In yizhenxu/TVLT: Targeted Gold Standard Testing
Type Package
Title Targeted Gold Standard Testing
Version 1.0
Date "r Sys.Date()"
Authors Yizhen Xu, Tao Liu
Maintainer Yizhen (yizhen_xu@alumni.brown.edu)
Description This package implements the optimal allocation of gold standard testing under constrained availability.
License GPL
URL https://github.com/yizhenxu/TGST
Depends R (>= 3.2.0)
LazyData true
TGST
Create a TGST Object
Description
Create a TGST object, usually used as an input for optimal rule search and ROC analysis.
Usage
TGST( Z, S, phi, method="nonpar")
Arguments
- Z A vector of true disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score.
- phi Percentage of patients taking gold standard test.
- method Method for searching for the optimal tripartite rule, options are "nonpar" (default) and "semipar".
Value
An object of class TGST.The class contains 6 slots: phi (percentage of gold standard tests), Z (true failure status), S (risk score), Rules (all possible tripartite rules), Nonparametric (logical indicator of the approach), and FNR.FPR (misclassification rates).
Author(s)
Yizhen Xu (yizhen_xu@brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
TGST( Z, S, phi, method="nonpar")
Check.exp.tilt
Check exponential tilt model assumption
Description
This function provides graphical assessment to the suitability of the exponential tilt model for risk score in finding optimal tripartite rules by semiparametric approach.
$$g_1(s) = exp(\beta_0^+\beta_1s)*g_0(s)$$
Usage
Check.exp.tilt( Z, S)
Arguments
- Z True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score.
Value
Returns the plot of empirical density for risk score S, joint empirical density for (S,Z=1) and (S,Z=0), and the density under the exponential tilt model assumption for (S,Z=1) and (S,Z=0).
Author(s)
Yizhen Xu (yizhen_xu@alumni.brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
Check.exp.tilt( Z, S)
CV.TGST
Cross Validation
Description
This function allows you to compute the average of misdiagnoses rate for viral failure and the optimal risk under min $\lambda$ rules from K-fold cross-validation.
Usage
CV.TGST(Obj, lambda, K=10)
Arguments
- Obj An object of class TGST.
- lambda A user-specified weight that reflects relative loss for the two types of misdiagnoses, taking value in [0,1].
$Loss=\lambdaI(FN)+(1-\lambda)I(FP)$.
- K Number of folds in cross validation. The default is 10.
Value
Cross validated results on false classification rates (FNR, FPR), $\lambda-$ risk, total misclassification rate and AUC.
Author(s)
Yizhen Xu (yizhen_xu@alumni.brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
data = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
Obj = TVLT(Z, S, phi, method="nonpar")
CV.TGST(Obj, lambda, K=10)
OptimalRule
Optimal Tripartite Rule
Description
This function gives you the optimal tripartite rule that minimizes the min-$\lambda$ risk based on the type of user selected approach. It takes the risk score and true disease status from a training data set and returns the optimal tripartite rule under the specified proportion of patients able to take gold standard test.
Usage
OptimalRule(Obj, lambda)
Arguments
- Z
- Obj An object of class TGST.
- lambda A user-specified weight that reflects relative loss for the two types of misdiagnoses, taking value in [0,1]. $Loss=\lambdaI(FN)+(1-\lambda)I(FP)$.
Value
Optimal tripartite rule.
Author(s)
Yizhen Xu (yizhen_xu@alumni.brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
lambda = 0.5
Obj = TGST(Z, S, phi, method="nonpar")
OptimalRule(Obj, lambda)
ROCAnalysis
ROC Analysis
Description
This function performs ROC analysis for tripartite rules. If 'plot=TRUE', the ROC curve is returned.
Usage
ROCAnalysis(Obj, plot=TRUE)
Arguments
- Obj An object of class TGST.
- plot Logical parameter indicating if ROC curve should be plotted. Default is 'plot=TRUE'. If false, then only AUC is calculated.
Value
AUC (the area under ROC curve) and ROC curve.
