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

Source: R/multi_reg.R
multi_reg.Rd

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

data

A data frame containing the analysis variables.

outcome

The name of the outcome variable. Must be a character string.

exposures

A character vector of predictor variables to include.

approach

Modeling approach to use. One of: - `"logit"` for logistic regression (OR), - `"log-binomial"` for log-binomial regression (RR), - `"poisson"` for Poisson regression (IRR), - `"robpoisson"` for robust Poisson regression (RR), - `"linear"` for linear regression (Beta coefficients), - `"negbin"` for negative binomial regression (IRR).

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)
}
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#>       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'><strong>Characteristic</strong></span></th>
#>       <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="estimate"><span class='gt_from_md'><strong>Adjusted OR</strong></span></th>
#>       <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="conf.low"><span class='gt_from_md'><strong>95% CI</strong></span></th>
#>       <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span></th>
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#>     <tr><td headers="label" class="gt_row gt_left">age</td>
#> <td headers="estimate" class="gt_row gt_center">1.05</td>
#> <td headers="conf.low" class="gt_row gt_center">1.03, 1.06</td>
#> <td headers="p.value" class="gt_row gt_center"><0.001</td></tr>
#>     <tr><td headers="label" class="gt_row gt_left">mass</td>
#> <td headers="estimate" class="gt_row gt_center">1.11</td>
#> <td headers="conf.low" class="gt_row gt_center">1.09, 1.14</td>
#> <td headers="p.value" class="gt_row gt_center"><0.001</td></tr>
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#>   <tfoot class="gt_sourcenotes">
#>     <tr>
#>       <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>Abbreviations: CI = Confidence Interval, OR = Odds Ratio</span></td>
#>     </tr>
#>     <tr>
#>       <td class="gt_sourcenote" colspan="4"><span class='gt_from_md'>N = 757 complete observations included in the multivariate model</span></td>
#>     </tr>
#>   </tfoot>
#>   
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