Regression Tables

Go from fitted models to publication-ready tables without hand-formatting effect estimates. gtregression supports logistic, log-binomial, Poisson, robust Poisson, negative binomial, and linear regression.

library(gtregression)
library(dplyr)

data("data_birthwt", package = "gtregression")

birthwt_data <- data_birthwt |>
  mutate(
    race = factor(race, levels = c(1, 2, 3),
                  labels = c("White", "Black", "Other")),
    smoke = factor(smoke, levels = c(0, 1), labels = c("No", "Yes")),
    ht = factor(ht, levels = c(0, 1), labels = c("No", "Yes")),
    ui = factor(ui, levels = c(0, 1), labels = c("No", "Yes")),
    low = factor(low, levels = c(0, 1), labels = c("Normal BW", "Low BW")),
    ptl_cat = factor(ifelse(ptl > 0, "Yes", "No"), levels = c("No", "Yes")),
    ftv_cat = factor(case_when(
      ftv == 0 ~ "None",
      ftv == 1 ~ "One",
      ftv >= 2 ~ "Two or more"
    ), levels = c("None", "One", "Two or more"))
  )

birthwt_exposures <- c(
  "age", "lwt", "race", "smoke", "ht", "ui", "ptl_cat", "ftv_cat"
)

Univariable Models

uni_reg() fits one model per exposure and returns a table ready for reports.

birthwt_uni <- uni_reg(
  data = birthwt_data,
  outcome = "low",
  exposures = birthwt_exposures,
  approach = "logit",
  theme = clinical
)

birthwt_uni$table

Characteristic	N	OR (95% CI)	p-value
age	189	0.95 (0.89-1.01)	0.105
ftv_cat	189
None		--
One		0.54 (0.25-1.20)	0.130
Two or more		0.71 (0.32-1.56)	0.394
ht	189
No		--
Yes		3.37 (1.02-11.09)	0.046
lwt	189	0.99 (0.97-1.00)	0.023
ptl_cat	189
No		--
Yes		4.32 (1.92-9.73)	<0.001
race	189
White		--
Black		2.33 (0.94-5.77)	0.068
Other		1.89 (0.96-3.74)	0.067
smoke	189
No		--
Yes		2.02 (1.08-3.78)	0.028
ui	189
No		--
Yes		2.58 (1.14-5.83)	0.023
Abbreviations: OR = Odds Ratio; CI = Confidence Interval.

Adjusted Models

multi_reg() can fit all exposures in one model, or fit one adjusted model per exposure using the same adjustment set.

birthwt_multi <- multi_reg(
  data = birthwt_data,
  outcome = "low",
  exposures = c("smoke", "ht", "ui", "ptl_cat", "ftv_cat"),
  adjust_for = c("age", "lwt", "race"),
  approach = "logit",
  theme = striped
)

birthwt_multi$table

Characteristic	Adjusted OR (95% CI)	p-value
ftv_cat
None	—
One	0.60 (0.26–1.38)	0.230
Two or more	0.86 (0.38–1.96)	0.717
ht
No	—
Yes	5.99 (1.51–23.79)	0.011
ptl_cat
No	—
Yes	4.49 (1.90–10.58)	<0.001
smoke
No	—
Yes	2.87 (1.36–6.04)	0.006
ui
No	—
Yes	2.27 (0.98–5.24)	0.055
Abbreviations: OR = Odds Ratio; CI = Confidence Interval.
Adjusted for age, lwt, and race
N = 189 complete observations included across outcome, exposure, and adjustment variables

The adjustment variables are recorded in the table footnote so the result is ready for manuscript-style reporting.

Other Effect Measures

Switch the approach to change the estimand.

uni_reg(
  data = birthwt_data,
  outcome = "low",
  exposures = c("smoke", "ht", "ui", "ptl_cat"),
  approach = "logbinomial"
)$table

Characteristic	N	RR (95% CI)	p-value
ht	189
No		--
Yes		1.99 (1.17-3.37)	0.011
ptl_cat	189
No		--
Yes		2.33 (1.57-3.45)	<0.001
smoke	189
No		--
Yes		1.61 (1.06-2.44)	0.026
ui	189
No		--
Yes		1.79 (1.15-2.79)	0.011
Abbreviations: RR = Risk Ratio; CI = Confidence Interval.

Continuous Outcomes

Linear regression outputs beta coefficients and keeps diagnostics under $reg_check.

birthwt_linear <- multi_reg(
  data = birthwt_data,
  outcome = "bwt",
  exposures = c("age", "lwt", "race", "smoke", "ht", "ui"),
  approach = "linear"
)

birthwt_linear$table

Characteristic	Adjusted Beta (95% CI)	p-value
age	-4.67 (-22.97–13.63)	0.617
ht
No	—
Yes	-590.03 (-982.59–-197.47)	0.004
lwt	4.40 (1.05–7.74)	0.011
race
White	—
Black	-490.64 (-783.04–-198.23)	0.001
Other	-356.61 (-579.77–-133.46)	0.002
smoke
No	—
Yes	-360.71 (-564.60–-156.82)	<0.001
ui
No	—
Yes	-528.53 (-793.30–-263.77)	<0.001
Abbreviations: Beta = Linear regression coefficient; CI = Confidence Interval.
N = 189 complete observations included in the multivariable model

What To Inspect

$table: publication-ready table.
$table_body: numeric estimates behind the display.
$models: fitted model objects.
$model_summaries: model-level summaries.
$reg_check: diagnostics for linear models.

- Regression Tables