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Start Here: Model to Manuscript

gtregression

Publication-ready regression tables and plots for real-world health data.

gtregression helps you fit, adjust, stratify, visualise, and export regression results with approachable R syntax. It supports logistic, log-binomial, Poisson, robust Poisson, negative binomial, Cox survival, parametric survival, and linear regression. flextable is the default table engine, so outputs are Word-friendly from the start; format = gt remains available for HTML-first workflows.

What You Can Make

Clean descriptive tables.
Univariable and multivariable regression tables.
Adjusted models with clear footnotes.
Stratified regression outputs.
Kaplan-Meier curves, survival summaries, Cox models, and parametric survival models.
Forest plots and publication-style forest tables.
Model diagnostics, model selection, confounding, and interaction checks.
HTML, PDF, PNG, and Word outputs.

What Powers the Package

gtregression is a readable interface over standard R modelling and reporting packages. The fitted models remain available inside the returned objects, so users can inspect the analysis behind the displayed table.

Area	Core packages used
Data handling	`dplyr`, `purrr`, `tibble`, `rlang`
Regression and survival models	`stats`, `MASS`, `survival`, `risks`, `logistf`
Robust inference and model tidying	`sandwich`, `lmtest`, `broom`, `broom.helpers`
Tables and Word output	`flextable`, `officer`, `gt`
Plots and forest plots	`ggplot2`, `patchwork`, `forestploter`, `scales`

Install

install.packages("gtregression")

# Development version
devtools::install_github("ThinkDenominator/gtregression")

Prepare Example Data

The articles use data_birthwt, a small built-in dataset that is easy to learn with.

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 = ifelse(ptl > 0, "Yes", "No"),
    ftv_cat = case_when(
      ftv == 0 ~ "None",
      ftv == 1 ~ "One",
      ftv >= 2 ~ "Two or more"
    )
  ) |>
  mutate(
    ptl_cat = factor(ptl_cat, levels = c("No", "Yes")),
    ftv_cat = factor(ftv_cat, levels = c("None", "One", "Two or more"))
  )

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

attr(birthwt_data$age, "label") <- "Maternal age"
attr(birthwt_data$lwt, "label") <- "Maternal weight"
attr(birthwt_data$race, "label") <- "Maternal race"
attr(birthwt_data$smoke, "label") <- "Smoking during pregnancy"
attr(birthwt_data$ht, "label") <- "Hypertension"
attr(birthwt_data$ui, "label") <- "Uterine irritability"
attr(birthwt_data$ptl_cat, "label") <- "Previous preterm labour"
attr(birthwt_data$ftv_cat, "label") <- "First trimester visits"

Five-Minute Workflow

Describe

birthwt_summary <- descriptive_table(
  data = birthwt_data,
  exposures = birthwt_exposures,
  by = low,
  percent = column,
  show_overall = last,
  theme = clinical
)

birthwt_summary$table

Characteristic	Normal BW, N=130	Low BW, N=59	Overall, N=189
Maternal age	23.0 (19.0-28.0)	22.0 (19.5-25.0)	23.0 (19.0-26.0)
Maternal weight	123.5 (113.0-147.0)	120.0 (104.0-130.0)	121.0 (110.0-140.0)
Maternal race
White	73 (56.2%)	23 (39.0%)	96 (50.8%)
Black	15 (11.5%)	11 (18.6%)	26 (13.8%)
Other	42 (32.3%)	25 (42.4%)	67 (35.4%)
Smoking during pregnancy
No	86 (66.2%)	29 (49.2%)	115 (60.8%)
Yes	44 (33.8%)	30 (50.8%)	74 (39.2%)
Hypertension
No	125 (96.2%)	52 (88.1%)	177 (93.7%)
Yes	5 (3.8%)	7 (11.9%)	12 (6.3%)
Uterine irritability
No	116 (89.2%)	45 (76.3%)	161 (85.2%)
Yes	14 (10.8%)	14 (23.7%)	28 (14.8%)
Previous preterm labour
No	118 (90.8%)	41 (69.5%)	159 (84.1%)
Yes	12 (9.2%)	18 (30.5%)	30 (15.9%)
First trimester visits
None	64 (49.2%)	36 (61.0%)	100 (52.9%)
One	36 (27.7%)	11 (18.6%)	47 (24.9%)
Two or more	30 (23.1%)	12 (20.3%)	42 (22.2%)
Categorical variables shown as n (%); percentages are by column.
Continuous variables shown as Median (IQR).

