Type:	Package
Title:	Tools for Creating Publication-Ready Regression Tables
Version:	1.0.0
Description:	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 <doi:10.1093/aje/kwi188>), Poisson and robust Poisson regression (Zou 2004 <doi:10.1093/aje/kwh090>), negative binomial regression (Hilbe 2011, ISBN:9780521179515), 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.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 4.1.0)
Imports:	dplyr, gtsummary, risks, purrr, MASS, rlang, stats, lmtest, patchwork, ggtext, ggplot2, tidyr, utils, sandwich, tibble, broom, broom.helpers, gt, officer, flextable
VignetteBuilder:	knitr
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, mlbench, car, forcats, pkgdown
RoxygenNote:	7.3.2
Config/testthat/edition:	3
URL:	https://thinkdenominator.github.io/gtregression/
BugReports:	https://github.com/ThinkDenominator/gtregression/issues
NeedsCompilation:	no
Packaged:	2025-07-30 18:38:55 UTC; salineliyas
Author:	Rubeshkumar Polani [aut, cre], Salin K Eliyas [aut], Manikandanesan Sakthivel [aut], Yuvaraj Krishnamoorthy [aut], Marie Gilbert Majella [aut]
Maintainer:	Rubeshkumar Polani <rubesh@thinkdenominator.com>
Repository:	CRAN
Date/Publication:	2025-08-18 14:50:13 UTC

Fit multivariate Regression Model (Internal)

Description

Fits a regression model based on the selected approach.

Usage

.fit_multi_model(data, outcome, exposures, approach)

Arguments

Value

A fitted model object ('glm', 'lm', 'riskratio', or 'negbin') or 'NULL' if fitting fails.

Fit Regression Model with One or More Predictors (Internal)

Description

Fits a regression model based on the selected approach. Can handle a single exposure or a vector of exposures.

Usage

.fit_uni_model(data, outcome, exposures, approach)

Arguments

Value

A fitted model object ('glm', 'lm', 'riskratio', or 'negbin') or 'NULL' if fitting fails.

Get Abbreviation Explanation

Description

Returns a plain-language abbreviation string for the regression approach.

Usage

.get_abbreviation(approach)

Arguments

Value

A character string explaining the abbreviation (e.g., '"OR = Odds Ratio"').

Get Unadjusted Effect Label

Description

Returns the appropriate label (e.g., OR, IRR, RR, Beta) for unadjusted regression estimates.

Usage

.get_effect_label(approach)

Arguments

Value

A character string for the effect label, formatted with markdown (e.g., '"**OR**"').

Get Adjusted Effect Label

Description

Returns a markdown-formatted label for adjusted estimates (e.g., Adjusted OR).

Usage

.get_effect_label_adjusted(approach)

Arguments

Value

A character string label (e.g., '"**Adjusted IRR**"').

Get Abbreviation to Remove

Description

Identifies which abbreviation string should be removed from the summary table, if applicable for the given approach.

Usage

.get_remove_abbreviation(approach)

Arguments

Value

A character string indicating the abbreviation to remove, or '""' if none.

Linear Regression Diagnostic Checks (Internal) similar to reg check in stata Performs diagnostic tests for linear regression models: - Breusch-Pagan test for heteroskedasticity - Shapiro-Wilk test for normality of residuals - RESET test for model specification - Cook's Distance for influential points

Description

Linear Regression Diagnostic Checks (Internal) similar to reg check in stata Performs diagnostic tests for linear regression models: - Breusch-Pagan test for heteroskedasticity - Shapiro-Wilk test for normality of residuals - RESET test for model specification - Cook's Distance for influential points

Usage

.reg_check_linear(model, exposure)

Arguments

Value

A data frame with one row per diagnostic test, including:

Exposure

Name of the exposure variable.

Test

Diagnostic test name.

Statistic

Test statistic or summary (e.g., p-values).

Interpretation

Plain-language result interpretation.

Validate Exposure Variable(s) for Regression

Description

Ensures that the exposure variable has at least two non-missing levels or sufficient numeric variation to support regression modelling.

Usage

.validate_exposures(data, exposures)

Arguments

Value

Returns TRUE if valid; otherwise throws an error.

