SROC: Summary receiver operating characteristic curves for copula...

In CopulaREMADA: Copula Mixed Models for Multivariate Meta-Analysis of Diagnostic Test Accuracy Studies

Summary receiver operating characteristic curves for copula mixed effect models for bivariate meta-analysis of diagnostic test accuracy studies

Description

Summary receiver operating characteristic (SROC) curves are demonstrated for the proposed models through quantile regression techniques and different characterizations of the estimated bivariate random effects distribution

Usage

SROC.norm(param,dcop,qcondcop,tau2par,TP,FN,FP,TN,
          points=TRUE,curves=TRUE,
          NEP=rep(0,length(TP)),NEN=rep(0,length(TP)))
SROC.beta(param,dcop,qcondcop,tau2par,TP,FN,FP,TN,
          points=TRUE,curves=TRUE,
          NEP=rep(0,length(TP)),NEN=rep(0,length(TP)))
SROC(param.beta,param.normal,TP,FN,FP,TN,
          NEP=rep(0,length(TP)),NEN=rep(0,length(TP)))

Arguments

param	A vector with the sensitivities, specifities, variabilities and Kendall's tau value (the latter only for SROC.norm and SROC.beta)
param.beta	A vector with the sensitivity, specifity and variabilities of the countermonotonic CopulaREMADA with beta margins
param.normal	A vector with the sensitivity, specifity and variabilities of the countermonotonic CopulaREMADA with normal margins
dcop	function for copula density
qcondcop	function for the inverse of conditional copula cdf
tau2par	function for maping Kendall's tau to copula parameter
TP	the number of true positives
FN	the number of false negatives
FP	the number of false positives
TN	the number of true negatives
points	logical: print individual studies
curves	logical: print quantile regression curves
NEP	the number of non-evaluable positives in the presence of non-evaluable subjects
NEN	the number of non-evaluable negatives in the presence of non-evaluable subjects

Value

Summary receiver operating characteristic curves

References

Nikoloulopoulos, A.K. (2015) A mixed effect model for bivariate meta-analysis of diagnostic test accuracy studies using a copula representation of the random effects distribution. Statistics in Medicine, 34, 3842–3865. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.6595")}.

See Also

CopulaREMADA rCopulaREMADA

Examples

nq=15
gl=gauss.quad.prob(nq,"uniform")
mgrid<- meshgrid(gl$n,gl$n)

data(telomerase) 
attach(telomerase)
est.n=countermonotonicCopulaREMADA.norm(TP,FN,FP,TN,gl,mgrid)
est.b=countermonotonicCopulaREMADA.beta(TP,FN,FP,TN,gl,mgrid)
SROC(est.b$e,est.n$e,TP,FN,FP,TN)
detach(telomerase)

data(LAG)
attach(LAG)
c180est.b=CopulaREMADA.beta(TP,FN,FP,TN,gl,mgrid,qcondcln180,tau2par.cln180)
SROC.beta(c180est.b$e,dcln180,qcondcln180,tau2par.cln180,TP,FN,FP,TN)
detach(LAG)

data(MRI)
attach(MRI)
c270est.n=CopulaREMADA.norm(TP,FN,FP,TN,gl,mgrid,qcondcln270,tau2par.cln270)
SROC.norm(c270est.n$e,dcln270,qcondcln270,tau2par.cln270,TP,FN,FP,TN)
detach(MRI)

data(MK2016)
attach(MK2016)
p=c(0.898745016,0.766105342,0.059168715,0.109217888)
g=c(0.090270947,0.079469009,0.367463579,0.154976269)
taus=c(0.82050793,-0.51867629,0.26457961)
SROC.beta(c(p[1:2],g[1:2],taus[1]),
          dcln180,qcondcln180,tau2par.cln180,
          TP,FN,FP,TN,points=TRUE,curves=TRUE,NEP,NEN)
detach(MK2016)

CopulaREMADA documentation built on Oct. 18, 2024, 1:08 a.m.