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Package ‘TGST’

Yizhen Xu, Tao Liu

2020-11-20

Type Package

Title Targeted Gold Standard Testing

Version 1.0

Date “2020-11-20”

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

####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 (), 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. \[g_1(s) = exp(\beta_0^*+\beta_1*s)*g_0(s)\]

####Usage

Check.exp.tilt( Z, S)

####Arguments

####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 (), 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

####Value

Cross validated results on false classification rates (FNR, FPR), \(\lambda-\) risk, total misclassification rate and AUC.

####Author(s)

Yizhen Xu (), 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

####Value

Optimal tripartite rule.

####Author(s)

Yizhen Xu (), 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

####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

####Value

Matrix with 2 columns. Each row is a possible tripartite rule, with output on lower and upper cutoff.

####Author(s)

Yizhen Xu (), 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

####Value

Matrix with 2 columns. Each row is a set of nonparametric (FNR, FPR) on an associated tripartite rule.

####Author(s)

Yizhen Xu (), 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

####Value

Matrix with 2 columns. Each row is a set of semiparametric (FNR, FPR) on an associated tripartite rule.

####Author(s)

Yizhen Xu (), 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

####Value

AUC.

####Author(s)

Yizhen Xu (), 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 (), 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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.