Check Collinearity Using VIF for Fitted Models

Description

Computes Variance Inflation Factors (VIF) for fitted models returned by uni_reg(), multi_reg(), uni_reg_nbin(), or multi_reg_nbin(). Returns one VIF table per model. For multivariate models only

Usage

check_collinearity(model)

Arguments

Value

A tibble containing VIF values and interpretation. For multivariable models, returns one tibble. For univariate models, an error is raised indicating VIF is not applicable.

Examples

if (requireNamespace("gtregression", quietly = TRUE) &&
  requireNamespace("mlbench", quietly = TRUE) &&
  getRversion() >= "4.1.0") {
  data(PimaIndiansDiabetes2, package = "mlbench")
  pima <- PimaIndiansDiabetes2 |> dplyr::filter(!is.na(diabetes))
  pima$diabetes <- ifelse(pima$diabetes == "pos", 1, 0)
  fit <- multi_reg(pima,
    outcome = "diabetes",
    exposures = c("age", "mass", "glucose"),
    approach = "logit"
  )
  check_collinearity(fit)
}

Check Convergence for a Regression Model

Description

Assesses model convergence and provides diagnostics for each exposure (in univariate mode) or for the full model (in multivariable mode), depending on the regression approach used.

Usage

check_convergence(
  data,
  exposures,
  outcome,
  approach = "logit",
  multivariate = FALSE
)

Arguments

Details

For robpoisson, predicted probabilities (fitted values) may exceed 1, which is acceptable when estimating risk ratios but should not be interpreted as actual probabilities.

This function is useful for identifying convergence issues, especially for "log-binomial" models, which often fail to converge .

Value

A data frame summarizing convergence diagnostics, including:

Exposure

Name of the exposure variable.

Model

The regression approach used.

Converged

TRUE if the model converged successfully; FALSE otherwise.

Max.prob

Maximum predicted probability or fitted value in the dataset.

See Also

[identify_confounder()], [interaction_models()]

Examples

if (requireNamespace("gtregression", quietly = TRUE)) {
  data(data_PimaIndiansDiabetes, package = "gtregression")

  check_convergence(
    data = data_PimaIndiansDiabetes,
    exposures = c("age", "bmi"),
    outcome = "diabetes",
    approach = "logit"
  )

  check_convergence(
    data = data_PimaIndiansDiabetes,
    exposures = c("age", "bmi"),
    outcome = "diabetes",
    approach = "logit",
    multivariate = TRUE
  )
}

PimaIndians2 Diabetes Dataset

Description

A cleaned version of the original Pima Indians Diabetes dataset from the 'mlbench' package. Useful for demonstrating regression approaches for binary outcomes.

Usage

data_PimaIndiansDiabetes

Format

A data frame with 768 observations and 9 variables:

pregnant

Number of times pregnant

glucose

Plasma glucose concentration (glucose tolerance test)

pressure

Diastolic blood pressure (mm Hg)

triceps

Triceps skin fold thickness (mm)

insulin

2-Hour serum insulin (mu U/ml)

mass

Body mass index (BMI)

pedigree

Diabetes pedigree function

age

Age in years

diabetes

Factor indicating diabetes status (pos/neg)

Source

https://www.openml.org/d/37

Birth Weight Data

Description

A dataset from the MASS package containing risk factors associated with low birth weight (LBW) in newborns. Originally collected at Baystate Medical Center, Springfield, Massachusetts, USA.

Usage

data_birthwt

Format

A data frame with 189 observations and 10 variables:

low

Indicator for birth weight < 2500g (binary): 0 = normal, 1 = low birth weight

age

Mother's age in years (numeric)

lwt

Mother's weight in pounds at last menstrual period (numeric)

race

Mother's race (factor): 1 = White, 2 = Black, 3 = Other

smoke

Smoking status during pregnancy (binary): 0 = No, 1 = Yes

ptl

Number of previous premature labors (integer)

ht

History of hypertension (binary): 0 = No, 1 = Yes

ui

Presence of uterine irritability (binary): 0 = No, 1 = Yes

ftv

no of physician visits during the 1st trimester (integer, 0–6)

bwt

Birth weight in grams (numeric)

Details

The outcome variable is binary ('low'): birth weight < 2500g (yes = 1) or not (no = 0).

Source

Hosmer, D.W., Lemeshow, S. (1989). *Applied Logistic Regression.* New York: Wiley. Also available in MASS and described in detail in its documentation.

