Decision Tree Graph for Cost-Effectiveness Analysis
Source:R/decisiongraph.b.R, R/decisiongraph.h.R
decisiongraph.RdCreates interactive decision tree visualizations for medical cost-effectiveness analysis
Creates decision tree graphs for cost-effectiveness analysis with typical decision nodes, chance nodes, and terminal nodes. Supports visualization of treatment pathways, probabilities, costs, and outcomes.
Usage
decisiongraph(
data,
treeType = "costeffectiveness",
clinicalPreset = "none",
decisions,
healthStates,
transitionProbs,
cycleLength = 1,
probabilities,
costs,
utilities,
outcomes,
layout = "horizontal",
nodeShapes = TRUE,
showProbabilities = TRUE,
showCosts = TRUE,
showUtilities = TRUE,
calculateExpectedValues = TRUE,
calculateNMB = TRUE,
willingnessToPay = 50000,
sensitivityAnalysis = FALSE,
discountRate = 0.03,
timeHorizon = 10,
nodeLabels = TRUE,
branchLabels = TRUE,
colorScheme = "medical",
summaryTable = TRUE,
tornado = FALSE,
decisionComparison = TRUE,
incrementalAnalysis = TRUE,
dominanceAnalysis = TRUE,
icer_confidence_intervals = FALSE,
probabilisticAnalysis = FALSE,
psa_advanced_outputs = FALSE,
numSimulations = 1000,
markovAdvanced = FALSE,
tunnelStates,
timeVaryingTransitions = FALSE,
ageSpecificTransitions,
chanceNodeMethod = "simple",
riskAdjustment = FALSE,
cycleCorrection = TRUE,
cohortTrace = FALSE,
cohortSize = 1000,
valueOfInformation = FALSE,
evpi_parameters,
budgetImpactAnalysis = FALSE,
targetPopulationSize = 10000,
marketPenetration = 0.5,
ceacThresholds = "0,100000,5000",
psa_distributions = "normal",
correlatedParameters = FALSE,
correlationMatrix,
performanceMode = "standard",
memoryOptimization = TRUE,
parallelProcessing = FALSE
)Arguments
- data
The data as a data frame containing decision tree parameters.
- treeType
Type of decision tree to create.
- clinicalPreset
Pre-configured analysis templates for common clinical decision analysis scenarios.
- decisions
Variables representing decision nodes (treatment options).
- healthStates
Variables defining health states for Markov model.
- transitionProbs
Variables containing transition probabilities between health states.
- cycleLength
Length of each Markov cycle in years.
- probabilities
Variables containing probability values for chance nodes.
- costs
Variables containing cost values for terminal nodes.
- utilities
Variables containing utility/effectiveness values.
- outcomes
Variables representing clinical outcomes.
- layout
Layout orientation for the decision tree.
- nodeShapes
Use different shapes for different node types.
- showProbabilities
Display probability values on tree branches.
- showCosts
Display cost values at terminal nodes.
- showUtilities
Display utility/effectiveness values.
- calculateExpectedValues
Calculate and display expected costs and utilities.
- calculateNMB
Calculate net monetary benefit (NMB) for decision paths using threshold analysis.
- willingnessToPay
Willingness to pay threshold (cost per QALY) for NMB calculations.
- sensitivityAnalysis
Perform one-way sensitivity analysis on key parameters.
- discountRate
Annual discount rate for future costs and benefits.
- timeHorizon
Time horizon for the analysis in years.
- nodeLabels
Display descriptive labels for nodes.
- branchLabels
Display labels on tree branches.
- colorScheme
Color scheme for the decision tree visualization.
- summaryTable
Show summary table with expected values and cost-effectiveness ratios.
- tornado
Create tornado diagram for sensitivity analysis.
- decisionComparison
Show comparison table with NMB scores for all decision paths.
- incrementalAnalysis
Perform incremental cost-effectiveness ratio (ICER) calculations.
- dominanceAnalysis
Identify dominated and extendedly dominated strategies in ICER analysis.
- icer_confidence_intervals
Calculate confidence intervals for ICERs using bootstrap or PSA results.
- probabilisticAnalysis
Perform probabilistic sensitivity analysis using Monte Carlo simulation.
- psa_advanced_outputs
Generate advanced PSA outputs (CEAC, EVPI, net benefit distributions).
- numSimulations
Number of Monte Carlo simulations for probabilistic analysis.
- markovAdvanced
Enable advanced Markov modeling features.
- tunnelStates
Define tunnel states for temporary health states.
- timeVaryingTransitions
Allow transition probabilities to change over time.
- ageSpecificTransitions
Variables defining age-specific transition rates.
- chanceNodeMethod
Method for calculating probabilities at chance nodes.
- riskAdjustment
Apply risk adjustment to probabilities based on patient characteristics.
- cycleCorrection
Apply half-cycle correction for more accurate discounting in Markov models.
- cohortTrace
Generate cohort trace showing population distribution across health states over time.
- cohortSize
Size of hypothetical cohort for trace analysis and budget impact modeling.
- valueOfInformation
Perform Expected Value of Perfect Information (EVPI) and partial EVPI analysis.
- evpi_parameters
Parameters for partial EVPI analysis.
- budgetImpactAnalysis
Perform budget impact analysis comparing total costs of alternative strategies.
- targetPopulationSize
Size of target population for budget impact calculations.
- marketPenetration
Expected market penetration rate for new intervention.
- ceacThresholds
Cost-effectiveness acceptability curve thresholds (min,max,step).
- psa_distributions
Default probability distribution for uncertain parameters in PSA.
Account for correlations between uncertain parameters in PSA.
- correlationMatrix
Variables defining correlation structure between uncertain parameters.
- performanceMode
Balance between speed and completeness of analysis.
- memoryOptimization
Enable memory-efficient processing for large simulations.
- parallelProcessing
Enable parallel processing for faster PSA calculations.
Value
A results object containing:
results$treeplot | Decision tree visualization with nodes and branches | ||||
results$summaryTable | a table | ||||
results$nodeTable | a table | ||||
results$tornadoplot | Sensitivity analysis tornado diagram | ||||
results$sensitivityTable | a table | ||||
results$text1 | a html | ||||
results$text2 | a html | ||||
results$markovTable | a table | ||||
results$markovCohortTable | a table | ||||
results$markovPlot | Markov state transition diagram | ||||
results$decisionComparisonTable | a table | ||||
results$nmbAnalysis | Net monetary benefit calculation details | ||||
results$icerTable | a table | ||||
results$psaResults | Monte Carlo simulation results | ||||
results$ceacPlot | CEAC showing probability of cost-effectiveness | ||||
results$scatterPlot | Cost-effectiveness plane scatter plot | ||||
results$executiveSummary | Executive summary of analysis results and key findings | ||||
results$glossary | Comprehensive glossary of decision analysis terminology |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$summaryTable$asDF
as.data.frame(results$summaryTable)
Details
This module provides comprehensive decision tree visualization capabilities including:
Decision nodes (square), chance nodes (circle), and terminal nodes (triangle)
Cost-effectiveness analysis with expected value calculations
Sensitivity analysis with tornado diagrams
Multiple layout options and customizable visualization