SROC {CopulaREMADA}

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. 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)