Epilepsy Treatment and Seizure Counts

Description

RCT on the effect of a drug on the seizures in patients with epilepsy. Contains repeated measures data with treatment groups, baseline seizure counts, and follow-up counts.

Usage

data_epilepsy

Format

A data frame with 236 observations and 9 variables:

y

Number of seizures in a 2-week period (count)

trt

Treatment group (factor): placebo or progabide

base

Seizure count during baseline period (numeric)

age

Age of patient (numeric)

V4

Indicator for 4th visit (binary)

subject

Patient ID (factor)

period

Follow-up period number (integer)

lbase

Log of baseline seizures (numeric)

lage

Log of age (numeric)

Source

MASS package. Original data from Thall and Vail (1990)

Student Absenteeism in Rural Schools

Description

This dataset contains observations on the number of days absent from school for children in rural Australia, along with student characteristics. It's commonly used to demonstrate count models such as Poisson and Negative Binomial regression.

Usage

data_gt_quin

Format

A data frame with 146 observations and 5 variables:

Eth

Ethnicity ("A" = Aboriginal, "N" = Non-Aboriginal)

Sex

Sex ("F" or "M")

Age

Age group ("F0", "F1", "F2", "F3")

Lrn

Learner status ("AL" = average learner, "SL" = slow learner)

Days

Number of days absent from school (count outcome)

Source

MASS package. See also Venables and Ripley (2002), *Modern Applied Statistics with S*.

Infertility Matched Case-Control Study

Description

investigating the relationship between infertility and abortions.

Usage

data_infertility

Format

A data frame with 248 observations and 8 variables:

education

Education level (0 = 0–5 years, 1 = 6–11 years, 2 = 12+ years)

age

Age in years

parity

Number of prior pregnancies

induced

Number of induced abortions

case

Infertility case status (1 = case, 0 = control)

spontaneous

Number of spontaneous abortions

stratum

Matched set ID

pooled.stratum

Pooled stratum ID used for conditional regression

Source

https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/infert.html

Lung Cancer Trial Data

Description

Survival data from a clinical trial of lung cancer patients conducted by the Veteran's Administration.

Usage

data_lungcancer

Format

A data frame with 137 observations and 8 variables:

trt

Treatment group (1 = standard, 2 = test)

celltype

Cell type (squamous, smallcell, adeno, large)

time

Survival time (in days)

status

Censoring status (1 = died, 0 = censored)

karno

Karnofsky performance score (higher = better)

diagtime

Months from diagnosis to randomization

age

Age in years

prior

Prior therapy (0 = no, 10 = yes)

Source

https://CRAN.R-project.org/package=survival

References

Kalbfleisch JD and Prentice RL (1980). The Statistical Analysis of Failure Time Data.

Descriptive Summary Table for Study Characteristics (User-Friendly)

Description

Creates a clean, publication-ready summary table using 'gtsummary::tbl_summary()'. Designed for beginner analysts, this function applies sensible defaults and flexible options to display categorical and continuous variables with or without stratification. It supports one-line summaries of dichotomous variables, handles missing data gracefully, and includes an optional "Overall" column for comparison.

Usage

descriptive_table(
  data,
  exposures,
  by = NULL,
  percent = c("column", "row"),
  digits = 1,
  show_missing = c("ifany", "no"),
  show_dichotomous = c("all_levels", "single_row"),
  show_overall = c("no", "first", "last"),
  statistic = NULL,
  value = NULL
)

Arguments

Value

A 'gtsummary::tbl_summary' object with additional class '"descriptive_table"'. Can be printed, customized, merged, or exported.

Examples

if (requireNamespace("mlbench", quietly = TRUE)) {
  data("PimaIndiansDiabetes2", package = "mlbench")
  library(dplyr)
pima <- PimaIndiansDiabetes2 |>
  mutate(
    diabetes = ifelse(diabetes == "pos", 1, 0),

    bmi = cut(
      mass,
      breaks = c(-Inf, 18.5, 24.9, 29.9, Inf),
      labels = c("Underweight", "Normal", "Overweight", "Obese")
    )
  )
  descriptive_table(pima, exposures = c("age", "bmi"),
                    by = "diabetes")
}

Dissect a Dataset Before Regression

Description

Returns a tidy summary of each variable's structure, missingness, uniqueness, and suitability for use in regression models.

