dx_plot_decision_curve: Plot Decision Curve

In overdodactyl/diagnosticSummary: Diagnostic Summaries

Plot Decision Curve

Description

Generates a decision curve to visualize the net benefit of a binary classification model across a specified range of threshold probabilities. The decision curve shows the net benefit of using the model at different threshold probabilities compared to the strategy of treating all or none.

Usage

dx_plot_decision_curve(dx_obj, plot_range = c(0.05, 0.95))

Arguments

dx_obj	A dx object containing a 'thresholds' data frame with columns for 'threshold' and 'net_benefit'.
plot_range	A numeric vector of length 2 specifying the lower and upper bounds for the range of threshold probabilities to be plotted. Defaults to c(0.05, .95).

Details

Decision Curve Analysis (DCA) is a method for evaluating and comparing the clinical usefulness of prediction models by considering the clinical consequences of decision making. The decision curve plots the net benefit of using the model across a range of threshold probabilities for making a decision. The net benefit is calculated as the true positive rate (sensitivity) minus the weighted false positive rate, where the weight is the ratio of the cost of false positives relative to the benefit of true positives. This ratio is derived from the threshold probability:

Net Benefit = Sensitivity - (Weight * False Positive Rate)

where Weight = Threshold / (1 - Threshold).

The decision curve will typically be compared against two default strategies: treating all patients or treating none. The net benefit is higher when the model's predictions provide a clear advantage over these default strategies. A well-performing model will have a decision curve that stays above the treat-none and treat-all lines for a range of clinically relevant threshold probabilities.

The 'plot_range' parameter allows limiting the analysis to a relevant range of thresholds, avoiding the extremes where the net benefit calculation can become unstable or not clinically relevant.

Value

A ggplot object representing the decision curve, which can be further customized as needed.

Examples

dx_obj <- dx(
  data = dx_heart_failure,
  true_varname = "truth",
  pred_varname = "predicted",
  outcome_label = "Heart Attack",
  setthreshold = .3
)
dx_plot_decision_curve(dx_obj)

overdodactyl/diagnosticSummary documentation built on Jan. 28, 2024, 10:07 a.m.