Pass-Through Rates for Alcohol Beverage Excise Taxes: Fixed-Effect versus Random-Effects Meta-Analysis and Meta-Regressions

Abstract Motivation Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect (FE) meta-analysis methods. However, the power to detect genetic associations under FE models deteriorates with increasing heterogeneity, so that some small-effect heterogeneous loci might go undetected. A modified random-effects meta-analysis approach (RE2) was previously developed that is more powerful than traditional fixed and random-effects methods at detecting small-effect heterogeneous genetic associations, the method was updated (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here, we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a FE meta-analysis. Results Our application of RE2C suggests a high sensitivity but low specificity of this approach for discovering small-effect heterogeneous genetic associations. We recommend that reports of small-effect heterogeneous loci discovered with RE2C are accompanied by forest plots and standardized predicted random-effects statistics to reveal the distribution of genetic effect estimates across component studies of meta-analyses, highlighting overly influential outlier studies with the potential to inflate genetic signals. Availability and implementation Scripts to calculate standardized predicted random-effects statistics and generate forest plots are available in the getspres R package entitled from https://magosil86.github.io/getspres/. Supplementary information Supplementary data are available at Bioinformatics online.

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A basic introduction to fixed-effect and random-effects models for meta-analysis

Research Synthesis Methods ◽

10.1002/jrsm.12 ◽

2010 ◽

Vol 1 (2) ◽

pp. 97-111 ◽

Cited By ~ 1612

Author(s):

Michael Borenstein ◽

Larry V. Hedges ◽

Julian P.T. Higgins ◽

Hannah R. Rothstein

Keyword(s):

Random Effects ◽

Meta Analysis ◽

Fixed Effect ◽

Random Effects Models

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Genetic Variants Modifying the Risk of Lung Cancer and Its Subtypes: A Comprehensive Meta-analysis and a Case-control Study

10.21203/rs.3.rs-127817/v1 ◽

2020 ◽

Author(s):

Debmalya Sengupta ◽

Souradeep Banerjee ◽

Ritabrata Mitra ◽

Tamohan Chaudhuri ◽

Abhijit Sarkar ◽

...

Keyword(s):

Lung Cancer ◽

Random Effects ◽

Case Control Study ◽

Association Studies ◽

Meta Analysis ◽

Case Control ◽

Effect Modification ◽

Fixed Effect ◽

Significant Heterogeneity ◽

Control Study

Abstract Association studies on lung cancer have often yielded conflicting and inconclusive results. We performed a comprehensive meta-analysis to dissect the precise effects of the candidate variants. We searched for association studies on lung cancer from the Indian subcontinent. Cochran’s Q-test assessed heterogeneity. Both overall and histotype-stratified meta-analysis was done using fixed-effect and random-effects models. Smoking status stratified subgroup analysis and effect modification tests were done. An associated variant with significant heterogeneity was genotyped in an eastern Indian population to investigate the contribution of potential confounders followed by a comprehensive meta-analysis across world populations. Significant heterogeneity was observed for the 8 variants. Both fixed-effect and random-effects meta-analysis of 24 variants showed FDR-corrected associations of rs3547/XRCC1 and rs1048943/CYP1A1 with lung cancer along with 5 nominal associations. del1/GSTT1, rs4646903/CYP1A1, and rs10488943/CYP1A1 were associated with adenocarcinoma, squamous cell carcinoma, and both, respectively. rs4646903/CYP1A1 was associated with lung cancer among smokers with significant effect modification by smoking. rs10488943/CYP1A1 was associated with lung adenocarcinoma in the East Indian case-control study. rs1048943/CYP1A1 was associated with lung cancer across world populations. Our work confirms the risk loci for lung cancer and its subtypes in the context of smoking and other aetiological factors, which could aid in personalised treatment.

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

Evidence-Based Mental Health ◽

10.1136/eb-2014-101794 ◽

2014 ◽

Vol 17 (2) ◽

pp. 64-64 ◽

Cited By ~ 22

Author(s):

Adriani Nikolakopoulou ◽

Dimitris Mavridis ◽

Georgia Salanti

Keyword(s):

Random Effects ◽

Meta Analysis ◽

Fixed Effect ◽

Meta Analyses ◽

Analysis Models

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A Primer on Bayesian Model-Averaged Meta-Analysis

10.31234/osf.io/97qup ◽

2020 ◽

Cited By ~ 1

Author(s):

Quentin Frederik Gronau ◽

Daniel W. Heck ◽

Sophie Wilhelmina Berkhout ◽

Julia M. Haaf ◽

Eric-Jan Wagenmakers

Keyword(s):

Random Effects ◽

Null Hypothesis ◽

Bayesian Model ◽

Alternative Hypothesis ◽

Meta Analysis ◽

Fixed Effect ◽

Random Effects Model ◽

Psychological Science ◽

Analysis Models ◽

Key Questions

Meta-analysis is the predominant approach for quantitatively synthesizing a set of studies. If the studies themselves are of high quality, meta-analysis can provide valuable insights into the current scientific state of knowledge about a particular phenomenon. In psychological science, the most common approach is to conduct frequentist meta-analysis. In this primer, we discuss an alternative method, Bayesian model-averaged meta-analysis. This procedure combines the results of four Bayesian meta-analysis models: (1) fixed-effect null hypothesis, (2) fixed-effect alternative hypothesis, (3) random-effects null hypothesis, and (4) random-effects alternative hypothesis. These models are combined according to their plausibilities in light of the observed data to address the two key questions "Is the overall effect non-zero?" and "Is there between-study variability in effect size?". Bayesian model-averaged meta-analysis therefore avoids the need to select either a fixed-effect or random-effects model and instead takes into account model uncertainty in a principled manner.