Usage

dissect(data)

Arguments

Value

A tibble with columns: Variable, Type, Missing ( and Regression Hint.

Examples

dissect(data_birthwt)

Identify Confounders Using the Change-in-Estimate Method

Description

Identifies whether one or more variables are confounders by comparing the crude and adjusted effect estimates of a primary exposure on an outcome. A variable is flagged as a confounder if its inclusion changes the estimate by more than a specified threshold (default = 10

Usage

identify_confounder(
  data,
  outcome,
  exposure,
  potential_confounder,
  approach = "logit",
  threshold = 10
)

Arguments

Details

Supports logistic, log-binomial, Poisson, robust Poisson, negative binomial, and linear regression approaches.

This method does not evaluate effect modification. Use causal diagrams (e.g., DAGs) and subject-matter knowledge to supplement decisions.

Value

If one confounder is provided, prints crude and adjusted estimates with a confounding flag. If multiple are given, returns a tibble with:

covariate

Name of potential confounder.

crude_est

Crude effect estimate.

adjusted_est

Adjusted estimate including the confounder.

pct_change

Percent change from crude to adjusted.

is_confounder

Logical: whether confounding threshold is exceeded.

See Also

[check_convergence()], [interaction_models()]

Examples

data <- data_PimaIndiansDiabetes
identify_confounder(
  data = data,
  outcome = "glucose",
  exposure = "insulin",
  potential_confounder = "age_cat",
  approach = "linear"
)

Compare Models With and Without Interaction Term

Description

This function fits two models—one with and one without an interaction term between an exposure and a potential effect modifier— and compares them using either a likelihood ratio test (LRT) or Wald test. It is useful for assessing whether there is statistical evidence of interaction (effect modification).

Usage

interaction_models(
  data,
  outcome,
  exposure,
  covariates = NULL,
  effect_modifier,
  approach = "logit",
  test = c("LRT", "Wald"),
  verbose = TRUE
)

Arguments

Value

A list with the following elements:

• model_no_interaction: The model without the interaction term.

• model_with_interaction: The model with the interaction term.

• p_value: The p-value for interaction (based on selected test).

• interpretation: A brief text interpretation if verbose = TRUE.

Examples

data <- data_PimaIndiansDiabetes

Merge Multiple gtsummary Tables (Descriptive, Univariate, Multivariable)

Description

Flexibly merges any 2 or more 'gtsummary' tables (e.g., from 'descriptive_table()', 'uni_reg()', 'multi_reg()') into a single table using 'tbl_merge()'. Automatically applies column spanners based on the order of inputs.

Usage

merge_tables(..., spanners = NULL)

Arguments

Value

A merged 'gtsummary::tbl_merge' object.

Examples

if (requireNamespace("mlbench", quietly = TRUE)) {
  data("PimaIndiansDiabetes2", package = "mlbench")
  library(dplyr)
  library(gtregression)

  # Prepare data
  pima <- PimaIndiansDiabetes2 |>
    mutate(
      diabetes = ifelse(diabetes == "pos", 1, 0),
      bmi_cat = cut(
        mass,
        breaks = c(-Inf, 18.5, 24.9, 29.9, Inf),
        labels = c("Underweight", "Normal", "Overweight", "Obese")
      )
    )

  # Descriptive table
  desc_tbl <- descriptive_table(pima,
                                exposures = c("age", "bmi_cat"),
                                by = "diabetes")

  # Univariate logistic regression
  uni_tbl <- uni_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age", "bmi_cat"),
    approach = "logit"
  )

  # Multivariable logistic regression
  multi_tbl <- multi_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age", "bmi_cat"),
    approach = "logit"
  )

  # Merge descriptive + univariate + multivariate
  merge_tables(desc_tbl, uni_tbl, multi_tbl)

  # Merge with custom spanners
  merge_tables(desc_tbl, uni_tbl, spanners = c("Summary", "Crude"))

  # Merge just uni and multi
  merge_tables(uni_tbl, multi_tbl)
}

Modify Regression Table Labels and Layout

Description

Allows customization of labels, headers, and layout of regression tables created using 'gtsummary'. Designed for tables from functions like 'uni_reg()', 'multi_reg()', etc.

