Meta-Analysis of Meta-Analyses in Communication: Comparing Fixed Effects and Random Effects Analysis Models

2010 ◽  
Vol 58 (3) ◽  
pp. 257-278 ◽  
Author(s):  
Ashley Anker ◽  
Amber Marie Reinhart ◽  
Thomas Hugh Feeley
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Meta Analyses ◽  
Analysis Models

scholarly journals Prognostic Role of Matrix Metalloproteinases in Cervical Cancer: A Meta-Analysis

2021 ◽  
Vol 28 ◽  
pp. 107327482110337
Author(s):  
Weiwei Chen ◽  
Shenjiao Huang ◽  
Kun Shi ◽  
Lisha Yi ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Matrix Metalloproteinases ◽  
Random Effects ◽  
Fixed Effects ◽  
Prognostic Value ◽  
Meta Analysis ◽  
Free Survival ◽  
Random Effects Models ◽  
Registration Number ◽  
Meta Analyses

Objective Studies have published the association between the expression of matrix metalloproteinases (MMPs) and the outcome of cervical cancer. However, the prognostic value in cervical cancer remains controversial. This meta-analysis was conducted to evaluate the prognostic functions of MMP expression in cervical cancer. Methods A comprehensive search of PubMed, Embase, and Web of Science databases was conducted to identify the eligible studies according to defined selection and excluding criteria and analyzed according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Fixed and random effects models were evaluated through the hazard ratios (HRs) and 95% confidence intervals (CIs) to estimate the overall survival (OS), recurrence-free survival (RFS), and progress-free survival (PFS). Results A total of 18 eligible studies including 1967 patients were analyzed for prognostic value. Totally 16 selected studies including 21 tests were relevant to the cervical cancer OS, 4 studies focused on RFS, and 1 study on PFS. The combined pooled HRs and 95% CIs of OS were calculated with random-effects models (HR = 1.64, 95% CI = 1.01–2.65, P = .000). In the subgroup analysis for OS, there was no heterogeneity in MMP-2 (I2 = .0%, P = .880), MMP-1 (I2 = .0%, P = .587), and MMP-14 (I2 = 28.3%, P = .248). In MMP-7 and MMP-9, the heterogeneities were obvious (I2 = 99.2% ( P = .000) and I2 = 77.9% ( P = .000), respectively). The pooled HRs and 95% CIs of RFS were calculated with fixed-effects models (HR = 2.22, 95% CI = 1.38–3.58, P = .001) and PFS (HR = 2.29, 95% CI = 1.14–4.58, P = .035). Conclusions The results indicated that MMP overexpression was associated with shorter OS and RFS in cervical cancer patients. It suggested that MMP overexpression might be a poor prognostic marker in cervical cancer. Research Registry Registration Number: reviewregistry 1159.

How to interpret meta-analysis models: fixed effect and random effects meta-analyses

2014 ◽  
Vol 17 (2) ◽  
pp. 64-64 ◽  
Author(s):  
Adriani Nikolakopoulou ◽  
Dimitris Mavridis ◽  
Georgia Salanti
Keyword(s):  
Random Effects ◽  
Meta Analysis ◽  
Fixed Effect ◽  
Meta Analyses ◽  
Analysis Models

scholarly journals metamicrobiomeR: an R package for analysis of microbiome relative abundance data using zero-inflated beta GAMLSS and meta-analysis across studies using random effect models

2018 ◽  
Author(s):  
Nhan Thi Ho ◽  
Fan Li
Keyword(s):  
Random Effects ◽  
High Throughput ◽  
Relative Abundance ◽  
High Throughput Sequencing ◽  
Meta Analysis ◽  
R Package ◽  
Standard Errors ◽  
Relative Abundances ◽  
Meta Analyses ◽  
Analysis Models

ABSTRACTBackgroundThe rapid growth of high-throughput sequencing-based microbiome profiling has yielded tremendous insights into human health and physiology. Data generated from high-throughput sequencing of 16S rRNA gene amplicons are often preprocessed into composition or relative abundance. However, reproducibility has been lacking due to the myriad of different experimental and computational approaches taken in these studies. Microbiome studies may report varying results on the same topic, therefore, meta-analyses examining different microbiome studies to provide robust results are important. So far, there is still a lack of implemented methods to properly examine differential relative abundances of microbial taxonomies and to perform meta-analysis examining the heterogeneity and overall effects across microbiome studies.ResultsWe developed an R package ‘metamicrobiomeR’ that applies Generalized Additive Models for Location, Scale and Shape (GAMLSS) with a zero-inflated beta (BEZI) family (GAMLSS-BEZI) for analysis of microbiome relative abundance datasets. Both simulation studies and application to real microbiome data demonstrate that GAMLSS-BEZI well performs in testing differential relative abundances of microbial taxonomies. Importantly, the estimates from GAMLSS-BEZI are log(odds ratio) of relative abundances between groups and thus are comparable between microbiome studies. As such, we also apply random effects meta-analysis models to pool estimates and their standard errors across microbiome studies. We demonstrate the meta-analysis workflow and highlight the utility of our package on four studies comparing gut microbiomes between male and female infants in the first six months of life.ConclusionsGAMLSS-BEZI allows proper examination of microbiome relative abundance data. Random effects meta-analysis models can be directly applied to pool comparable estimates and their standard errors to evaluate the heterogeneity and overall effects across microbiome studies. The examples and workflow using our metamicrobiomeR package are reproducible and applicable for the analyses and meta-analyses of other microbiome studies.

