imperfect.REMADA: Maximum likelihood estimation of univariate mixed models for...

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

Maximum likelihood estimation of univariate mixed models for meta-analysis of diagnostic accuracy studies without a gold standard

Description

The estimated parameters can be obtained by using a quasi-Newton method applied to the logarithm of the joint likelihood. This numerical method requires only the objective function, i.e., the logarithm of the joint likelihood, while the gradients are computed numerically and the Hessian matrix of the second order derivatives is updated in each iteration. The standard errors (SE) of the ML estimates can be also obtained via the gradients and the Hessian computed numerically during the maximization process.

Usage

imperfectREMADA.norm(y11,y10,y01,y00,gl,select.random,start)
                               
imperfectREMADA.beta(y11,y10,y01,y00,gl,select.random,start)

Arguments

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
gl	a list containing the components of Gauss-Legendre nodes gl$nodes and weights gl$weights
select.random	a scalar from 1 to 5 that indicates the random effect
start	starting values for the parameters

Value

A list containing the following components:

LogLikelihood	the maximized log-likelihood
Estimates	the MLE
SE	the standard errors

References

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

Examples

data(Pap)
attach(Pap)

nq=30
gl=gauss.quad.prob(nq,"uniform")
start=c(rep(0.6,5),0.5)
select.random=1 
est.norm=imperfectREMADA.norm(y11,y10,y01,y00,gl,
select.random,start)
start=c(rep(0.6,5),0.1)
est.beta=imperfectREMADA.beta(y11,y10,y01,y00,gl,
select.random,start)

detach(Pap)

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