CopulaREMADA (version 1.7.3)

rimperfect.trivariateVineCopulaREMADA: Simulation from trivariate 1-truncated D-vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard

Description

Simulation from trivariate 1-truncated D-vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard

Usage

rimperfect.trivariateVineCopulaREMADA.norm(N,p,si,taus,select.random,qcond1,
tau2par1,qcond2,tau2par2)
rimperfect.trivariateVineCopulaREMADA.beta(N,p,g,taus,select.random,qcond1,
tau2par1,qcond2,tau2par2)

Value

Simulated data with 4 columns and \(N\) rows.

y11

the number of the test results where the index test outcome is positive and the reference test outcome is positive

y10

the number of the test results where the index test outcome is positive and the reference test outcome is negative

y01

the number of the test results where the index test outcome is negative and the reference test outcome is positive

y00

the number of the test results where the index test outcome is negative and the reference test outcome is negative

Arguments

N

sample size

p

Vector \((\pi_{1},\pi_{2},\pi_{3},\pi_{4},\pi_{5})\), where \(\pi_1\) is the meta-analytic parameter for the prevalence, \(\pi_2\) and \(\pi_3\) are the meta-analytic parameters for the sensitivity of the index and the reference test, respectively, and \(\pi_4\) and \(\pi_5\) are the meta-analytic parameters for the specificity of the index and the reference test, respectively.

si

Vector \((\sigma_{1},\sigma_{2},\sigma_{3})\), where \(\sigma_t,\,t=1,\ldots,3\) denote the between-study heterogeneities (normal margins)

g

Vector \((\gamma_{1},\gamma_{2},\gamma_{3})\) where\(\gamma_t,\,t=1,\ldots,3\) denote the between-study heterogeneities (beta margins)

taus

Kendall's tau values

select.random

vector \((t_{1},t_{2},t_3)\), where \(1\leq t_1<t_2<t_3\leq 5\)

qcond1

function for the inverse of conditional copula cdf for the \((t_{1},t_{2})\) bivariate margin; choices are qcondbvn, qcondfrk, qcondcln, qcondcln90, qcondcln180 and qcondcln270

tau2par1

function for maping Kendall's tau to copula parameter for the \((t_{1},t_{2})\) bivariate margin; choices are tau2par.bvn, tau2par.frk, tau2par.cln, tau2par.cln90, tau2par.cln180 and tau2par.cln270

qcond2

function for the inverse of conditional copula cdf for the \((t_{2},t_{3})\) bivariate margin; choices are qcondbvn, qcondfrk, qcondcln, qcondcln90, qcondcln180 and qcondcln270

tau2par2

function for maping Kendall's tau to copula parameter for the \((t_{2},t_{3})\) bivariate margin; choices are tau2par.bvn, tau2par.frk, tau2par.cln, tau2par.cln90, tau2par.cln180 and tau2par.cln270

References

Nikoloulopoulos, A.K. (2024) Vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard. Submitted.

Examples

N=59
p=c(0.631,0.653,0.902,0.843,0.987)
si=c(1.513,1.341,1.341)
taus=c(0.3,-0.3)
select.random=c(1,2,4)

out=rimperfect.trivariateVineCopulaREMADA.norm(N,p,si,taus,select.random,
qcondcln180,tau2par.cln180,qcondcln270,tau2par.cln270)

g=c(0.290,0.244,0.190)
out=rimperfect.trivariateVineCopulaREMADA.beta(N,p,g,taus,select.random,
qcondcln180,tau2par.cln180,qcondcln270,tau2par.cln270)