Model

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

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
Smoking during pregnancy
No	Ref.
Yes	2.87 (1.36–6.04)	0.006
Hypertension
No	Ref.
Yes	5.99 (1.51–23.79)	0.011
Uterine irritability
No	Ref.
Yes	2.27 (0.98–5.24)	0.055
Previous preterm labour
No	Ref.
Yes	4.49 (1.90–10.58)	<0.001
First trimester visits
None	Ref.
One	0.60 (0.26–1.38)	0.230
Two or more	0.86 (0.38–1.96)	0.717
Abbreviations: OR = Odds Ratio; CI = Confidence Interval.
Ref. = reference category.
Adjusted for Maternal age, Maternal weight, and Maternal race
N = 189 complete observations included in each adjusted model.

Visualise

plot_reg(
  birthwt_multi,
  title = "Adjusted Regression for Low Birth Weight"
)

forest_reg(forest_df(birthwt_uni, birthwt_multi))

Merge and Polish

birthwt_final <- merge_tables(
  birthwt_summary,
  birthwt_uni,
  birthwt_multi,
  spanners = c("Clinical profile", "Crude OR", "Adjusted OR")
)

birthwt_final <- modify_table(
  birthwt_final,
  caption = "Clinical profile and regression estimates for low birth weight",
  caveat = "Adjusted estimates are adjusted for maternal age, maternal weight, and maternal race."
)

birthwt_final$table

Clinical profile and regression estimates for low birth weight
	Clinical profile			Crude OR			Adjusted OR
Characteristic	Normal BW	Low BW	Overall	N	OR (95% CI)	p-value	Adjusted OR (95% CI)	p-value
Maternal age	23.0 (19.0-28.0)	22.0 (19.5-25.0)	23.0 (19.0-26.0)	189	0.95 (0.89-1.01)	0.105
Maternal weight	123.5 (113.0-147.0)	120.0 (104.0-130.0)	121.0 (110.0-140.0)	189	0.99 (0.97-1.00)	0.023
Maternal race				189
White	73 (56.2%)	23 (39.0%)	96 (50.8%)		Ref.
Black	15 (11.5%)	11 (18.6%)	26 (13.8%)		2.33 (0.94-5.77)	0.068
Other	42 (32.3%)	25 (42.4%)	67 (35.4%)		1.89 (0.96-3.74)	0.067
Smoking during pregnancy				189
No	86 (66.2%)	29 (49.2%)	115 (60.8%)		Ref.		Ref.
Yes	44 (33.8%)	30 (50.8%)	74 (39.2%)		2.02 (1.08-3.78)	0.028	2.87 (1.36–6.04)	0.006
Hypertension				189
No	125 (96.2%)	52 (88.1%)	177 (93.7%)		Ref.		Ref.
Yes	5 (3.8%)	7 (11.9%)	12 (6.3%)		3.37 (1.02-11.09)	0.046	5.99 (1.51–23.79)	0.011
Uterine irritability				189
No	116 (89.2%)	45 (76.3%)	161 (85.2%)		Ref.		Ref.
Yes	14 (10.8%)	14 (23.7%)	28 (14.8%)		2.58 (1.14-5.83)	0.023	2.27 (0.98–5.24)	0.055
Previous preterm labour				189
No	118 (90.8%)	41 (69.5%)	159 (84.1%)		Ref.		Ref.
Yes	12 (9.2%)	18 (30.5%)	30 (15.9%)		4.32 (1.92-9.73)	<0.001	4.49 (1.90–10.58)	<0.001
First trimester visits				189
None	64 (49.2%)	36 (61.0%)	100 (52.9%)		Ref.		Ref.
One	36 (27.7%)	11 (18.6%)	47 (24.9%)		0.54 (0.25-1.20)	0.130	0.60 (0.26–1.38)	0.230
Two or more	30 (23.1%)	12 (20.3%)	42 (22.2%)		0.71 (0.32-1.56)	0.394	0.86 (0.38–1.96)	0.717
Categorical variables shown as n (%); percentages are by column.
Continuous variables shown as Median (IQR).
Abbreviations: OR = Odds Ratio; CI = Confidence Interval.
Ref. = reference category.
Adjusted for Maternal age, Maternal weight, and Maternal race
N = 189 complete observations included in each adjusted model.
Adjusted estimates are adjusted for maternal age, maternal weight, and maternal race.

Save helpers return file paths and use tempdir() when no directory is supplied, which keeps examples CRAN-safe.

save_table(birthwt_final, filename = "birthwt-table", format = html)
save_docx(tables = birthwt_final, filename = "birthwt-report")

Where To Go Next

Descriptive Tables: build baseline tables users can read.
Regression Tables: create crude and adjusted publication-ready outputs.
Survival Analysis: Kaplan-Meier curves, survival summaries, Cox regression, parametric survival models, and survival predictions.
Causal Mediation: estimate direct, indirect, total, and proportion mediated effects with clear causal caveats.
Visualise Results: plot regression estimates and forest tables.
Stratified Analysis: repeat models across subgroups.
Diagnostics: check convergence, collinearity, and model selection.
Confounding & Interaction: support interpretation and model decisions.
Customize & Export: polish and save tables, plots, and reports.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.