Calculate Single ROC Curve for Model Balance Assessment

Computes the Receiver Operating Characteristic (ROC) curve for a single weighting scheme or unweighted data. In causal inference, an ROC curve near the diagonal indicates good balance between treatment groups.

Usage

bal_model_roc_curve(
  .data,
  .exposure,
  .estimate,
  .weights = NULL,
  na.rm = TRUE,
  .focal_level = NULL
)

Arguments

.data

A data frame containing the variables.

.exposure

The treatment/outcome variable (unquoted).

.estimate

The propensity score or fitted values (unquoted).

.weights

Optional single weight variable (unquoted). If NULL, computes unweighted ROC curve.

na.rm

A logical value indicating whether to remove missing values before computation. If FALSE (default), missing values in the input will produce NA in the output.

.focal_level

The level of the outcome variable to consider as the treatment/event. If NULL (default), uses the last level for factors or the maximum value for numeric variables.

Value

A tibble with columns:

threshold

Numeric. The decision threshold.

sensitivity

Numeric. True positive rate at the threshold.

specificity

Numeric. True negative rate at the threshold.

Details

The ROC curve plots sensitivity (true positive rate) against 1-specificity (false positive rate) across all possible threshold values. When propensity scores achieve perfect balance, the ROC curve should lie close to the diagonal line from (0,0) to (1,1), indicating that the propensity scores have no discriminatory ability between treatment groups.

ROC curves that bow significantly above the diagonal indicate that the propensity scores can still distinguish between treatment groups, suggesting inadequate balance.

See also

check_model_roc_curve() for computing ROC curves across multiple weights, bal_model_auc() for the area under the curve summary

Other balance functions: bal_corr(), bal_ess(), bal_ks(), bal_model_auc(), bal_qq(), bal_smd(), bal_vr(), check_balance(), check_ess(), check_model_auc(), check_model_roc_curve(), check_qq(), plot_balance()

Examples

# Unweighted ROC curve
bal_model_roc_curve(nhefs_weights, qsmk, .fitted)
#> # A tibble: 1,568 × 3
#>    threshold sensitivity specificity
#>        <dbl>       <dbl>       <dbl>
#>  1 -Inf            1        0       
#>  2    0.0510       1        0       
#>  3    0.0527       1        0.000860
#>  4    0.0558       1        0.00172 
#>  5    0.0559       1        0.00258 
#>  6    0.0567       1        0.00344 
#>  7    0.0595       1        0.00430 
#>  8    0.0599       1        0.00516 
#>  9    0.0601       0.998    0.00516 
#> 10    0.0608       0.995    0.00516 
#> # ℹ 1,558 more rows

# Weighted ROC curve
bal_model_roc_curve(nhefs_weights, qsmk, .fitted, w_ate)
#> # A tibble: 1,568 × 3
#>    threshold sensitivity specificity
#>        <dbl>       <dbl>       <dbl>
#>  1 -Inf            1        0       
#>  2    0.0510       1        0       
#>  3    0.0527       1        0.000673
#>  4    0.0558       1        0.00135 
#>  5    0.0559       1        0.00202 
#>  6    0.0567       1        0.00270 
#>  7    0.0595       1        0.00338 
#>  8    0.0599       1        0.00406 
#>  9    0.0601       0.989    0.00406 
#> 10    0.0608       0.979    0.00406 
#> # ℹ 1,558 more rows