Bayesian semiparametric mixed effects models for meta‐analysis of the literature data : An application to cadmium toxicity studies

2021 ◽  
Author(s):  
Seongil Jo ◽  
Beomjo Park ◽  
Yeonseung Chung ◽  
Jeongseon Kim ◽  
Eunji Lee ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Cadmium Toxicity ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Toxicity Studies ◽  
Bayesian Semiparametric

scholarly journals The influence of study characteristics on coordinate-based fMRI meta-analyses

2017 ◽  
Author(s):  
Han Bossier ◽  
Ruth Seurinck ◽  
Simone Kühn ◽  
Tobias Banaschewski ◽  
Gareth J. Barker ◽  
...  
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Group Analysis ◽  
Likelihood Estimation ◽  
Ordinary Least Squares ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Activation Likelihood Estimation ◽  
Meta Analyses

AbstractGiven the increasing amount of neuroimaging studies, there is a growing need to summarize published results. Coordinate-based meta-analyses use the locations of statistically significant local maxima with possibly the associated effect sizes to aggregate studies. In this paper, we investigate the influence of key characteristics of a coordinate-based meta-analysis on (1) the balance between false and true positives and (2) the reliability of the outcome from a coordinate-based meta-analysis. More particularly, we consider the influence of the chosen group level model at the study level (fixed effects, ordinary least squares or mixed effects models), the type of coordinate-based meta-analysis (Activation Likelihood Estimation, fixed effects and random effects meta-analysis) and the amount of studies included in the analysis (10, 20 or 35). To do this, we apply a resampling scheme on a large dataset (N = 1400) to create a test condition and compare this with an independent evaluation condition. The test condition corresponds to subsampling participants into studies and combine these using meta-analyses. The evaluation condition corresponds to a high-powered group analysis. We observe the best performance when using mixed effects models in individual studies combined with a random effects meta-analysis. This effect increases with the number of studies included in the meta-analysis. We also show that the popular Activation Likelihood Estimation procedure is a valid alternative, though the results depend on the chosen threshold for significance. Furthermore, this method requires at least 20 to 35 studies. Finally, we discuss the differences, interpretations and limitations of our results.

scholarly journals Meta-Analysis of Fixed, Random and Mixed Effects Models

2019 ◽  
Vol 4 (1) ◽  
pp. 199-218
Author(s):  
Savita Jain ◽  
Suresh K. Sharma ◽  
Kanchan Jain
Keyword(s):  
Meta Analysis ◽  
The Elderly ◽  
Mixed Effects ◽  
Age Related Macular Degeneration ◽  
Mixed Effects Models ◽  
Data Sets ◽  
Mixed Effect ◽  
Severe Visual Loss ◽  
Age Related ◽  
Genetic And Environmental Factors

A statistical procedure used for integrating the results obtained from a number of findings is termed as Meta-analysis. In a systematic review, all the information collected can be best used by increasing the power of analysis. The precision of treatment effects can be improved and accessed by statistically combining the results of similar studies. A very common degenerative disease among the elderly population with multiple genetic and environmental factors is Age-Related Macular Degeneration (AMD) that leads to the distorted central vision and severe visual loss in the early and advanced stage, respectively. In this study, the data sets from the results of 20 studies on the effectiveness of the treatments against AMD disease were analysed using fixed, random and mixed effect models. Various plots Forest, Funnel, Radial, QQ were also fitted for fixed, random and mixed effects models.

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