Tools for Creating Publication-Ready Regression Tables

Simplifies regression modeling in R by integrating multiple modeling and summarization tools into a cohesive, user-friendly interface. Designed to be accessible for researchers, particularly those in Low- and Middle-Income Countries (LMIC). Built upon widely accepted statistical methods, including logistic regression (Hosmer et al. 2013, ISBN:9781118548429), log-binomial regression (Spiegelman and Hertzmark 2005 ), Firth penalized logistic regression (Firth 1993 ), Poisson and robust Poisson regression (Zou 2004 ), negative binomial regression (Hilbe 2011, ISBN:9780521179515), Cox proportional hazards regression, parametric survival regression, causal mediation analysis, and linear regression (Kutner et al. 2005, ISBN:9780071122214). Leverages multiple dependencies to ensure high-quality output and generate reproducible, publication-ready tables in alignment with best practices in epidemiology and applied statistics.

gtregression

Publication-ready regression and survival analysis tables, plots, and forest plots for real-world health data. Fit models, compare estimates, visualise results, and export manuscript-ready outputs without hand-formatting every coefficient.

Start the workflowExplore functions

Publication-Ready Regression, Survival, and Mediation Outputs

gtregression helps you move from model to manuscript: fit regression models, produce clean tables, visualise estimates, merge outputs, and export results without hand-formatting every coefficient.

It supports logistic, log-binomial, Poisson, robust Poisson, negative binomial, linear, Cox, parametric survival, and causal mediation workflows, including adjusted and stratified models.

From Data to Manuscript

Why It Exists

Many students, researchers, and public health analysts need regression outputs that are readable, reproducible, and report-ready. gtregression keeps the R syntax approachable while preserving transparent model objects underneath.

Built on Trusted R Packages

gtregression is intentionally a readable interface over established R packages. The package uses widely trusted modelling, tidying, plotting, and reporting tools so users can inspect fitted models and understand the statistical engines behind each output.

The user-facing functions return objects with fitted models, table bodies, and display metadata that advanced users can audit, modify, or reuse.

Install

# CRAN
install.packages("gtregression")

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

Five-Minute Workflow

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"))
  )

exposures <- c("age", "lwt", "race", "smoke", "ht", "ui")

attr(birthwt_data$age, "label") <- "Maternal age"
attr(birthwt_data$lwt, "label") <- "Maternal weight"
attr(birthwt_data$smoke, "label") <- "Smoking during pregnancy"

desc <- descriptive_table(
  birthwt_data,
  exposures = exposures,
  by = "low",
  percent = "column",
  show_overall = "last"
)

uni <- uni_reg(
  birthwt_data,
  outcome = "low",
  exposures = exposures,
  approach = "logit"
)

multi <- multi_reg(
  birthwt_data,
  outcome = "low",
  exposures = c("smoke", "ht", "ui"),
  adjust_for = c("age", "lwt", "race"),
  approach = "logit"
)

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

forest_reg = forest_reg(forest_df(uni, multi)) 

merge_tables(desc, uni, multi)

Variable labels set with attr(x, "label") or labelled::var_label() are used automatically in display tables and plots, while original column names remain available internally for merging, modification, and testing.

Objects stay inspectable:

desc$table
uni$table
multi$table
multi$models

Optional model-fit statistics can be requested without changing the publication table:

uni_stats <- uni_reg(
  data = birthwt_data,
  outcome = "low",
  exposures = exposures,
  approach = "logit",
  model_stats = TRUE
)

uni_stats$model_stats

Browse by Task

Function Map

Citation

If you use gtregression in your work, please cite it as:

Polani R, Eliyas SK, Sakthivel M, Kaviprawin M, Krishnamoorthy Y, Majella MG. gtregression: Tools for Creating Publication-Ready Regression Tables. Zenodo. https://doi.org/10.5281/zenodo.16905350

Acknowledgements

gtregression builds on the R ecosystem, especially stats, survival, MASS, risks, logistf, broom, broom.helpers, sandwich, lmtest, dplyr, purrr, tibble, rlang, flextable, officer, gt, ggplot2, patchwork, forestploter, and scales.