rimperfect.trivariateVineCopulaREMADA: Simulation from trivariate 1-truncated D-vine copula mixed...

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

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)

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

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

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)

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