CopulaREMADA (version 1.7.3)

rmultinom6dVineCopulaREMADA: Simulation from multinomial six-variate 1-truncated D-vine copula mixed models for meta-analysis of two diagnostic tests accounting for within and between studies dependence

Description

Simulation from multinomial six-variate 1-truncated D-vine copula mixed models for meta-analysis of two diagnostic tests accounting for within and between studies dependence

Usage

rmultinom6dVineCopulaREMADA.norm(N,p,si,taus,qcond,tau2par)
rmultinom6dVineCopulaREMADA.beta(N,p,g,taus,qcond,tau2par)

Value

Simulated data with 8 columns and \(N\) rows.

y001

the number of the test results in the diseased where the test 1 outcome is negative and the test 2 outcome is negative

y011

the number of the test results in the diseased where the test 1 outcome is negative and the test 2 outcome is positive

y101

the number of the test results in the diseased where the test 1 outcome is positive and the test 2 outcome is negative

y111

the number of the test results in the diseased where the test 1 outcome is positive and the test 2 outcome is positive

y000

the number of the test results in the non-diseased where the test 1 outcome is negative and the test 2 outcome is negative

y010

the number of the test results in the non-diseased where the test 1 outcome is negative and the test 2 outcome is positive

y100

the number of the test results in the non-diseased where the test 1 outcome is positive and the test 2 outcome is negative

y110

the number of the test results in the non-diseased where the test 1 outcome is positive and the test 2 outcome is positive

Arguments

N

sample size

p

Vector \((\pi_{101},\pi_{011},\pi_{111},\pi_{100},\pi_{010},\pi_{110})\) of the meta-analytic parameters of interest for each combination of test results in diseased and non-diseased participants in a \(4\times 2\) table

si

Vector \((\sigma_{101},\sigma_{011},\sigma_{111},\sigma_{100},\sigma_{010},\sigma_{110})\) of variability parameters; normal margins

g

Vector \((\gamma_{101},\gamma_{011},\gamma_{111},\gamma_{100},\gamma_{010},\gamma_{110})\) of variability parameters; beta margins

taus

Kendall's tau values

qcond

function for the inverse conditional copula cdf

tau2par

function for maping Kendall's taus to copula parameters

References

Nikoloulopoulos, A.K. (2024) Joint meta-analysis of two diagnostic tests accounting for within and between studies dependence. Statistical Methods in Medical Research. tools:::Rd_expr_doi("10.1177/09622802241269645")

See Also

dvine6dsim

Examples

N=11
p=c(0.03667409,  0.09299767,  0.29450436,  0.01733081,  0.04923809,  0.02984361)
si=c(1.69868880, 0.54292079,  0.58489574,  0.92918177,  0.48998484,  0.57004098)
taus=c(-0.52475006,  0.55768873, 0.18454559,  0.02233204,  0.57570506)


tau2par=tau2par.bvn
qcond=qcondbvn

out=rmultinom6dVineCopulaREMADA.norm(N,p,si,taus,qcond,tau2par)
 
  
y101=out[,1]
y011=out[,2]
y111=out[,3]
y001=out[,4]
y100=out[,5]
y010=out[,6]
y110=out[,7]
y000=out[,8]