Performs Dorfman-Berbaum-Metz (DBM) or Obuchowski-Rockette (OR)
significance testing with Hillis' improvements, 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) difference between treatments is zero. The results of
the analysis are better visualized in the text or
Excel-formatted files produced by UtilOutputReport.
StSignificanceTesting(dataset, FOM = "Wilcoxon", alpha = 0.05,
method = "DBMH", covEstMethod = "Jackknife", nBoots = 200,
option = "ALL", VarCompFlag = FALSE, FPFValue = 0.2)The dataset to be analyzed, see RJafroc-package
The figure of merit, default "Wilcoxon", UtilFigureOfMerit
The significance level of the test of the null hypothesis that all treatment effects are zero; the default is 0.05
The significance testing method to be used. There are two options:
"DBMH" (the default) or "ORH", representing the Dorfman-Berbaum-Metz
and the Obuchowski-Rockette significance testing methods, respectively.
This parameter is not relevant if
method = "DBMH". Specifies covariance matrix estimation method
in ORH analysis.
"Jackknife", the default,
"Bootstrap", in which case nBoots is relevant
"DeLong"; the last assumes FOM = "Wilcoxon", otherwise
an error results.
The number of bootstraps (defaults to 200), relevant only if
covEstMethod = "Bootstrap" and method = "ORH"
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 the results of "RRRC", "FRRC" and "RRFC" analyses
If TRUE, only the appropriate (DBM or OR) variance components (six in all) are returned, default is FALSE
Only needed for LROC data; where to evaluate a partial curve based figure of merit. The default is 0.2.
For method = "DBMH" the returned value is a list with 22 members:
The figure of merit array for each treatment-reader combination
The ANOVA table of the pseudovalues over all treatments
The ANOVA table of the pseudovalues for each treatment
The variance components of the pseudovalue model underlying the analysis, 6 values, in the following order: c("Var(R)", "Var(C)", "Var(T*R)", "Var(T*C)", "Var(R*C)", "Var(Error)")
For random-reader random-case (RRRC) analysis, the F-statistic for rejecting the null hypothesis of no treatment effect
For RRRC analysis, the denominator degrees of freedom of the F statistic
For RRRC analysis, the p-value of the significance test of the NH
For RRRC analysis, the confidence intervals and related test statistics for the FOM differences between pairs of treatments
For RRRC analysis, the confidence intervals and related test statistics for rdr. avg. FOM in each treatment
For fixed-reader random-case (FRRC) analysis, the F-statistic for rejecting the NH
For FRRC analysis, the denominator degrees of freedom of the F-statistic
For FRRC analysis, the p-value of the significance test of the NH
For FRRC analysis, the confidence intervals and related test statistics for the FOM differences between pairs of treatments
For FRRC analysis, the confidence intervals and related tests for rdr. avg. FOM in each treatment
The sum of squares table of the ANOVA of the pseudovalues for each reader (based on data for the specified reader)
The mean squares table of the ANOVA of the pseudovalues for each reader (based on data for the specified reader)
The confidence intervals and related tests of the FOM differences between pairs of treatments for each reader
For random-reader fixed-case (RRFC) analysis, the F statistic
For RRFC analysis, the denominator degrees of freedom of the F statistic
For RRFC analysis, the p-value for rejecting the NH
For RRFC analysis, the confidence intervals and related test statistics for the FOM differences between pairs of treatments
For RRFC analysis, the confidence intervals and related tests for reader averaged FOM in each treatment
For method = "ORH" the return value is a list with with 21 members:
Figures of merit array. See the return of
UtilFigureOfMerit
Mean square of the figure of merit corresponding to the treatment effect
Mean square of the figure of merit corresponding to the treatment-reader effect
The variance components of the pseudovalue model underlying the analysis, 6 values, in the following order: c("Var(R)", "Var(T*R)", "COV1", "COV2", "COV3", "Var(Error)")
Same as DBMH method
Same as DBMH method
Same as DBMH method
Same as DBMH method
Same as DBMH method
Same as DBMH method
Same as DBMH method
Same as DBMH method
Same as DBMH method
Same as DBMH method
Same as DBMH method
Obuchowski-Rockette Variance and Cov1 estimates for each reader
Same as DBMH method
Same as DBMH method
Same as DBMH method
Same as DBMH method
Same as DBMH method
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.crcpress.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840
# NOT RUN {
StSignificanceTesting(dataset02,FOM = "Wilcoxon", method = "DBMH")
StSignificanceTesting(dataset02,FOM = "Wilcoxon", method = "ORH")
# }
# NOT RUN {
StSignificanceTesting(dataset05, FOM = "wAFROC")
StSignificanceTesting(dataset05, FOM = "HrAuc", method = "DBMH")
StSignificanceTesting(dataset05, FOM = "SongA1", method = "DBMH")
StSignificanceTesting(dataset05, FOM = "SongA2", method = "DBMH")
StSignificanceTesting(dataset05, FOM = "wJAFROC1", method = "DBMH")
StSignificanceTesting(dataset05, FOM = "JAFROC1", method = "DBMH")
StSignificanceTesting(dataset05, FOM = "JAFROC", method = "DBMH")
# }
# NOT RUN {
# }
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