Power Analysis for Inter-rater Agreement Studies

Performs power analysis to determine the required sample size for detecting a specified improvement in inter-rater agreement (kappa coefficient). This function helps researchers design adequately powered studies to validate training programs, standardized protocols, or other interventions aimed at improving agreement between raters.

Details

The function calculates the sample size needed to detect a difference between two kappa values (kappa0 vs kappa1) with specified power and significance level. It supports 2-5 outcome categories and 2-5 raters, using the kappaSize package implementation of power calculations for different scenarios.

Key requirements:

• kappa1 must be greater than kappa0 (alternative hypothesis should represent improvement)

• Proportions must sum to 1.0 and match the number of outcome categories

• Power should be at least 0.50, typically 0.80 or higher

Super classes

jmvcore::Analysis -> ClinicoPath::kappaSizePowerBase -> kappaSizePowerClass

Methods

Public methods

• kappaSizePowerClass$clone()

Inherited methods

• jmvcore::Analysis$.createImage()
• jmvcore::Analysis$.createImages()
• jmvcore::Analysis$.createPlotObject()
• jmvcore::Analysis$.load()
• jmvcore::Analysis$.render()
• jmvcore::Analysis$.save()
• jmvcore::Analysis$.savePart()
• jmvcore::Analysis$.setCheckpoint()
• jmvcore::Analysis$.setParent()
• jmvcore::Analysis$.setReadDatasetHeaderSource()
• jmvcore::Analysis$.setReadDatasetSource()
• jmvcore::Analysis$.setResourcesPathSource()
• jmvcore::Analysis$.setStatePathSource()
• jmvcore::Analysis$addAddon()
• jmvcore::Analysis$asProtoBuf()
• jmvcore::Analysis$asSource()
• jmvcore::Analysis$check()
• jmvcore::Analysis$init()
• jmvcore::Analysis$optionsChangedHandler()
• jmvcore::Analysis$postInit()
• jmvcore::Analysis$print()
• jmvcore::Analysis$readDataset()
• jmvcore::Analysis$run()
• jmvcore::Analysis$serialize()
• jmvcore::Analysis$setError()
• jmvcore::Analysis$setStatus()
• jmvcore::Analysis$translate()
• ClinicoPath::kappaSizePowerBase$initialize()

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Method clone()

The objects of this class are cloneable with this method.

Usage

kappaSizePowerClass$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # \dontrun{
# Basic binary outcome power analysis
result <- kappaSizePower(
  outcome = "2",
  kappa0 = 0.40,           # Current agreement level
  kappa1 = 0.60,           # Target agreement level
  props = "0.30, 0.70",    # Expected proportions
  raters = "2",            # Number of raters
  alpha = 0.05,            # Significance level
  power = 0.80             # Desired power
)

# Medical diagnosis validation study
result <- kappaSizePower(
  outcome = "2",
  kappa0 = 0.50,           # Baseline fair agreement
  kappa1 = 0.75,           # Target good agreement
  props = "0.25, 0.75",    # 25% disease prevalence
  raters = "2",
  alpha = 0.05,
  power = 0.80
)

# Multi-category severity assessment
result <- kappaSizePower(
  outcome = "3",
  kappa0 = 0.55,           # Current moderate agreement
  kappa1 = 0.75,           # Target good agreement
  props = "0.20, 0.50, 0.30", # Mild, moderate, severe
  raters = "3",
  alpha = 0.05,
  power = 0.85
)
} # }