -0.05953479 0.45042027 -0.1321761 0.89484501
#> stageT4     -0.23108633 0.44822835 -0.5155549 0.60616530
  • We will then a regression model table to summarize and present these results in just one line of code from {gtsummary}.
tbl_regression(m1, exponentiate = TRUE)
Characteristic OR1 95% CI1 p-value
Age 1.02 1.00, 1.04 0.091
T Stage
    T2 0.58 0.24, 1.37 0.2
    T3 0.94 0.39, 2.28 0.9
    T4 0.79 0.33, 1.90 0.6
1 OR = Odds Ratio, CI = Confidence Interval

Note the sensible defaults with this basic usage (that can be customized later):

  • The model was recognized as logistic regression with coefficients exponentiated, so the header displayed “OR” for odds ratio.

  • Variable types are automatically detected and reference rows are added for categorical variables.

  • Model estimates and confidence intervals are rounded and formatted.

  • Because the variables in the data set were labelled, the labels were carried through into the {gtsummary} output table. Had the data not been labelled, the default is to display the variable name.

  • Variable levels are indented and footnotes added.

Customize Output

There are four primary ways to customize the output of the regression model table.

  1. Modify tbl_regression() function input arguments
  2. Add additional data/information to a summary table with add_*() functions
  3. Modify summary table appearance with the {gtsummary} functions
  4. Modify table appearance with {gt} package functions

Modifying function arguments

The tbl_regression() function includes many arguments for modifying the appearance.

Argument Description


modify variable labels in table


exponentiate model coefficients


names of variables to include in output. Default is all variables


By default, categorical variables are printed on multiple rows. If a variable is dichotomous and you wish to print the regression coefficient on a single row, include the variable name(s) here.


confidence level of confidence interval


indicates whether to include the intercept


function to round and format coefficient estimates


function to round and format p-values


function to specify/customize tidier function

{gtsummary} functions to add information

The {gtsummary} package has built-in functions for adding to results from tbl_regression(). The following functions add columns and/or information to the regression table.

Function Description
adds the global p-value for a categorical variables
adds statistics from `broom::glance()` as source note
adds column of the variance inflation factors (VIF)
add a column of q values to control for multiple comparisons

{gtsummary} functions to format table

The {gtsummary} package comes with functions specifically made to modify and format summary tables.

Function Description
update column headers
update column footnote
update spanning headers
update table caption/title
bold variable labels
bold variable levels
italicize variable labels
italicize variable levels
bold significant p-values

{gt} functions to format table

The {gt} package is packed with many great functions for modifying table output—too many to list here. Review the package’s website for a full listing.

To use the {gt} package functions with {gtsummary} tables, the regression table must first be converted into a {gt} object. To this end, use the as_gt() function after modifications have been completed with {gtsummary} functions.

m1 %>%
  tbl_regression(exponentiate = TRUE) %>%
  as_gt() %>%
  gt::tab_source_note(gt::md("*This data is simulated*"))
Characteristic OR1 95% CI1 p-value
Age 1.02 1.00, 1.04 0.091
T Stage
    T2 0.58 0.24, 1.37 0.2
    T3 0.94 0.39, 2.28 0.9
    T4 0.79 0.33, 1.90 0.6
This data is simulated
1 OR = Odds Ratio, CI = Confidence Interval


There are formatting options available, such as adding bold and italics to text. In the example below,
- Coefficients are exponentiated to give odds ratios
- Global p-values for Stage are reported - Large p-values are rounded to two decimal places
- P-values less than 0.10 are bold - Variable labels are bold
- Variable levels are italicized

# format results into data frame with global p-values
m1 %>%
    exponentiate = TRUE,
    pvalue_fun = ~ style_pvalue(.x, digits = 2),
  ) %>%
  add_global_p() %>%
  bold_p(t = 0.10) %>%
  bold_labels() %>%
Characteristic OR1 95% CI1 p-value
Age 1.02 1.00, 1.04 0.087
T Stage 0.62
    T2 0.58 0.24, 1.37
    T3 0.94 0.39, 2.28
    T4 0.79 0.33, 1.90
1 OR = Odds Ratio, CI = Confidence Interval

Univariate Regression

The tbl_uvregression() function produces a table of univariate regression models. The function is a wrapper for tbl_regression(), and as a result, accepts nearly identical function arguments. The function’s results can be modified in similar ways to tbl_regression().

trial %>%
  select(response, age, grade) %>%
    method = glm,
    y = response,
    method.args = list(family = binomial),
    exponentiate = TRUE,
    pvalue_fun = ~ style_pvalue(.x, digits = 2)
  ) %>%
  add_global_p() %>% # add global p-value
  add_nevent() %>% # add number of events of the outcome
  add_q() %>% # adjusts global p-values for multiple testing
  bold_p() %>% # bold p-values under a given threshold (default 0.05)
  bold_p(t = 0.10, q = TRUE) %>% # now bold q-values under the threshold of 0.10
#> add_q: Adjusting p-values with
#> `stats::p.adjust(x$table_body$p.value, method = "fdr")`
Characteristic N Event N OR1 95% CI1 p-value q-value2
Age 183 58 1.02 1.00, 1.04 0.091 0.18
Grade 193 61 0.93 0.93
    II 0.95 0.45, 2.00
    III 1.10 0.52, 2.29
1 OR = Odds Ratio, CI = Confidence Interval
2 False discovery rate correction for multiple testing

Setting Default Options

The {gtsummary} regression functions and their related functions have sensible defaults for rounding and formatting results. If you, however, would like to change the defaults there are a few options. The default options can be changed using the {gtsummary} themes function set_gtsummary_theme(). The package includes pre-specified themes, and you can also create your own. Themes can control baseline behavior, for example, how p-values are rounded, coefficients are rounded, default headers, confidence levels, etc. For details on creating a theme and setting personal defaults, visit the themes vignette.

Supported Models

Below is a listing of known and tested models supported by tbl_regression(). If a model follows a standard format and has a tidier, it’s likely to be supported as well, even if not listed below.

Model Details




broom.mixed package required

Limited support. It is recommended to use tidycmprsk::crr() instead.


May fail with R <= 4.0.


May fail with R <= 4.0.


May fail with R <= 4.0.


May fail with R <= 4.0.



broom.mixed package required


Limited support for categorical variables


broom.mixed package required

broom.mixed package required

broom.mixed package required


Requires logitr >= 0.8.0

Use default tidier broom::tidy() for smooth terms only, or gtsummary::tidy_gam() to include parametric terms

Limited support. If mod is a mira object, use tidy_plus_plus(mod, tidy_fun = function(x, ...) mice::pool(x) %>% mice::tidy(...))


Experimental support. Use tidy_multgee() as tidy_fun.


Experimental support. Use tidy_multgee() as tidy_fun.

Limited support for models with nominal predictors.

Limited support for models with nominal predictors.

Supported as long as the type of model and the engine is supported.



broom.mixed package required

Reference rows are not relevant for such models.

Limited support


Limited support. It is recommended to use tidy_parameters() as tidy_fun.