Author(s)
Yizhen Xu (yizhen_xu@alumni.brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
lambda = 0.5
Obj = TGST(Z, S, phi, method="nonpar")
ROCAnalysis(Obj, plot=TRUE)
nonpar.rules
Nonparametric Rules Set
Description
This function gives you all possible cutoffs [l,u] for tripartite rules, by applying nonparametric search to the given data.
$$P(S \in [l,u]) \le \phi$$
Usage
nonpar.rules( Z, S, phi)
Arguments
- Z True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score.
- phi Percentage of patients taking viral load test.
Value
Matrix with 2 columns. Each row is a possible tripartite rule, with output on lower and upper cutoff.
Author(s)
Yizhen Xu (yizhen_xu@brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10\% of patients taking viral load test
nonpar.rules( Z, S, phi)
nonpar.fnr.fpr
Nonparametric FNR FPR of the rules
Description
This function gives you the nonparametric FNRs and FPRs associated with a given set of tripartite rules.
Usage
nonpar.fnr.fpr(Z,S,rules[1,1],rules[1,2])
Arguments
- Z True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score.
- l Lower cutoff of tripartite rule.
- u Upper cutoff of tripartite rule.
Value
Matrix with 2 columns. Each row is a set of nonparametric (FNR, FPR) on an associated tripartite rule.
Author(s)
Yizhen Xu (yizhen_xu@brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10\% of patients taking viral load test
rules = nonpar.rules( Z, S, phi)
nonpar.fnr.fpr(Z,S,rules[1,1],rules[1,2])
semipar.fnr.fpr
Semiparametric FNR FPR of the rules
Description
This function gives you the semiparametric FNR and FPR associated with a set of given tripartite rules.
Usage
semipar.fnr.fpr(Z,S,rules[1,1],rules[1,2])
Arguments
- Z True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score.
- l Lower cutoff of tripartite rule.
- u Upper cutoff of tripartite rule.
Value
Matrix with 2 columns. Each row is a set of semiparametric (FNR, FPR) on an associated tripartite rule.
Author(s)
Yizhen Xu (yizhen_xu@brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10\% of patients taking viral load test
rules = nonpar.rules( Z, S, phi)
semipar.fnr.fpr(Z,S,rules[1,1],rules[1,2])
cal.AUC
Calculate AUC
Description
This function gives you the AUC associated with the rules set.
Usage
cal.AUC(Z,S,rules[,1],rules[,2])
Arguments
- Z True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score.
- l Lower cutoff of tripartite rule.
- u Upper cutoff of tripartite rule.
Value
AUC.
Author(s)
Yizhen Xu (yizhen_xu@brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata
Z = d$Z # True Disease Status
S = d$S # Risk Score
phi = 0.1 #10% of patients taking viral load test
rules = nonpar.rules( Z, S, phi)
cal.AUC(Z,S,rules[,1],rules[,2])
Simdata
Simulated data for package illustration
Description
A simulated dataset containing true disease status and risk score. See details for simulation setting.
Format
A data frame with 8000 simulated observations on the following 2 variables.
- Z True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score. Higher risk score indicates larger tendency of diseased / treatment failure.
Details
We first simulate true failure status $Z$ assuming $Z\sim Bernoulli(p)$ with $p=0.25$; and then conditional on $Z$, simulate ${S|Z=z}=ceiling(W)$ with $W\sim Gamma(\eta_z,\kappa_z)$ where $\eta$ and $\kappa$ are shape and scale parameters.$(\eta_0,\kappa_0)=(2.3,80)$ and $(\eta_1,\kappa_1)=(9.2,62)$.
Author(s)
Yizhen Xu (yizhen_xu@brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
data(Simdata)
summary(Simdata)
plot(Simdata)
yizhenxu/TVLT documentation built on Nov. 27, 2020, 2:37 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Type Package
Title Targeted Gold Standard Testing
Version 1.0
Date "r Sys.Date()"
Authors Yizhen Xu, Tao Liu
Maintainer Yizhen (yizhen_xu@alumni.brown.edu)
Description This package implements the optimal allocation of gold standard testing under constrained availability.
License GPL
URL https://github.com/yizhenxu/TGST
Depends R (>= 3.2.0)
LazyData true
TGST
Create a TGST Object
Description
Create a TGST object, usually used as an input for optimal rule search and ROC analysis.
