The composite likelihood (CL) is amongst the computational methods used for estimation of the generalized linear mixed model (GLMM) in the context of bivariate meta-analysis of diagnostic test accuracy studies. Its advantage is that the likelihood can be derived conveniently under the assumption of independence between the random effects, but there has not been a clear analysis of the merit or necessity of this method. For synthesis of diagnostic test accuracy studies, a copula mixed model has been proposed in the biostatistics literature. This general model includes the GLMM as a special case and can also allow for flexible dependence modelling, different from assuming simple linear correlation structures, normality and tail independence i...
This study outlines the development of a new method (split component synthesis; SCS) for meta-analys...
A composite likelihood is usually constructed by multiplying a collection of lower dimensional margi...
Composite likelihoods are a class of alternatives to the full likelihood which may be used for infer...
As meta-analysis of multiple diagnostic tests impacts clinical decision making and patient health, t...
For a particular disease, there may be two diagnostic tests developed, where each of the tests is su...
The current statistical procedures implemented in statistical software packages for pooling of diagn...
A composite likelihood consists of a combination of valid likelihood objects, and in particular it i...
Composite likelihood inference has gained much popularity thanks to its computational manageability ...
In this article, we present an overview and tutorial of statistical methods for meta-analysis of dia...
Objectives: The two main objectives of this research are (1) to compare several different models use...
The composite likelihood (CL) is amongst the computational methods used for the estimation of high-d...
A recent paper proposed an extended trivariate generalized linear mixed model (TGLMM) for synthesis ...
Bivariate random effect models are currently one of the main methods recommended to synthesize diagn...
Multivariate meta-analysis of test accuracy studies when tests are evaluated in terms of sensitivity...
A composite likelihood is a non-genuine likelihood function that allows to make inference on limite...
This study outlines the development of a new method (split component synthesis; SCS) for meta-analys...
A composite likelihood is usually constructed by multiplying a collection of lower dimensional margi...
Composite likelihoods are a class of alternatives to the full likelihood which may be used for infer...
As meta-analysis of multiple diagnostic tests impacts clinical decision making and patient health, t...
For a particular disease, there may be two diagnostic tests developed, where each of the tests is su...
The current statistical procedures implemented in statistical software packages for pooling of diagn...
A composite likelihood consists of a combination of valid likelihood objects, and in particular it i...
Composite likelihood inference has gained much popularity thanks to its computational manageability ...
In this article, we present an overview and tutorial of statistical methods for meta-analysis of dia...
Objectives: The two main objectives of this research are (1) to compare several different models use...
The composite likelihood (CL) is amongst the computational methods used for the estimation of high-d...
A recent paper proposed an extended trivariate generalized linear mixed model (TGLMM) for synthesis ...
Bivariate random effect models are currently one of the main methods recommended to synthesize diagn...
Multivariate meta-analysis of test accuracy studies when tests are evaluated in terms of sensitivity...
A composite likelihood is a non-genuine likelihood function that allows to make inference on limite...
This study outlines the development of a new method (split component synthesis; SCS) for meta-analys...
A composite likelihood is usually constructed by multiplying a collection of lower dimensional margi...
Composite likelihoods are a class of alternatives to the full likelihood which may be used for infer...