Usage

modify_table(
  gt_table,
  variable_labels = NULL,
  level_labels = NULL,
  header_labels = NULL,
  caption = NULL,
  bold_labels = FALSE,
  bold_levels = FALSE,
  remove_N = FALSE,
  remove_N_obs = FALSE,
  remove_abbreviations = FALSE,
  caveat = NULL
)

Arguments

Value

A customized 'gtsummary' table object with modified labels, layout, and options.

Examples

if (requireNamespace("mlbench", quietly = TRUE)) {
  data("PimaIndiansDiabetes2", package = "mlbench")
  library(dplyr)
  library(gtregression)

  # Prepare data
  pima <- PimaIndiansDiabetes2 |>
    mutate(
      diabetes = ifelse(diabetes == "pos", 1, 0),
      bmi_cat = cut(
        mass,
        breaks = c(-Inf, 18.5, 24.9, 29.9, Inf),
        labels = c("Underweight", "Normal", "Overweight", "Obese")
      )
    )

  # Descriptive table
  desc_tbl <- descriptive_table(pima,
                                exposures = c("age", "bmi_cat"),
                                by = "diabetes")

  # Univariate logistic regression
  uni_rr <- uni_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age", "bmi_cat"),
    approach = "logit"
  )
}

Multivariable Regression (Adjusted Odds, Risk, or Rate Ratios)

Description

Fits multivariable regression models for binary, count, or continuous outcomes and returns a publication-ready summary table using 'gtsummary'. Depending on the specified 'approach', the function estimates adjusted Odds Ratios (OR), Risk Ratios (RR), Incidence Rate Ratios (IRR), or Beta coefficients.

Usage

multi_reg(data, outcome, exposures, approach = "logit")

Arguments

Value

An object of class 'multi_reg', extending 'gtsummary::tbl_regression'. Additional components can be accessed using:

• x$models: List of fitted model objects.

• x$model_summaries: List of summary outputs.

• x$reg_check: Regression diagnostics (only for linear models).

• x$table: Returns the main regression table.

Accessors

$models

List of fitted model objects.

$model_summaries

A tibble of tidy regression summaries for each model.

See Also

[uni_reg()], [plot_reg()], [plot_reg_combine()]

Examples

if (requireNamespace("mlbench", quietly = TRUE)) {
  data(PimaIndiansDiabetes2, package = "mlbench")
  pima <- dplyr::mutate(PimaIndiansDiabetes2,
  diabetes = ifelse(diabetes == "pos", 1, 0))
  model <- multi_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age", "mass"),
    approach = "logit"
  )
 print(model)
}

Visualize a Regression Model as a Forest Plot

Description

Creates a forest plot from a 'gtsummary' object. Supports both univariate and multivariable models with hierarchical labels for categorical variables. Designed to work seamlessly with outputs from functions like 'uni_reg()' and 'multi_reg()'.

Usage

plot_reg(
  tbl,
  title = NULL,
  ref_line = 1,
  order_y = NULL,
  log_x = FALSE,
  xlim = NULL,
  breaks = NULL,
  point_color = "#1F77B4",
  errorbar_color = "#4C4C4C",
  base_size = 14,
  show_ref = TRUE
)

Arguments

Value

A 'ggplot2' object representing the forest plot.

Examples

if (requireNamespace("mlbench", quietly = TRUE)) {
  data("PimaIndiansDiabetes2", package = "mlbench")
  library(dplyr)
  library(gtregression)

  # Prepare data
  pima <- PimaIndiansDiabetes2 |>
    mutate(
      diabetes = ifelse(diabetes == "pos", 1, 0),
      bmi_cat = cut(
        mass,
        breaks = c(-Inf, 18.5, 24.9, 29.9, Inf),
        labels = c("Underweight", "Normal", "Overweight", "Obese"))
  )

  # Univariate logistic regression
  uni_rr <- uni_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age", "bmi_cat"),
    approach = "logit"
  )
plot_reg(uni_rr)
}

Visualize Univariate and Multivariate Regression Side-by-Side

Description

Generates side-by-side plots to compare univariate & multivariable results

Usage

plot_reg_combine(
  tbl_uni,
  tbl_multi,
  title_uni = NULL,
  title_multi = NULL,
  ref_line = 1,
  order_y = NULL,
  log_x = FALSE,
  point_color = "#1F77B4",
  errorbar_color = "#4C4C4C",
  base_size = 14,
  show_ref = TRUE,
  xlim_uni = NULL,
  breaks_uni = NULL,
  xlim_multi = NULL,
  breaks_multi = NULL
)

Arguments

Value

A 'ggplot2' object with two forest plots displayed side-by-side.

