DecisionCurve (version 1.4)

cv_decision_curve: Calculate cross-validated decision curves

Description

This is a wrapper for 'decision_curve' that computes k-fold cross-validated estimates of sensitivity, specificity, and net benefit so that cross-validated net benefit curves can be plotted.

Usage

cv_decision_curve(formula, data, family = binomial(link = "logit"),
  thresholds = seq(0, 1, by = 0.01), folds = 5, study.design = c("cohort",
  "case-control"), population.prevalence, policy = c("opt-in", "opt-out"))

Arguments

formula

an object of class 'formula' of the form outcome ~ predictors, giving the prediction model to be fitted using glm. The outcome must be a binary variable that equals '1' for cases and '0' for controls.

data

data.frame containing outcome and predictors. Missing data on any of the predictors will cause the entire observation to be removed.

family

a description of the error distribution and link function to pass to 'glm" used for model fitting. Defaults to binomial(link = "logit") for logistic regression.

thresholds

Numeric vector of high risk thresholds to use when plotting and calculating net benefit values.

folds

Number of folds for k-fold cross-validation.

study.design

Either 'cohort' (default) or 'case-control' describing the study design used to obtain data. See details for more information.

population.prevalence

Outcome prevalence rate in the population used to calculate decision curves when study.design = 'case-control'.

policy

Either 'opt-in' (default) or 'opt-out', describing the type of policy for which to report the net benefit. A policy is 'opt-in' when the standard-of-care for a population is to assign a particular 'treatment' to no one. Clinicians then use a risk model to categorize patients as 'high-risk', with the recommendation to treat high-risk patients with some intervention. Alternatively, an 'opt-out' policy is applicable to contexts where the standard-of-care is to recommend a treatment to an entire patient population. The potential use of a risk model in this setting is to identify patients who are 'low-risk' and recommend that those patients 'opt-out' of treatment.

Value

List with components

• derived.data: derived.data: A data frame in long form showing the following for each predictor and each 'threshold', 'FPR':false positive rate, 'TPR': true positive rate, 'NB': net benefit, 'sNB': standardized net benefit, 'rho': outcome prevalence, 'prob.high.risk': percent of the population considered high risk. 'DP': detection probability = TPR*rho, 'model': name of prediction model or 'all' or 'none', and cost.benefit.ratio's.

• folds: number of folds used for cross-validation.

• call: matched function call.

See Also

summary.decision_curve, decision_curve, Add_CostBenefit_Axis

Examples

# NOT RUN {
full.model_cv <- cv_decision_curve(Cancer~Age + Female + Smokes + Marker1 + Marker2,
                                  data = dcaData,
                                  folds = 5,
                                  thresholds = seq(0, .4, by = .01))

full.model_apparent <- decision_curve(Cancer~Age + Female + Smokes + Marker1 + Marker2,
                                     data = dcaData,
                                     thresholds = seq(0, .4, by = .01),
                                     confidence.intervals = 'none')

plot_decision_curve( list(full.model_apparent, full.model_cv),
                    curve.names = c('Apparent curve', 'Cross-validated curve'),
                    col = c('red', 'blue'),
                    lty = c(2,1),
                    lwd = c(3,2, 2, 1),
                    legend.position = 'bottomright')

# }