Usage
TGST( Z, S, phi, method="nonpar")
Arguments
- Z A vector of true disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score.
- phi Percentage of patients taking gold standard test.
- method Method for searching for the optimal tripartite rule, options are "nonpar" (default) and "semipar".
Value
An object of class TGST.The class contains 6 slots: phi (percentage of gold standard tests), Z (true failure status), S (risk score), Rules (all possible tripartite rules), Nonparametric (logical indicator of the approach), and FNR.FPR (misclassification rates).
Author(s)
Yizhen Xu (yizhen_xu@brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata Z = d$Z # True Disease Status S = d$S # Risk Score phi = 0.1 #10% of patients taking viral load test TGST( Z, S, phi, method="nonpar")
Check.exp.tilt
Check exponential tilt model assumption
Description
This function provides graphical assessment to the suitability of the exponential tilt model for risk score in finding optimal tripartite rules by semiparametric approach. $$g_1(s) = exp(\beta_0^+\beta_1s)*g_0(s)$$
Usage
Check.exp.tilt( Z, S)
Arguments
- Z True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score.
Value
Returns the plot of empirical density for risk score S, joint empirical density for (S,Z=1) and (S,Z=0), and the density under the exponential tilt model assumption for (S,Z=1) and (S,Z=0).
Author(s)
Yizhen Xu (yizhen_xu@alumni.brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata Z = d$Z # True Disease Status S = d$S # Risk Score Check.exp.tilt( Z, S)
CV.TGST
Cross Validation
Description
This function allows you to compute the average of misdiagnoses rate for viral failure and the optimal risk under min $\lambda$ rules from K-fold cross-validation.
Usage
CV.TGST(Obj, lambda, K=10)
Arguments
- Obj An object of class TGST.
- lambda A user-specified weight that reflects relative loss for the two types of misdiagnoses, taking value in [0,1]. $Loss=\lambdaI(FN)+(1-\lambda)I(FP)$.
- K Number of folds in cross validation. The default is 10.
Value
Cross validated results on false classification rates (FNR, FPR), $\lambda-$ risk, total misclassification rate and AUC.
Author(s)
Yizhen Xu (yizhen_xu@alumni.brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
data = Simdata Z = d$Z # True Disease Status S = d$S # Risk Score phi = 0.1 #10% of patients taking viral load test Obj = TVLT(Z, S, phi, method="nonpar") CV.TGST(Obj, lambda, K=10)
OptimalRule
Optimal Tripartite Rule
Description
This function gives you the optimal tripartite rule that minimizes the min-$\lambda$ risk based on the type of user selected approach. It takes the risk score and true disease status from a training data set and returns the optimal tripartite rule under the specified proportion of patients able to take gold standard test.
Usage
OptimalRule(Obj, lambda)
Arguments
- Z
- Obj An object of class TGST.
- lambda A user-specified weight that reflects relative loss for the two types of misdiagnoses, taking value in [0,1]. $Loss=\lambdaI(FN)+(1-\lambda)I(FP)$.
Value
Optimal tripartite rule.
Author(s)
Yizhen Xu (yizhen_xu@alumni.brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata Z = d$Z # True Disease Status S = d$S # Risk Score phi = 0.1 #10% of patients taking viral load test lambda = 0.5 Obj = TGST(Z, S, phi, method="nonpar") OptimalRule(Obj, lambda)
ROCAnalysis
ROC Analysis
Description
This function performs ROC analysis for tripartite rules. If 'plot=TRUE', the ROC curve is returned.
Usage
ROCAnalysis(Obj, plot=TRUE)
Arguments
- Obj An object of class TGST.
- plot Logical parameter indicating if ROC curve should be plotted. Default is 'plot=TRUE'. If false, then only AUC is calculated.
Value
AUC (the area under ROC curve) and ROC curve.
