Univariate regression — uni_reg • gtregression

Fit one model per exposure and return a clean regression table in flextable or gt format.

Usage

uni_reg(
  data,
  outcome,
  exposures,
  approach = "logit",
  format = c("flextable", "gt"),
  theme = c("minimal"),
  model_stats = FALSE,
  show_ref = TRUE
)

Arguments

data: A data frame containing the outcome and exposure variables.
outcome: Character scalar; outcome column name. Quoted and bare names are accepted.
exposures: Character vector; exposure column names. Quoted names are recommended in scripts, and bare names are also accepted.
approach: Regression approach. One of "logit", "firth", "logbinomial", "poisson", "robpoisson", "linear", or "negbin". Use "firth" for Firth penalized logistic regression, especially with sparse cells or separation.
format: One of "flextable" (default) or "gt".
theme: Preset name (e.g. "minimal", "striped", "clinical", "shaded", "jama") or primitives c("plain","zebra","lines","labels_bold","compact","header_shaded")
model_stats: Logical; if TRUE, extract model-fit statistics such as AIC, BIC, log-likelihood, deviance, pseudo R-squared for non-linear models, and R-squared for linear models. Statistics are stored in the returned object's model_stats element and are not added to the publication table.
show_ref: Logical; if TRUE (default), display reference-category rows as "Ref.". If FALSE, hide reference rows; a message reminds users to use show_ref = TRUE when reference rows are needed.

Value

A list of class c("gtregression","uni_reg", ...) with elements:

table: A flextable (when format="flextable") or gt_tbl (when format="gt").
table_body: Data frame of numeric estimates and CIs.
table_display: Data frame for display (headers + levels).
models: List of fitted univariate models.
model_summaries: Per-model summary() results.
model_stats: Model-fit statistics when model_stats = TRUE; otherwise NULL.
variable_labels: Named character vector of display labels used for exposure variables.
reg_check: Diagnostics for linear models; message otherwise.
approach, format, source: Metadata fields.

Details

Use this when you want a quick crude association table before building an adjusted model. The fitted models are kept in the returned object, so the formatted table does not hide the underlying analysis.

If exposure variables have a "label" attribute, for example from labelled::var_label(), those labels are used automatically in the displayed table and plots. Internal matching still uses the original column names.

Examples

d <- mtcars
if (requireNamespace("gt", quietly = TRUE)) {
  uni_reg(d, "am", c("mpg","cyl"), approach = "logit", format = "gt")$table
}


  Characteristic
      N
      OR (95% CI)
      p-value
    
mpg
32
1.36 (1.09-1.70)
0.008
cyl
32
0.50 (0.30-0.82)
0.006
Abbreviations: OR = Odds Ratio; CI = Confidence Interval.
    
if (requireNamespace("flextable", quietly = TRUE)) {
  ft <- uni_reg(d, "am", c("mpg","cyl"), approach = "logit", format = "flextable")
  class(ft$table)
}
#> [1] "flextable"

endometrial_data <- data_endometrial
endometrial_data$HG <- factor(endometrial_data$HG, levels = c(0, 1))
endometrial_data$NV <- factor(endometrial_data$NV, levels = c(0, 1))
uni_reg(endometrial_data, HG, c(NV, PI, EH), approach = firth, format = gt)$table


  Characteristic
      N
      OR (95% CI)
      p-value
    
NV
79

0


Ref.
  1


76.37 (9.27-9,962.22)
<0.001
PI
79
1.01 (0.97-1.06)
0.576
EH
79
0.03 (0.01-0.13)
<0.001
Abbreviations: OR = Odds Ratio from Firth penalized logistic regression; CI = Confidence Interval.
    
Ref. = reference category.

Characteristic	N	OR (95% CI)	p-value
mpg	32	1.36 (1.09-1.70)	0.008
cyl	32	0.50 (0.30-0.82)	0.006
Abbreviations: OR = Odds Ratio; CI = Confidence Interval.

Characteristic	N	OR (95% CI)	p-value
NV	79
0		Ref.
1		76.37 (9.27-9,962.22)	<0.001
PI	79	1.01 (0.97-1.06)	0.576
EH	79	0.03 (0.01-0.13)	<0.001
Abbreviations: OR = Odds Ratio from Firth penalized logistic regression; CI = Confidence Interval.
Ref. = reference category.