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 Smoking prevalence among hospitalized COVID-19 patients and its association with disease severity and mortality: an expanded re-analysis of a recent publication

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Konstantinos Farsalinos ◽  
Pantelis G. Bagos ◽  
Theodoros Giannouchos ◽  
Raymond Niaura ◽  
Anastasia Barbouni ◽  
...  
Keyword(s):  
Disease Severity ◽  
Random Effects ◽  
Fixed Effects ◽  
Smoking Prevalence ◽  
Meta Analysis ◽  
Original Study ◽  
Hospitalized Patients ◽  
Prevalence Odds Ratio ◽  
Meta Analyses ◽  
Low Prevalence

Abstract Background There is a lot of debate about the effects of smoking on COVID-19. A recent fixed-effects meta-analysis found smoking to be associated with disease severity among hospitalized patients, but other studies report an unusually low prevalence of smoking among hospitalized patients. The purpose of this study was to expand the analysis by calculating the prevalence odds ratio (POR) of smoking among hospitalized COVID-19 patients, while the association between smoking and disease severity and mortality was examined by random-effects meta-analyses considering the highly heterogeneous study populations. Methods The same studies as examined in the previous meta-analysis were analyzed (N = 22, 20 studies from China and 2 from USA). The POR relative to the expected smoking prevalence was calculated using gender and age-adjusted population smoking rates. Random-effects meta-analyses were used for all other associations. Results A total of 7162 patients were included, with 482 being smokers. The POR was 0.24 (95%CI 0.19–0.30). Unlike the original study, the association between smoking and disease severity was not statistically significant using random-effects meta-analysis (OR 1.40, 95%CI 0.98–1.98). In agreement with the original study, no statistically significant association was found between smoking and mortality (OR 1.86, 95%CI 0.88–3.94). Conclusion An unusually low prevalence of smoking, approximately 1/4th the expected prevalence, was observed among hospitalized COVID-19 patients. Any association between smoking and COVID-19 severity cannot be generalized but should refer to the seemingly low proportion of smokers who develop severe COVID-19 that requires hospitalization. Smokers should be advised to quit due to long-term health risks, but pharmaceutical nicotine or other nicotinic cholinergic agonists should be explored as potential therapeutic options, based on a recently presented hypothesis.

scholarly journals Applied Comparison of Meta-analysis Techniques

10.36469/9848 ◽  
2013 ◽  
Vol 1 (1) ◽  
pp. 14-22
Author(s):  
Li Wang ◽  
Colin Lewis-Beck ◽  
Elyse Fritschel ◽  
Erdem Baser ◽  
Onur Baser
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Fixed Effects Model ◽  
Analysis Techniques ◽  
Level Data ◽  
Study Results ◽  
Meta Regression ◽  
Treatment Comparisons ◽  
Analysis Models

Background: Meta-analysis is an approach that combines findings from similar studies. The aggregation of study level data can provide precise estimates for outcomes of interest, allow for unique treatment comparisons, and explain the differences arising from conflicting study results. Proper meta-analysis includes five basic steps: identify relevant studies; extract summary data from each paper; compute study effect sizes, perform statistical analysis; and interpret and report the results. Objectives: This study aims to review meta-analysis methods and their assumptions, apply various meta-techniques to empirical data, and compare the results from each method. Methods: Three different meta-analysis techniques were applied to a dataset looking at the effects of the bacille Calmette-Guerin (BCG) vaccine on tuberculosis (TB). First, a fixed-effects model was applied; then a random-effects model; and third meta-regression with study-level covariates were added to the model. Overall and stratified results, by geographic latitude were reported. Results: All three techniques showed a statistically significant effects from the vaccination. However, once covariates were added, efficacy diminished. Independent variables, such as the latitude of the location in which the study was performed, appeared to be partially driving the results. Conclusions: Meta-analysis is useful for drawing general conclusions from a variety of studies. However, proper study and model selection are important to ensure the correct interpretation of results. Basic meta-analysis models are fixed-effects, random-effects, and meta-regression.

Meta-analysis

2010 ◽  
pp. 447-475
Author(s):  
Janet L. Peacock ◽  
Philip J. Peacock
Keyword(s):  
Publication Bias ◽  
Random Effects ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Patient Data ◽  
Meta Analyses ◽  
Combining Estimates

Meta-analysis: introduction 448 Searching for studies 450 Combining estimates in meta-analyses 452 Heterogeneity 454 Overcoming heterogeneity 456 Fixed effects estimates 458 Random effects estimates 460 Presenting meta-analyses 462 Publication bias 464 Detecting publication bias 466 Adjusting for publication bias 468 Independent patient data meta-analysis 472...

An Exact Test for Meta-analysis with Binary Endpoints

2007 ◽  
Vol 46 (06) ◽  
pp. 662-668 ◽  
Author(s):  
C. Gromann ◽  
O. Kuss
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Artery Bypass ◽  
Sparse Data ◽  
Random Effects Model ◽  
Exact Test ◽  
Standard Methods ◽  
Meta Analyses ◽  
Empirical Size

Summary Objectives : We reintroduce an exact Mantel-Haenszel (MH) procedure for meta-analysis with binary endpoints which is expected to workespeciallywell i sparse data, e.g., in meta-analyses of safety or adverse events. Methods : The performance of the exact MH procedure in terms of empirical size and power is compared to the asymptotic MH and to the two standard procedures (fixed effects and random effects model) in a simulation study. We illustrate the methods with a metaanalysis of postoperative stroke occurrence after offpump or on-pump surgery in coronary artery bypass grafting. Results : We find that in almost all situations the asymptotic MH procedure outperforms its competitors; especially the standard methods yield poor results in terms of power and size. Conclusions : There is no need to use the reintroduced exact MH procedure; the asymptotic MH procedure will be sufficient in most practical situations. The standard methods (fixed effects and random effects model) should not be used in the sparse data situation.

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