BayesianFROC: FROC Analysis by Bayesian Approaches

Provides new methods for the so-called Free-response Receiver Operating Characteristic (FROC) analysis. The ultimate aim of FROC analysis is to compare observer performances, which means comparing characteristics, such as area under the curve (AUC) or figure of merit (FOM). In this package, we only use the notion of AUC for modality comparison, where by "modality", we mean imaging methods such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET),...,etc. So there is a problem that which imaging method is better to detect lesions from shadows in radiographs. To solve modality comparison issues, this package provides new methods using hierarchical Bayesian models proposed by the author of this package. Using this package, one can obtain at least one conclusion that which imaging methods are better for finding lesions in radiographs with the case of your data. Fitting FROC statistical models is sometimes not so good, it can easily confirm by drawing FROC curves and comparing these curves and the points constructed by False Positive fractions (FPFs) and True Positive Fractions (TPFs), we can validate the goodness of fit intuitively. Such validation is also implemented by the Chi square goodness of fit statistics in the Bayesian context which means that the parameter is not deterministic, thus by integrating it with the posterior predictive measure, we get a desired value. To compare modalities (imaging methods: MRI,CT,PET,...,etc), we evaluate AUCs for each modality. FROC is developed by Dev Chakraborty, his FROC model in his 1989 paper relies on the maximal likelihood methodology. The author modified and provided the alternative Bayesian FROC model. Strictly speaking, his model does not coincide with models in this package. In FROC context, we means by multiple reader and multiple case (MRMC) the case of the number of reader or modality is two or more. The MRMC data is available for functions of this package. I hope that medical researchers use not only the frequentist method but also alternative Bayesian methods. In medical research, many problems are considered under only frequentist methods, such as the notion of p-values. But p-value is sometimes misunderstood. Bayesian methods provide very simple, direct, intuitive answer for research questions. Combining frequentist methods with Bayesian methods, we can obtain more reliable answer for research questions. Please execute the following R scripts from the R (R studio) console, demo(demo_MRMC, package = "BayesianFROC"); demo(demo_srsc, package = "BayesianFROC"); demo(demo_stan, package = "BayesianFROC"); demo(demo_drawcurves_srsc, package = "BayesianFROC"); demo_Bayesian_FROC(); demo_Bayesian_FROC_without_pause(). References: Dev Chakraborty (1989) <doi:10.1118/1.596358> Maximum likelihood analysis of free - response receiver operating characteristic (FROC) data. Pre-print: Issei Tsunoda; Bayesian Models for free-response receiver operating characteristic analysis. See the vignettes for more details.

Version: 0.1.2
Depends: rstan (≥ 2.18.2), R (≥ 3.5.0), Rcpp
Imports: knitr, readxl, xlsx, stats, graphics, tcltk, grDevices, ggplot2, methods, car, crayon, DiagrammeR
Suggests: rmarkdown, rstantools, openxlsx, hexbin, MASS
Published: 2019-05-28
Author: Issei Tsunoda [aut, cre]
Maintainer: Issei Tsunoda <tsunoda.issei1111 at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: BayesianFROC results

Downloads:

Reference manual: BayesianFROC.pdf
Vignettes: Appendix:Notations and details
FROC_task_and_resulting_FROC_data
How_FROC_data_arise
Multiple readers and Multiple Modality (MRMC) case
Model_described_in_stan_file______Model_Hiera_TargetFormulation
Single reader and Single modality
memo
Theory of FROC for Bayesian context on a single reader and single modality
Theory_second_edition
Theory for thresholds
Validation for a Single Reader and Single Modality
Validation for a Single Reader and Single Modality case via p value of Bayesian sense
Package source: BayesianFROC_0.1.2.tar.gz
Windows binaries: r-devel: BayesianFROC_0.1.2.zip, r-release: BayesianFROC_0.1.2.zip, r-oldrel: BayesianFROC_0.1.2.zip
OS X binaries: r-release: BayesianFROC_0.1.2.tgz, r-oldrel: BayesianFROC_0.1.2.tgz
Old sources: BayesianFROC archive

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