Univariable screening table (with optional multivariable merge)

Fits one model per candidate predictor (the univariable screen) and renders them as a single table with one row block per predictor. With multivariable = TRUE (default), the full model containing all predictors is merged side by side under "Univariable" / "Multivariable" column groups – the standard presentation of applied epidemiology (the gtsummary::tbl_uvregression() + tbl_merge() workflow).

Usage

table_regression_uv(
  data,
  outcome,
  predictors,
  method = c("glm", "lm", "coxph"),
  family = stats::binomial(),
  multivariable = TRUE,
  complete_cases = FALSE,
  show_columns = c("n", "b", "ci", "p"),
  title = NULL,
  ...
)

Arguments

data

A data frame.

outcome

The outcome column (unquoted name, tidyselect). For method = "coxph", a Surv(time, status) expression evaluated in data (the tbl_uvregression convention).

predictors

Candidate predictor columns (tidyselect, e.g. c(age, sex, education) or where(is.numeric)). The outcome column(s) are dropped from the selection automatically.

method

"glm" (default), "lm", or "coxph" (requires the survival package; estimates render as HRs with exponentiate = TRUE).

family

A stats::family object for method = "glm". Default binomial(). Refused for method = "coxph", and gaussian() with the identity link is refused too: use method = "lm" for the linear screen.

multivariable

Logical, default TRUE: merge the full model (all predictors together) as a second column group.

complete_cases

Logical, default FALSE. TRUE restricts ALL models to the rows complete on outcome + every predictor (common-sample comparison); the reduction is disclosed in the table note.

show_columns

Passed to table_regression(). Default c("n", "b", "ci", "p") – the tbl_uvregression column set; "n" is the per-predictor sample size. The multivariable group carries no N column (its single n is a fit-statistics row, as in the reference layouts). For binary outcomes, add "n_events" for outcome event counts as events/N per factor level (each column group counts on its own estimation sample).

title

Table title; NULL (default) builds "Univariable and multivariable <type> regression: <outcome>".

...

Passed to table_regression() (exponentiate, vcov, cluster, p_adjust, digits, labels, output, ...). show_intercept defaults to FALSE here; nested is not meaningful for a screen and is refused. cluster must be a single vector with one value per row of data; it is aligned to each fit's own estimation sample automatically.

Value

See table_regression() (same output contract).

Sample sizes

By default each univariable model is fit on its own complete cases, so N varies across predictors – that is what the N column discloses (shown on the first row of each block), and a table note states it whenever the Ns differ. The multivariable model is fit on the complete cases of all its variables (its n appears in the fit-statistics rows). Pass complete_cases = TRUE to restrict every model – univariable and multivariable – to the common complete-case sample.

Multiplicity

p_adjust (passed through to table_regression()) treats the whole univariable screen as ONE family (all screened coefficients together); the multivariable model is its own family, as in any multi-model table.

Intercepts

Hidden by default on both sides (each univariable fit has its own nuisance intercept), matching gtsummary::tbl_regression()'s intercept = FALSE default. Pass show_intercept = TRUE to display them.

References

Batra, N. et al. (Eds.) (2021). The Epidemiologist R Handbook, Univariate and multivariable regression. https://epirhandbook.com/en/new_pages/regression.html

Sjoberg, D.D., Whiting, K., Curry, M., Lavery, J.A., & Larmarange, J. (2021). Reproducible summary tables with the gtsummary package. The R Journal, 13(1), 570-580.

Examples

# \donttest{
table_regression_uv(
  sochealth,
  outcome    = smoking,
  predictors = c(age, sex, education),
  family     = binomial(),
  exponentiate = TRUE
)
#> Ordered factor(s) detected. Polynomial contrasts (the R default for `ordered()`) decompose the factor into orthogonal trend components: `.L` = linear, `.Q` = quadratic, `.C` = cubic, `^k` = degree k. Coefficients are trends across the ordered levels, NOT per-level effects against a reference.
#> ℹ To display per-level (treatment) effects, refit with `factor(x, ordered = FALSE)` or set `options(contrasts = c("contr.treatment", "contr.treatment"))`.
#> This message is displayed once per session.
#> Univariable and multivariable logistic regression: smoking
#> 
#>                              Univariable                Multivariable     
#>                    ───────────────────────────────  ───────────────────── 
#>  Variable        │  N     OR      95% CI       p      OR        95% CI    
#> ─────────────────┼────────────────────────────────────────────────────────
#>  age             │ 1175  1.01  [1.00, 1.01]   .292     1.01  [1.00, 1.02] 
#>  sex:            │                                                        
#>    Female (ref.) │    –   –         –         –         –         –       
#>    Male          │ 1175  0.95  [0.72, 1.26]   .713     0.96  [0.73, 1.28] 
#>  education:      │                                                        
#>    .L            │ 1175  0.53  [0.40, 0.70]  <.001     0.53  [0.40, 0.69] 
#>    .Q            │       1.03  [0.81, 1.29]   .832     1.02  [0.81, 1.29] 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n               │                                  1175                  
#>  R² (McFadden)   │                                     0.02               
#>  R² (Nagelkerke) │                                     0.03               
#>  AIC             │                                  1200.9                
#> 
#>                    Mult… 
#>                    ───── 
#>  Variable        │   p   
#> ─────────────────┼───────
#>  age             │  .214 
#>  sex:            │       
#>    Female (ref.) │  –    
#>    Male          │  .800 
#>  education:      │       
#>    .L            │ <.001 
#>    .Q            │  .852 
#> 
#> Note. Logistic regression models.
#> Std. errors: classical (Fisher information).
#> OR = odds ratio.
#> Coefficients exponentiated and displayed as OR; CI bounds exponentiated.
#> Ordered factor `education`: polynomial trends (.L = linear, .Q = quadratic).
# }