RJafroc (version 2.1.2)

StSignificanceTesting: Performs DBM or OR significance testing for factorial or split-plot A,C datasets

Description

Performs Dorfman-Berbaum-Metz (DBM) or Obuchowski-Rockette (OR) significance testing, for specified dataset; significance testing refers to analysis designed to assign a P-value, and other statistics, for rejecting the null hypothesis (NH) that the reader-averaged figure of merit (FOM) differences between treatments is zero. The results of the analysis are best visualized in the text or Excel-formatted files produced by UtilOutputReport.

Usage

StSignificanceTesting(
  dataset,
  FOM,
  FPFValue = 0.2,
  alpha = 0.05,
  method = "DBM",
  covEstMethod = "jackknife",
  nBoots = 200,
  analysisOption = "ALL",
  tempOrgCode = FALSE
)

Value

For method = "DBM" the returned list contains 4 dataframes:

FOMs

Contains foms, trtMeans and trtMeanDiffs: see return of UtilFigureOfMerit

ANOVA

Contains TRCAnova, VarCom, IndividualTrt and IndividualRdr ANOVA tables of pseudovalues

RRRC

Contains results of "RRRC" analyses: FTests, ciDiffTrt, ciAvgRdrEachTrt

FRRC

Contains results of "FRRC" analyses: FTests, ciDiffTrt, ciAvgRdrEachTrt, ciDiffTrtEachRdr

RRFC

Contains results of "RRFC" analyses: FTests, ciDiffTrt, ciAvgRdrEachTrt

For method = "OR" the return list contains 4 dataframes:

FOMs

Contains foms, trtMeans and trtMeanDiffs: UtilFigureOfMerit

ANOVA

Contains TRAnova, VarCom, IndividualTrt and IndividualRdr ANOVA tables of FOM values

RRRC

Contains results of "RRRC" analyses - same organization as DBM, see above

FRRC

Contains results of "FRRC" analyses - ditto

RRFC

Contains results of "RRFC" analyses- ditto

Arguments

dataset

The dataset to be analyzed, see RJafroc-package. Must have two or more treatments and two or more readers. The dataset design can be "FCTRL", "SPLIT-PLOT-A" or "SPLIT-PLOT-C".

FOM

The figure of merit, see UtilFigureOfMerit

FPFValue

Only needed for LROC data and FOM = "PCL" or "ALROC"; where to evaluate a partial curve based figure of merit. The default is 0.2.

alpha

The significance level of the test of the null hypothesis that all treatment effects are zero; the default is 0.05

method

The significance testing method to be used: "DBM" (the default), representing the Dorfman-Berbaum-Metz method or "OR", representing the Obuchowski-Rockette method. and the Obuchowski-Rockette significance testing methods, respectively.

covEstMethod

The covariance matrix estimation method in ORH analysis (for method = "DBM" the jackknife is always used).

• "Jackknife", the default,

• "Bootstrap", in which case nBoots (above) is relevant,

• "DeLong"; requires FOM = "Wilcoxon" or "ROI" or "HrAuc", otherwise an error results.

nBoots

The number of bootstraps (defaults to 200), relevant only if covEstMethod = "bootstrap" and method = "OR"

analysisOption

Determines which factors are regarded as random vs. fixed:

• "RRRC" = random-reader random case,

• "FRRC" = fixed-reader random case,

• "RRFC" = random-reader fixed case,

• "ALL" = outputs results of "RRRC", "FRRC" and "RRFC" analyses - this is the default.

tempOrgCode,

default FALSE; if TRUE, then code from version 0.0.1 of RJafroc is used (see RJafroc_0.0.1.tar). This is intended to check against errors that crept in subsequent to the original version as I attempted to improve the organization of the code and the output. As implicit in the name of this temporary flag, it will eventually be removed.

References

Dorfman DD, Berbaum KS, Metz CE (1992) ROC characteristic rating analysis: Generalization to the Population of Readers and Patients with the Jackknife method, Invest. Radiol. 27, 723-731.

Obuchowski NA, Rockette HE (1995) Hypothesis Testing of the Diagnostic Accuracy for Multiple Diagnostic Tests: An ANOVA Approach with Dependent Observations, Communications in Statistics: Simulation and Computation 24, 285-308.

Hillis SL (2014) A marginal-mean ANOVA approach for analyzing multireader multicase radiological imaging data, Statistics in medicine 33, 330-360.

Chakraborty DP (2017) Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples, CRC Press, Boca Raton, FL. https://www.routledge.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840

Examples

StSignificanceTesting(dataset02,FOM = "Wilcoxon", method = "DBM") 
StSignificanceTesting(dataset02,FOM = "Wilcoxon", method = "OR")
## following is split-plot-c analysis using a simulated split-plot-c dataset
StSignificanceTesting(datasetFROCSpC, FOM = "wAFROC", method = "OR")

# \donttest{
StSignificanceTesting(dataset05, FOM = "wAFROC")
StSignificanceTesting(dataset05, FOM = "HrAuc", method = "DBM") 
StSignificanceTesting(dataset05, FOM = "SongA1", method = "DBM") 
StSignificanceTesting(dataset05, FOM = "SongA2", method = "DBM") 
StSignificanceTesting(dataset05, FOM = "wAFROC1", method = "DBM")
StSignificanceTesting(dataset05, FOM = "AFROC1", method = "DBM")
StSignificanceTesting(dataset05, FOM = "AFROC", method = "DBM")
# }