Univariable regression model summary

This function estimates univariable regression models and returns them in a publication-ready table. It can create regression models holding either a covariate or an outcome constant.

Usage

tbl_uvregression(data, ...)

# S3 method for class 'data.frame'
tbl_uvregression(
  data,
  y = NULL,
  x = NULL,
  method,
  method.args = list(),
  exponentiate = FALSE,
  label = NULL,
  include = everything(),
  tidy_fun = broom.helpers::tidy_with_broom_or_parameters,
  hide_n = FALSE,
  show_single_row = NULL,
  conf.level = 0.95,
  estimate_fun = ifelse(exponentiate, label_style_ratio(), label_style_sigfig()),
  pvalue_fun = label_style_pvalue(digits = 1),
  formula = "{y} ~ {x}",
  add_estimate_to_reference_rows = FALSE,
  conf.int = TRUE,
  ...
)

# S3 method for class 'survey.design'
tbl_uvregression(
  data,
  y = NULL,
  x = NULL,
  method,
  method.args = list(),
  exponentiate = FALSE,
  label = NULL,
  include = everything(),
  tidy_fun = broom.helpers::tidy_with_broom_or_parameters,
  hide_n = FALSE,
  show_single_row = NULL,
  conf.level = 0.95,
  estimate_fun = ifelse(exponentiate, label_style_ratio(), label_style_sigfig()),
  pvalue_fun = label_style_pvalue(digits = 1),
  formula = "{y} ~ {x}",
  add_estimate_to_reference_rows = FALSE,
  conf.int = TRUE,
  ...
)

Arguments

data: (data.frame, survey.design)
A data frame or a survey design object.
...: Additional arguments passed to broom.helpers::tidy_plus_plus().
y, x: (expression, string)
Model outcome (e.g. y=recurrence or y=Surv(time, recur)) or covariate (e.g. x=trt. All other column specified in include will be regressed against the constant y or x. Specify one and only one of y or x.
method: (string/function)
Regression method or function, e.g. lm, glm, survival::coxph, survey::svyglm, etc. Methods may be passed as functions (method=lm) or as strings (method='lm').
method.args: (named list)
Named list of arguments assed to method.
exponentiate: (scalar logical)
Logical indicating whether to exponentiate the coefficient estimates. Default is FALSE.
label: (formula-list-selector)
Used to change variables labels, e.g. list(age = "Age", stage = "Path T Stage")
include: (tidy-select)
Variables to include in output. Default is everything().
tidy_fun: (function)
Tidier function for the model. Default is to use broom::tidy(). If an error occurs, the tidying of the model is attempted with parameters::model_parameters(), if installed.
hide_n: (scalar logical)
Hide N column. Default is FALSE
show_single_row: (tidy-select)
By default categorical variables are printed on multiple rows. If a variable is dichotomous (e.g. Yes/No) and you wish to print the regression coefficient on a single row, include the variable name(s) here.
conf.level: (scalar real)
Confidence level for confidence interval/credible interval. Defaults to 0.95.
estimate_fun: (function)
Function to round and format coefficient estimates. Default is label_style_sigfig() when the coefficients are not transformed, and label_style_ratio() when the coefficients have been exponentiated.
pvalue_fun: (function)
Function to round and format p-values. Default is label_style_pvalue().
formula: (string)
String of the model formula. Uses glue::glue() syntax. Default is "{y} ~ {x}", where {y} is the dependent variable, and {x} represents a single covariate. For a random intercept model, the formula may be formula = "{y} ~ {x} + (1 | gear)".
add_estimate_to_reference_rows: (scalar logical)
Add a reference value. Default is FALSE.
conf.int: (scalar logical)
Logical indicating whether or not to include a confidence interval in the output. Default is TRUE.

Value

A tbl_uvregression object

`x` and `y` arguments

For models holding outcome constant, the function takes as arguments a data frame, the type of regression model, and the outcome variable y=. Each column in the data frame is regressed on the specified outcome. The tbl_uvregression() function arguments are similar to the tbl_regression() arguments. Review the tbl_uvregression vignette for detailed examples.

You may alternatively hold a single covariate constant. For this, pass a data frame, the type of regression model, and a single covariate in the x= argument. Each column of the data frame will serve as the outcome in a univariate regression model. Take care using the x argument that each of the columns in the data frame are appropriate for the same type of model, e.g. they are all continuous variables appropriate for lm, or dichotomous variables appropriate for logistic regression with glm.

Methods

The default method for tbl_regression() model summary uses broom::tidy(x) to perform the initial tidying of the model object. There are, however, a few models that use modifications.

"parsnip/workflows": If the model was prepared using parsnip/workflows, the original model fit is extracted and the original x= argument is replaced with the model fit. This will typically go unnoticed; however,if you've provided a custom tidier in tidy_fun= the tidier will be applied to the model fit object and not the parsnip/workflows object.
"survreg": The scale parameter is removed, broom::tidy(x) %>% dplyr::filter(term != "Log(scale)")
"multinom": This multinomial outcome is complex, with one line per covariate per outcome (less the reference group)
"gam": Uses the internal tidier tidy_gam() to print both parametric and smooth terms.
"lmerMod", "glmerMod", "glmmTMB", "glmmadmb", "stanreg", "brmsfit": These mixed effects models use broom.mixed::tidy(x, effects = "fixed"). Specify tidy_fun = broom.mixed::tidy to print the random components.

Author

Daniel D. Sjoberg

Examples

# Example 1 ----------------------------------
tbl_uvregression(
  trial,
  method = glm,
  y = response,
  method.args = list(family = binomial),
  exponentiate = TRUE,
  include = c("age", "grade")
)


  Characteristic
      N
      OR¹
      95% CI¹
      p-value
    
Age
183
1.02
1.00, 1.04
0.10
Grade
193





    I


—
—

    II


0.95
0.45, 2.00
0.9
    III


1.10
0.52, 2.29
0.8
¹ OR = Odds Ratio, CI = Confidence Interval
    

# Example 2 ----------------------------------
# rounding pvalues to 2 decimal places
library(survival)

tbl_uvregression(
  trial,
  method = coxph,
  y = Surv(ttdeath, death),
  exponentiate = TRUE,
  include = c("age", "grade", "response"),
  pvalue_fun = label_style_pvalue(digits = 2)
)


  Characteristic
      N
      HR¹
      95% CI¹
      p-value
    
Age
189
1.01
0.99, 1.02
0.33
Grade
200





    I


—
—

    II


1.28
0.80, 2.05
0.31
    III


1.69
1.07, 2.66
0.024
Tumor Response
193
0.50
0.31, 0.78
0.003
¹ HR = Hazard Ratio, CI = Confidence Interval

Characteristic	N	OR¹	95% CI¹	p-value
Age	183	1.02	1.00, 1.04	0.10
Grade	193
I		—	—
II		0.95	0.45, 2.00	0.9
III		1.10	0.52, 2.29	0.8
¹ OR = Odds Ratio, CI = Confidence Interval

Characteristic	N	HR¹	95% CI¹	p-value
Age	189	1.01	0.99, 1.02	0.33
Grade	200
I		—	—
II		1.28	0.80, 2.05	0.31
III		1.69	1.07, 2.66	0.024
Tumor Response	193	0.50	0.31, 0.78	0.003
¹ HR = Hazard Ratio, CI = Confidence Interval