optimalThreshold

Bayesian Methods for Optimal Threshold Estimation

Functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) <doi:10.1177/0962280218821394> for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) <doi:10.1002/bimj.200900242> for diagnostic markers.

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Description file content

Package
optimalThreshold
Type
Package
Title
Bayesian Methods for Optimal Threshold Estimation
Version
1.0
SystemRequirements
JAGS 4.x.y
Depends
R (>= 3.1.2)
Date
2020-01-09
Description
Functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) for diagnostic markers.
License
GPL (>= 2.0)
Collate
Package.R import_package.R scaledT.R ClassUnions.R ClassFitNormalDist.R ClassFitLogNormalDist.R ClassFitGammaDist.R ClassFitStudentDist.R ClassFitLogisticDist.R ClassFitUserDefinedDist.R ClassUndefined.R ClassNormalDist.R ClassLogNormalDist.R ClassGammaDist.R ClassStudentDist.R ClassLogisticDist.R ClassUnionsDist.R ClassCompoundDist.R ClassTrtSelOptThresh.R ClassTrtSelRelUtility.R ClassDiagOptThresh.R ClassDiagRelUtility.R cdf.R gradient.R hessian.R ARS.R samplePosteriorDist.R global.R
RoxygenNote
6.1.1
Imports
ars, rjags, HDInterval, mgcv, utils, coda, grDevices, methods, stats, graphics
NeedsCompilation
no
Packaged
2020-01-09 21:51:17 UTC; yblan
Author
Yoann Blangero [aut, cre]
Maintainer
Yoann Blangero
Repository
CRAN
Date/Publication
2020-01-13 16:10:05 UTC

install.packages('optimalThreshold')

1.0

11 days ago

Yoann Blangero

GPL (>= 2.0)

Depends on

R (>= 3.1.2)

Imports

ars, rjags, HDInterval, mgcv, utils, coda, grDevices, methods, stats, graphics

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