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The Diamond Ratio: A Visual Indicator of the Extent of Heterogeneity in Meta-Analysis

10.31234/osf.io/f4xus ◽

2020 ◽

Author(s):

Maxwell Cairns ◽

Geoff Cumming ◽

Robert Calin-Jageman ◽

Luke A. Prendergast

Keyword(s):

Confidence Interval ◽

Random Effects ◽

Meta Analysis ◽

Fixed Effect ◽

Forest Plot ◽

Summary Measure ◽

First Time

The result of a meta-analysis is conventionally pictured in the forest plot as a diamond, whose length is the 95% confidence interval (CI) for the summary measure of interest. The Diamond Ratio (DR) is the ratio of the length of the diamond given by a random effects meta-analysis to that given by a fixed effect meta-analysis. The DR is a simple visual indicator of the extent of heterogeneity in the meta-analysis, where increasing values of DR greater than 1.0 indicate increasing heterogeneity. We investigate the properties of the DR, and its relationship to four conventional but more complex measures of heterogeneity. We propose for the first time a CI on the DR, and show that it performs well in terms of coverage. We provide example code to calculate the DR and its CI, and to show these in a forest plot. We conclude that the DR is a useful indicator that can assist students and researchers to understand heterogeneity, and to appreciate its extent in particular cases.

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Churras y merinas. Metanálisis (I).

Revista Electrónica AnestesiaR ◽

10.30445/rear.v10i10.685 ◽

2018 ◽

Vol 10 (10) ◽

pp. 5

Author(s):

Manuel Molina

Keyword(s):

Statistical Methods ◽

Random Effects ◽

Meta Analysis ◽

Fixed Effect Model ◽

Fixed Effect ◽

Random Effects Model ◽

Effect Model ◽

Global Result

El metanálisis es una técnica que permite obtener un resultado resumen a partir de varios estudios individuales. Esto solo puede hacer tras comprobar que los estudios se parecen lo suficiente como para poder combinarse, lo cual se hará con métodos estadísticos específicos, siendo los más usados el modelo de efecto fijo y el modelo de efectos aleatorios. ABSTRACT Meta-analysis is a technique that allows obtaining a global result from several individual studies. This can only be done after checking that the studies are similar enough to be combined, which will be done with specific statistical methods, the most used being the fixed effect model and the random effects model.

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Metaanalyse

Norsk Epidemiologi ◽

10.5324/nje.v23i2.1636 ◽

2013 ◽

Vol 23 (2) ◽

Cited By ~ 1

Author(s):

Geir Smedslund

Keyword(s):

Random Effects ◽

Effect Size ◽

Meta Analysis ◽

Large Body ◽

Fixed Effect Model ◽

English Summary ◽

Fixed Effect ◽

Effect Estimate ◽

Study Results ◽

Meta Analyses

Metaanalyse er en kvantitativ metode for å oppsummere resultatene av flere enkeltstudier. I en metaanalyse forsøker man å tallfeste behandlingseffekten, og man gir store studier større vekt enn små studier. En mye brukt metode for å vekte er invers variansmetoden. Dersom alle studiene har målt resultatene på samme måte kan resultatene brukes direkte i metaanalysen, men dersom det samme utfallet er målt på ulike måter, må man bruke standardiserte effektstørrelser hvor alle resultatene er omregnet til en felles skala. Dersom man tror at effekten av behandlingen vil være lik for alle, bortsett fra tilfeldige variasjoner, benytter man en fixed-effect modell. Tror man derimot at det vil være systematiske forskjeller i effekt når behandlingen gis i ulike kontekster, legges dette inn i en såkalt random-effects modell. Metaanalyser blir ofte fremstilt grafisk i form av forest plots. Hver linje representerer da én studie, med effektestimatet markert som et punkt, mens ytterpunktene av linjen representerer konfidensintervallet. Metaanalysen blir fremstilt som en diamant hvor bredden viser usikkerheten i estimatet. Dersom resultatene fra alle studiene trekker i samme retning er metaanalysen ”homogen”. Men dersom studiene spriker når det gjelder effektstørrelse og retning på effekt, er det ”heterogenitet”. Styrken ved metaanalyse er at den kan sammenfatte en stor mengde informasjon i ett tall. Samtidig er dette også svakheten ved metoden. Et enkelt tall kan ikke beskrive variasjonen på tvers av flere studier.Smedslund G. