inline_text() tutorial • gtsummary

Introduction

Reproducible reports are an important part of good practices. We often need to report the results from a table in the text of an R markdown report. Inline reporting has been made simple with inline_text(). The inline_text() function reports statistics from {gtsummary} tables inline in an R markdown report.

Setup

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

# install.packages("gtsummary")
library(gtsummary)

Example data set

We’ll be using the trial data set throughout this example.

This set contains data from 200 patients who received one of two types of chemotherapy (Drug A or Drug B). The outcomes are tumor response and death.

For brevity in the tutorial, let’s keep a subset of the variables from the trial data set.

trial2 <-
  trial %>%
  select(trt, marker, stage)

Inline results from tbl_summary()

First create a basic summary table using tbl_summary() (review tbl_summary() vignette for detailed overview of this function if needed).

tab1 <- tbl_summary(trial2, by = trt)
tab1

Characteristic	Drug A N = 98¹	Drug B N = 102¹
Marker Level (ng/mL)	0.84 (0.23, 1.60)	0.52 (0.18, 1.21)
Unknown	6	4
T Stage
T1	28 (29%)	25 (25%)
T2	25 (26%)	29 (28%)
T3	22 (22%)	21 (21%)
T4	23 (23%)	27 (26%)
¹ Median (Q1, Q3); n (%)

To report the median (IQR) of the marker levels in each group, use the following commands inline.

The median (IQR) marker level in the Drug A and Drug B groups are `r inline_text(tab1, variable = marker, column = "Drug A")` and `r inline_text(tab1, variable = marker, column = "Drug B")`, respectively.

Here’s how the line will appear in your report.

The median (IQR) marker level in the Drug A and Drug B groups are 0.84 (0.23, 1.60) and 0.52 (0.18, 1.21), respectively.

If you display a statistic from a categorical variable, include the level argument.

`r inline_text(tab1, variable = stage, level = "T1", column = "Drug B")` resolves to “25 (25%)”

Inline results from tbl_regression()

Similar syntax is used to report results from tbl_regression() and tbl_uvregression() tables. Refer to the tbl_regression() vignette if you need detailed guidance on using these functions.

Let’s first create a regression model.

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

Now summarize the results with tbl_regression(); exponentiate to get the odds ratios.

tbl_m1 <- tbl_regression(m1, exponentiate = TRUE)
tbl_m1

Characteristic	OR¹	95% CI¹	p-value
Age	1.02	1.00, 1.04	0.091
T Stage
T1	—	—
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

To report the result for age, use the following commands inline.

`r inline_text(tbl_m1, variable = age)`

Here’s how the line will appear in your report.

1.02 (95% CI 1.00, 1.04; p=0.091)

It is reasonable that you’ll need to modify the text. To do this, use the pattern argument. The pattern argument syntax follows glue::glue() format with referenced R objects being inserted between curly brackets. The default is pattern = "{estimate} ({conf.level*100}% CI {conf.low}, {conf.high}; {p.value})". You have access the to following fields within the pattern argument.

Parameter	Description
`{estimate}`	primary estimate (e.g. model coefficient, odds ratio)
`{conf.low}`	lower limit of confidence interval
`{conf.high}`	upper limit of confidence interval
`{p.value}`	p-value
`{conf.level}`	confidence level of interval
`{N}`	number of observations

Age was not significantly associated with tumor response `r inline_text(tbl_m1, variable = age, pattern = "(OR {estimate}; 95% CI {conf.low}, {conf.high}; {p.value})")`.

Age was not significantly associated with tumor response (OR 1.02; 95% CI 1.00, 1.04; p=0.091).

If you’re printing results from a categorical variable, include the level argument, e.g. inline_text(tbl_m1, variable = stage, level = "T3") resolves to “0.94 (95% CI 0.39, 2.28; p=0.9)”.

The inline_text function has arguments for rounding the p-value (pvalue_fun) and the coefficients and confidence interval (estimate_fun). These default to the same rounding performed in the table, but can be modified when reporting inline.

For more details about inline code, review to the RStudio documentation page.