Bayesian Decision Curve Analysis

Bayesian Decision Curve Analysis

Usage

bayesdca(
  data,
  outcomes,
  outcomePos,
  predictors,
  thresholdMin = 0.01,
  thresholdMax = 0.5,
  thresholdPoints = 50,
  useExternalPrevalence = FALSE,
  externalCases = 100,
  externalTotal = 500,
  bayesianAnalysis = TRUE,
  priorStrength = 2,
  bootstrapCI = TRUE,
  bootstrapReps = 2000,
  calculateEVPI = FALSE,
  nDraws = 2000,
  directionIndicator = ">="
)

Arguments

data

The data as a data frame.

outcomes

Binary outcome variable (0/1) representing the true disease status or event.

outcomePos

Specifies which level of the outcome variable should be treated as the positive class.

predictors

Variables containing either probability predictions from models or binary results (0/1) from diagnostic tests.

thresholdMin

Minimum decision threshold for the analysis.

thresholdMax

Maximum decision threshold for the analysis.

thresholdPoints

Number of threshold points to evaluate.

useExternalPrevalence

Use external prevalence data instead of sample prevalence.

externalCases

Number of cases in external prevalence data.

externalTotal

Total sample size in external prevalence data.

bayesianAnalysis

Perform Bayesian analysis with uncertainty quantification.

priorStrength

Strength of prior (effective sample size).

bootstrapCI

Calculate bootstrap confidence intervals for non-Bayesian analysis.

bootstrapReps

Number of bootstrap replications for confidence intervals.

calculateEVPI

Calculate Expected Value of Perfect Information.

nDraws

Number of posterior draws for Bayesian analysis.

directionIndicator

Direction of classification relative to the cutpoint. Use '>=' when higher values predict positive outcomes.

Value

A results object containing:

results$instructions					a html
results$summary					a html
results$netBenefitTable					a table
results$modelResults					an array of tables
results$comparisonTable					a table
results$evpiTable					a table
results$mainPlot					an image
results$deltaPlot					an image
results$probPlot					an image
results$evpiPlot					an image

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$netBenefitTable$asDF

as.data.frame(results$netBenefitTable)