Author(s)
Yizhen Xu (yizhen_xu@alumni.brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata Z = d$Z # True Disease Status S = d$S # Risk Score phi = 0.1 #10% of patients taking viral load test lambda = 0.5 Obj = TGST(Z, S, phi, method="nonpar") ROCAnalysis(Obj, plot=TRUE)
nonpar.rules
Nonparametric Rules Set
Description
This function gives you all possible cutoffs [l,u] for tripartite rules, by applying nonparametric search to the given data. $$P(S \in [l,u]) \le \phi$$
Usage
nonpar.rules( Z, S, phi)
Arguments
- Z True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score.
- phi Percentage of patients taking viral load test.
Value
Matrix with 2 columns. Each row is a possible tripartite rule, with output on lower and upper cutoff.
Author(s)
Yizhen Xu (yizhen_xu@brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata Z = d$Z # True Disease Status S = d$S # Risk Score phi = 0.1 #10\% of patients taking viral load test nonpar.rules( Z, S, phi)
nonpar.fnr.fpr
Nonparametric FNR FPR of the rules
Description
This function gives you the nonparametric FNRs and FPRs associated with a given set of tripartite rules.
Usage
nonpar.fnr.fpr(Z,S,rules[1,1],rules[1,2])
Arguments
- Z True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score.
- l Lower cutoff of tripartite rule.
- u Upper cutoff of tripartite rule.
Value
Matrix with 2 columns. Each row is a set of nonparametric (FNR, FPR) on an associated tripartite rule.
Author(s)
Yizhen Xu (yizhen_xu@brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata Z = d$Z # True Disease Status S = d$S # Risk Score phi = 0.1 #10\% of patients taking viral load test rules = nonpar.rules( Z, S, phi) nonpar.fnr.fpr(Z,S,rules[1,1],rules[1,2])
semipar.fnr.fpr
Semiparametric FNR FPR of the rules
Description
This function gives you the semiparametric FNR and FPR associated with a set of given tripartite rules.
Usage
semipar.fnr.fpr(Z,S,rules[1,1],rules[1,2])
Arguments
- Z True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score.
- l Lower cutoff of tripartite rule.
- u Upper cutoff of tripartite rule.
Value
Matrix with 2 columns. Each row is a set of semiparametric (FNR, FPR) on an associated tripartite rule.
Author(s)
Yizhen Xu (yizhen_xu@brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata Z = d$Z # True Disease Status S = d$S # Risk Score phi = 0.1 #10\% of patients taking viral load test rules = nonpar.rules( Z, S, phi) semipar.fnr.fpr(Z,S,rules[1,1],rules[1,2])
cal.AUC
Calculate AUC
Description
This function gives you the AUC associated with the rules set.
Usage
cal.AUC(Z,S,rules[,1],rules[,2])
Arguments
- Z True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1).
- S Risk score.
- l Lower cutoff of tripartite rule.
- u Upper cutoff of tripartite rule.
Value
AUC.
Author(s)
Yizhen Xu (yizhen_xu@brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
d = Simdata Z = d$Z # True Disease Status S = d$S # Risk Score phi = 0.1 #10% of patients taking viral load test rules = nonpar.rules( Z, S, phi) cal.AUC(Z,S,rules[,1],rules[,2])
Simdata
Simulated data for package illustration
Description
A simulated dataset containing true disease status and risk score. See details for simulation setting.
Format
A data frame with 8000 simulated observations on the following 2 variables. - Z True disease status (No disease / treatment success coded as Z=0, diseased / treatment failure coded as Z=1). - S Risk score. Higher risk score indicates larger tendency of diseased / treatment failure.
Details
We first simulate true failure status $Z$ assuming $Z\sim Bernoulli(p)$ with $p=0.25$; and then conditional on $Z$, simulate ${S|Z=z}=ceiling(W)$ with $W\sim Gamma(\eta_z,\kappa_z)$ where $\eta$ and $\kappa$ are shape and scale parameters.$(\eta_0,\kappa_0)=(2.3,80)$ and $(\eta_1,\kappa_1)=(9.2,62)$.
Author(s)
Yizhen Xu (yizhen_xu@brown.edu), Tao Liu, Joseph Hogan
References
T. Liu, J. Hogan, L. Wang, S. Zhang, R. Kantor (2013) Journal of the American Statistical Association Vol.108, No.504
Examples
data(Simdata) summary(Simdata) plot(Simdata)
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