decisionpanel: Decision Panel Optimization

In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research

Decision Panel Optimization

Description

Optimize diagnostic test panels by evaluating various combination strategies including parallel testing (cotest), sequential testing, and repeated tests. Creates decision trees to minimize cost while maximizing accuracy.

Usage

decisionpanel(
  data,
  tests,
  testLevels = "",
  gold,
  goldPositive,
  useCosts = FALSE,
  testCosts = "",
  fpCost = 100,
  fnCost = 1000,
  strategies = "all",
  parallelRules = "any",
  customThreshold = 2,
  maxTests = 3,
  sequentialStop = "positive",
  optimizationCriteria = "accuracy",
  minSensitivity = 0.8,
  minSpecificity = 0.8,
  createTree = FALSE,
  treeMethod = "costSensitive",
  maxDepth = 5,
  minSplit = 20,
  showAllCombinations = FALSE,
  topN = 10,
  compareStrategies = TRUE,
  bootstrap = FALSE,
  bootReps = 1000,
  plotTree = TRUE,
  plotComparison = TRUE,
  plotCostEffect = TRUE,
  plotROC = FALSE,
  prevalence = 0,
  crossValidate = FALSE,
  nFolds = 5,
  seed = 12345
)

Arguments

data	The data as a data frame.
tests	Variables representing different diagnostic tests. Can select multiple tests (up to 10) for panel optimization.
testLevels	String specifying positive test levels. Can be: - Single value (used for all tests): "Positive" - Multiple values (comma-separated): "Positive,Yes,1" - Empty string (auto-detection): ""
gold	The gold standard variable for disease classification.
goldPositive	The level of the gold standard variable that indicates disease presence.
useCosts	Include cost considerations in the optimization process.
testCosts	Comma-separated costs for each test in the same order as selected tests. Example: "10,25,50" for three tests.
fpCost	Cost or harm associated with a false positive result.
fnCost	Cost or harm associated with a false negative result.
strategies	Which testing strategies to evaluate in the analysis.
parallelRules	Rule for combining results in parallel testing.
customThreshold	Number of positive tests required for overall positive result (when using custom rule).
maxTests	Maximum number of tests to combine in any strategy.
sequentialStop	When to stop testing in sequential strategies.
optimizationCriteria	Primary criterion for optimizing test panels.
minSensitivity	Minimum sensitivity constraint for panel selection.
minSpecificity	Minimum specificity constraint for panel selection.
createTree	Generate an optimal decision tree for test sequencing.
treeMethod	Method for constructing the decision tree.
maxDepth	Maximum depth of the decision tree.
minSplit	Minimum number of observations required to split a node.
showAllCombinations	Display performance metrics for all possible test combinations.
topN	Number of best-performing panels to display in results.
compareStrategies	Show comparative analysis of different testing strategies.
bootstrap	Calculate bootstrap confidence intervals for performance metrics.
bootReps	Number of bootstrap replications for confidence intervals.
plotTree	Display visual representation of the optimal decision tree.
plotComparison	Create comparison plots for different testing strategies.
plotCostEffect	Create cost-effectiveness frontier plot.
plotROC	Display ROC curves for top performing panels.
prevalence	Known disease prevalence (0 = use sample prevalence).
crossValidate	Perform k-fold cross-validation for panel performance.
nFolds	Number of folds for cross-validation.
seed	Random seed for reproducibility in bootstrap and cross-validation.

Value

A results object containing:

results$summary					a html
results$optimalPanel					a table
results$strategyComparison					a table
results$individualTests					a table
results$allCombinations					a table
results$treeStructure					a html
results$treePerformance					a table
results$crossValidation					a table
results$bootstrapResults					a table
results$treeVisualization					an image
results$strategyComparisonPlot					an image
results$costEffectivenessPlot					an image
results$rocCurvesPlot					an image
results$recommendations					a html

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

results$optimalPanel$asDF

as.data.frame(results$optimalPanel)

Examples

# example will be added

sbalci/ClinicoPathJamoviModule documentation built on June 13, 2025, 9:34 a.m.