Add to ORKGA general framework for meta-analysis based on linear mixed-effects models, where potentially complex patterns of effect sizes are modelled through an extended and flexible structure of fixed and random terms. This definition includes, as special cases, a variety of meta-analytical models that have been separately proposed in the literature, such as multivariate, network, multilevel, dose-response, and longitudinal meta-analysis and meta-regression. This extended meta-analytical framework is illustrated in: "An extended mixed-effects framework for meta-analysis"
Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of ef...
Clustering effect and non-zero intraclass correlation produce the variability that can be observed i...
Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of ef...
Standard methods for meta-analysis are limited to pooling tasks in which a single effect size is est...
Standard methods for meta‐analysis are limited to pooling tasks in which a single effect size is est...
Standard applications of meta-analysis consider a single effect-size estimated from independent stud...
Standard applications of meta-analysis consider a single effect-size estimated from independent stud...
Standard applications of meta-analysis consider a single effect-size estimated from independent stud...
Meta-analysis is both a theory and a toolbox of statistical techniques for combining summary statist...
In the past decade, a new statistical method—network meta-analysis—has been developed to address lim...
A meta-analysis combines and compares quantitative results of a set of studies in which more or less...
Meta-analysis is the joint statistical analysis of results from a number of related studies to obtai...
Meta-analysis and structural equation modeling (SEM) are two important statistical methods in the be...
Model-based meta-analysis (MBMA) is increasingly used in drug development to inform decision making ...
General rights This document is made available in accordance with publisher policies. Please cite on...
Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of ef...
Clustering effect and non-zero intraclass correlation produce the variability that can be observed i...
Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of ef...
Standard methods for meta-analysis are limited to pooling tasks in which a single effect size is est...
Standard methods for meta‐analysis are limited to pooling tasks in which a single effect size is est...
Standard applications of meta-analysis consider a single effect-size estimated from independent stud...
Standard applications of meta-analysis consider a single effect-size estimated from independent stud...
Standard applications of meta-analysis consider a single effect-size estimated from independent stud...
Meta-analysis is both a theory and a toolbox of statistical techniques for combining summary statist...
In the past decade, a new statistical method—network meta-analysis—has been developed to address lim...
A meta-analysis combines and compares quantitative results of a set of studies in which more or less...
Meta-analysis is the joint statistical analysis of results from a number of related studies to obtai...
Meta-analysis and structural equation modeling (SEM) are two important statistical methods in the be...
Model-based meta-analysis (MBMA) is increasingly used in drug development to inform decision making ...
General rights This document is made available in accordance with publisher policies. Please cite on...
Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of ef...
Clustering effect and non-zero intraclass correlation produce the variability that can be observed i...
Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of ef...