Tutorial: tbl_regression

Introduction

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 more are vetted tidy models that are known to work with our package). It is also possible to specify your own function to tidy the model results if needed.

To start, a quick note on the {magrittr} package’s pipe function, %>%. By default the pipe operator puts whatever is on the left hand side of %>% into the first argument of the function on the right hand side. The pipe function can be used to make the code relating to tbl_regression() easier to use, but it is not required. Here are a few examples of how %>% translates into typical R notation.

x %>% f() is equivalent to f(x)
x %>% f(y) is equivalent to f(x, y)
y %>% f(x, .) is equivalent to f(x, y)
z %>% f(x, y, arg = .) is equivalent to f(x, y, arg = z)

Setup

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

install.packages("gtsummary")
remotes::install_github("rstudio/gt", ref = gtsummary::gt_sha)

library(gtsummary)
library(dplyr)

• If you experience issues installing {gt} on Windows, install Rtools from CRAN, restart R, and attempt installation again.

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.

    trt      Chemotherapy Treatment
    age      Age, yrs
    marker   Marker Level, ng/mL
    stage    T Stage
    grade    Grade
    response Tumor Response
    death    Patient Died
    ttdeath  Years from Treatment to Death/Censor

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, stage, and grade from the trial data set.

# build logistic regression model
m1 = glm(response ~ age + stage + grade, trial, family = binomial(link = "logit"))

# view raw model results
summary(m1)$coefficients
#>                Estimate Std. Error    z value   Pr(>|z|)
#> (Intercept) -1.42184501 0.65711995 -2.1637526 0.03048334
#> age          0.01935700 0.01149333  1.6841945 0.09214409
#> stageT2     -0.56765609 0.44328677 -1.2805618 0.20034764
#> stageT3     -0.09619949 0.45702787 -0.2104893 0.83328578
#> stageT4     -0.26797315 0.45364355 -0.5907130 0.55471272
#> gradeII     -0.17315419 0.40255106 -0.4301422 0.66709221
#> gradeIII     0.04434059 0.38892269  0.1140087 0.90923087

• We will then a regression model table to summarize and present these results in just one line of code from {gtsummary}.

# format results
tbl_regression(m1, exponentiate = TRUE)

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 created for categorical variables.

• Model estimates and confidence intervals are rounded and nicely formatted.

• P-values above 0.9 are presented as “>0.9” and below 0.001 are presented as “<0.001”. Non-significant p-values are only rounded to one decimal, while those close to or below the significance threshold (default 0.05) have additional decimal places by default.

• 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 are added if printed using {gt}. (can alternatively be printed using knitr::kable(); see options here)

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

label            modify the variable labels printed in the table.  
exponentiate     exponentiate model coefficients.  
include          names of variables to include in output. Default is all variables.  
show_single_row  By default, categorical variables are printed on multiple rows.  
                 If a variable is dichotomous (e.g. Yes/No) and you wish to print 
                 the regression coefficient on a single row, include the variable name(s) here. 
conf.level       confidence level of confidence interval.  
intercept        logical argument indicates whether to include the intercept in output.  
estimate_fun     function to round and format coefficient estimates.  
pvalue_fun       function to round and format p-values.  
tidy_fun         function to specify/customize tidier function

add_global_p()  adds the global p-value for a categorical variables   
add_nevent()    adds the number of observed events to the results object    

bold_labels()       bold variable labels  
bold_levels()       bold variable levels  
italicize_labels()  italicize variable labels  
italicize_levels()  italicize variable levels  
bold_p()            bold significant p-values  

m1 %>%
  tbl_regression(exponentiate = TRUE) %>%
  as_gt() %>%
  <gt functions>

# format results into data frame with global p-values
m1 %>%
  tbl_regression(
    exponentiate = TRUE, 
    pvalue_fun = function(x) style_pvalue(x, digits = 2),
    estimate_fun = function(x) style_ratio(x, digits = 3)
  ) %>% 
  add_global_p() %>%
  bold_p(t = 0.10) %>%
  bold_labels() %>% 
  italicize_levels()

Advanced Customization

When you print the output from the tbl_regression() function into the R console or into an R markdown, there are default printing functions that are called in the background: print.tbl_regression() and knit_print.tbl_regression(). The true output from tbl_regression() is a named list, but when you print the object, a formatted version of .$table_body is displayed. All formatting and modifications are made using the {gt} package by default.

tbl_regression(m1) %>% names()
#> [1] "table_body"   "table_header" "n"            "model_obj"    "inputs"      
#> [6] "call_list"    "gt_calls"     "kable_calls"  "fmt_fun"

These are the additional data stored in the tbl_regression() output list.

table_body   data frame with summary statistics  
n            N included in model  
model_obj    the model object passed to `tbl_regression`  
call_list    named list of each function called on the `tbl_regression` object  
inputs       inputs from the `tbl_regression()` function call  
gt_calls     named list of {gt} function calls  
kable_calls  named list of function calls to be applied before knitr::kable()  

gt_calls is a named list of saved {gt} function calls. The {gt} calls are run when the object is printed to the console or in an R markdown document. Here’s an example of the first few calls saved with tbl_regression():

tbl_regression(m1) %>% purrr::pluck("gt_calls") %>% head(n = 5)
#> $gt
#> gt::gt(data = x$table_body)
#> 
#> $cols_align
#> gt::cols_align(align = 'center') %>% gt::cols_align(align = 'left', columns = gt::vars(label))
#> 
#> $fmt_missing
#> gt::fmt_missing(columns = gt::everything(), missing_text = '')
#> 
#> $fmt_missing_ref
#> gt::fmt_missing(columns = gt::vars(estimate, ci), rows = row_ref == TRUE, missing_text = '---')
#> 
#> $tab_style_text_indent
#> gt::tab_style(style = gt::cell_text(indent = gt::px(10), align = 'left'),locations = gt::cells_body(columns = gt::vars(label), rows = row_type != 'label'))

The {gt} functions are called in the order they appear, always beginning with the gt() function.

If the user does not want a specific {gt} function to run, any {gt} call can be excluded in the as_gt() function. For example, the tbl_regression() call creates many named {gt} function calls: gt, cols_align, fmt_missing, fmt_missing_ref, tab_style_text_indent, cols_label, cols_hide, fmt, tab_footnote. Any one of these can be excluded. In this example, the default footnote will be excluded from the output.

tbl_regression(m1, exponentiate = TRUE) %>%
  as_gt(include = -tab_footnote)

Univariate Regression

The tbl_uvregression() produces a table of univariate regression results. 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() and the results reported inline similarly to tbl_regression().

trial %>%
  select(-death, -ttdeath, -stage) %>%
  tbl_uvregression(
    method = glm,
    y = response,
    method.args = list(family = binomial),
    exponentiate = TRUE,
    pvalue_fun = function(x) style_pvalue(x, digits = 2)
  ) %>%
  # overrides the default that shows p-values for each level
  add_global_p() %>%
  # adjusts global p-values for multiple testing (default method: FDR)
  add_q() %>%
  # bold p-values under a given threshold (default 0.05)
  bold_p() %>%
  # now bold q-values under the threshold of 0.10
  bold_p(t = 0.10, q = TRUE) %>%
  bold_labels()

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 in a single script with addition an options() command in the script. The defaults can also be set on the project- or user-level R profile, .Rprofile.

help("Rprofile")

usethis::edit_r_profile()

The following parameters are available to be set:

When setting default rounding/formatting functions, set the default to a function object rather than an evaluated function. For example, if you want to round estimates to 3 significant figures use,

options(gtsummary.tbl_regression.estimate_fun = function(x) sigfig(x, digits = 3))