Examples

if (requireNamespace("mlbench", quietly = TRUE)) {
  data("PimaIndiansDiabetes2", package = "mlbench")
  library(dplyr)
  library(gtregression)

  # Prepare data
  pima <- PimaIndiansDiabetes2 |>
    mutate(
      diabetes = ifelse(diabetes == "pos", 1, 0),
      bmi_cat = cut(
        mass,
        breaks = c(-Inf, 18.5, 24.9, 29.9, Inf),
        labels = c("Underweight", "Normal", "Overweight", "Obese")
      ),
      age_cat = cut(
        age,
        breaks = c(-Inf, 29, 49, Inf),
        labels = c("Young", "Middle-aged", "Older")
      )
    )

  # Univariate logistic regression
  uni_rr <- uni_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age_cat", "bmi_cat"),
    approach = "logit"
  )

  # Multivariable logistic regression
  multi_rr <- multi_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age_cat", "bmi_cat"),
    approach = "logit"
  )

  # Combined plot
  plot_reg_combine(uni_rr, multi_rr)
}

Save Multiple Tables and Plots to a Word Document

Description

Saves a collection of gtsummary tables and ggplot2 plots into a .docx file.

Usage

save_docx(tables = NULL, plots = NULL, filename = "report.docx", titles = NULL)

Arguments

Value

A Word document saved to a temporary directory (if no path is given). No object is returned.

Examples

library(gtsummary)
library(ggplot2)
tbl <- tbl_regression(glm(mpg ~ hp + wt, data = mtcars))
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point()
save_docx(
  tables = list(tbl),
  plots = list(p),
  filename = file.path(tempdir(), "report.docx"),
  titles = c("Table 1: Regression", "Figure 1: Scatterplot")
)

Save a Single Plot

Description

Saves a ggplot2 plot to a file in PNG, PDF, or JPG format.

Usage

save_plot(
  plot,
  filename = "plot",
  format = c("png", "pdf", "jpg"),
  width = 8,
  height = 6,
  dpi = 300
)

Arguments

Value

Saves the file to a temporary directory (if no path is given).

Examples

library(ggplot2)
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point()
save_plot(p, filename = file.path(tempdir(), "scatterplot"), format = "png")

Save a Single Regression Table

Description

Saves a gtsummary table as a Word, PDF, or HTML file

Usage

save_table(tbl, filename = "table", format = c("docx", "pdf", "html"))

Arguments

Value

Saves the file to a temporary directory (if no path is given). Does not return an object.

Examples

model <- glm(mpg ~ hp + wt, data = mtcars)
tbl <- gtsummary::tbl_regression(model)
save_table(tbl, filename = file.path(tempdir(), "regression_table"), format = "docx")

Stepwise Model Selection with Evaluation Metrics

Description

Performs stepwise model selection using forward, backward, or both directions across different regression approaches. Returns a summary table with evaluation metrics (AIC, BIC, log-likelihood, deviance) and the best model.

Usage

select_models(
  data,
  outcome,
  exposures,
  approach = "logit",
  direction = "forward"
)

Arguments

Value

A list with the following components:

• results_table: A tibble summarising each tested model's metric (AIC, BIC, deviance, log-likelihood, adjusted R² if applicable).

• best_model: The best-fitting model object based on low AIC.

• all_models: A named list of all fitted models.

Examples

data <- data_PimaIndiansDiabetes
stepwise <- select_models(
  data = data,
  outcome = "glucose",
  exposures = c("age", "pregnant", "mass"),
  approach = "linear",
  direction = "forward"
)
summary(stepwise)
stepwise$results_table
stepwise$best_model

Stratified Multivariable Regression (Adjusted OR, RR, IRR, or Beta)

Description

Performs multivariable regression with multiple exposures on a binary, count, or continuous outcome, stratified by a specified variable. NA values in the stratifier are excluded from analysis.

