rVineCopulaREMADA: Simulation from trivariate vine copula mixed models for...

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

Simulation from trivariate vine copula mixed models for diagnostic test accuaracy studies accounting for disease prevalence and non-evaluable results

Description

Simulation from trivariate vine copula mixed models for diagnostic test accuaracy studies accounting for disease prevalence and non-evaluable results

Usage

rVineCopulaREMADA.beta(N,p,g,taus,omega1,omega0,qcondcop12,qcondcop13,
qcondcop23,tau2par12,tau2par13,tau2par23)
rVineCopulaREMADA.norm(N,p,si,taus,omega1,omega0,qcondcop12,qcondcop13,
qcondcop23,tau2par12,tau2par13,tau2par23)

Arguments

N	sample size
p	Vector (\pi_1,\pi_2,\pi_3) of sensitivity/specificity/prevalence
si	Vector (\sigma_1,\sigma_2,\sigma_3) of variability; normal margins
g	Vector (\gamma_1,\gamma_2,\gamma_3) of variability; beta margins
taus	Kendall's tau values
omega1	the probability for non-evaluable positives
omega0	the probability for non-evaluable negatives
qcondcop12	function for the inverse of conditional copula cdf at the (1,2) bivariate margin
qcondcop13	function for the inverse of conditional copula cdf at the (1,3) bivariate margin
qcondcop23	function for the inverse of conditional copula cdf at the (2,3|1) bivariate margin
tau2par12	function for maping Kendall's tau at the (1,2) bivariate margin to copula parameter
tau2par13	function for maping Kendall's tau at the (1,3) bivariate margin to copula parameter
tau2par23	function for maping Kendall's tau at the (2,3|1) bivariate margin to the conditional copula parameter

Value

Simuated data with 6 columns and N rows.

TP

the number of true positives

FN

the number of false negatives

FP

the number of false positives

TN

the number of true negatives

NEP

the number of non-evaluable positives

NEN

the number of non-evaluable negatives

References

Nikoloulopoulos, A.K. (2017) A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence. Statistical Methods in Medical Research, 26, 2270–2286. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0962280215596769")}.

Nikoloulopoulos, A.K. (2018) A vine copula mixed model for trivariate meta-analysis of diagnostic studies accounting for disease prevalence and non-evaluable subjects. ArXiv e-prints, arXiv:1812.03685. https://arxiv.org/abs/1812.03685.

See Also

rCopulaREMADA rcop cvinesim

Examples

p=c(0.8,0.7,0.4)
g=c(0.1,0.1,0.05)
taus=c(-0.5,-0.3,-0.0001)
qcondcop12=qcondcop23=qcondcop13=qcondcln90
tau2par12=tau2par23=tau2par13=tau2par.cln90
# in the absence of non-evaluable results
omega1=0
omega0=0
rVineCopulaREMADA.beta(50,p,g,taus,omega1,omega0,
qcondcop12,qcondcop13,qcondcop23,tau2par12,
tau2par13,tau2par23)
# in the presence of non-evaluable results
omega1=0.1
omega0=0.2
rVineCopulaREMADA.beta(50,p,g,taus,omega1,omega0,
qcondcop12,qcondcop13,qcondcop23,tau2par12,
tau2par13,tau2par23)

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