Usage

stratified_multi_reg(data, outcome, exposures, stratifier, approach = "logit")

Arguments

Value

An object of class 'stratified_multi_reg', which includes: - 'table': A 'gtsummary::tbl_stack' object of regression tables by stratum, - 'models': A named list of model objects for each stratum, - 'model_summaries': A list of tidy model summaries, - 'reg_check': Diagnostics results (if available for the model type).

Accessors

$table

Stacked table of stratified regression outputs.

$models

Named list of fitted models per stratum.

$model_summaries

Tidy summaries for each model.

$reg_check

Regression diagnostic checks (when applicable).

See Also

[multi_reg()], [stratified_uni_reg()], [plot_reg()]

Examples

if (requireNamespace("mlbench", quietly = TRUE) &&
  requireNamespace("dplyr", quietly = TRUE)) {
  data(PimaIndiansDiabetes2, package = "mlbench")
  pima <- dplyr::mutate(
    PimaIndiansDiabetes2,
    diabetes = ifelse(diabetes == "pos", 1, 0),
    glucose_cat = dplyr::case_when(
      glucose < 140 ~ "Normal",
      glucose >= 140 ~ "High"
    )
  )
  stratified_multi <- stratified_multi_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age", "mass"),
    stratifier = "glucose_cat",
    approach = "logit"
  )
  stratified_multi$table
}

Performs univariate regression for each exposure on a binary, count, or continuous outcome, stratified by a specified variable. Produces a stacked 'gtsummary' table with one column per stratum, along with underlying models and diagnostics.

Description

Performs univariate regression for each exposure on a binary, count, or continuous outcome, stratified by a specified variable. Produces a stacked 'gtsummary' table with one column per stratum, along with underlying models and diagnostics.

Usage

stratified_uni_reg(data, outcome, exposures, stratifier, approach = "logit")

Arguments

Value

An object of class 'stratified_uni_reg', which includes: - 'table': A 'gtsummary::tbl_stack' object with stratified results, - 'models': A list of fitted models for each stratum, - 'model_summaries': A tidy list of model summaries, - 'reg_check': A tibble of regression diagnostics (when available).

Accessors

$table

Stacked stratified regression table.

$models

List of fitted model objects for each stratum.

$model_summaries

List of tidy model summaries.

$reg_check

Diagnostic check results (when applicable).

See Also

[multi_reg()], [plot_reg()], [identify_confounder()]

Examples

if (requireNamespace("mlbench", quietly = TRUE) &&
  requireNamespace("dplyr", quietly = TRUE)) {
  data(PimaIndiansDiabetes2, package = "mlbench")
  pima <- dplyr::mutate(
    PimaIndiansDiabetes2,
    diabetes = ifelse(diabetes == "pos", 1, 0),
    glucose_cat = dplyr::case_when(
      glucose < 140 ~ "Normal",
      glucose >= 140 ~ "High"
    )
  )
  stratified_uni <- stratified_uni_reg(
    data = pima,
    outcome = "diabetes",
    exposures = c("age", "mass"),
    stratifier = "glucose_cat",
    approach = "logit"
  )
  stratified_uni$table
}

Univariate regression (Odds, Risk, or Rate Ratios)

Description

Performs univariate regression for each exposure on a binary, continuous, or count outcome. Depending on 'approach', returns either Odds Ratios (OR), Risk Ratios (RR), or Incidence Rate Ratios (IRR).

Usage

uni_reg(data, outcome, exposures, approach = "logit")

Arguments

Details

This function requires the following packages: 'dplyr', 'purrr', 'gtsummary', 'risks'.

Value

A list of class 'uni_reg' and 'gtsummary::tbl_stack', including:

• A publication-ready regression table ('tbl_stack')

• Accessor elements:

  • '$models': Fitted regression models for each exposure

  • '$model_summaries': Tidy model summaries

  • '$reg_check': Diagnostics (only for linear regression)

See Also

multi_reg, plot_reg

Examples

data(PimaIndiansDiabetes2, package = "mlbench")
library(dplyr)
pima <- PimaIndiansDiabetes2 |>
  dplyr::mutate(diabetes = ifelse(diabetes == "pos", 1, 0))
uni_reg(pima, outcome = "diabetes", exposures = "age", approach = "logit")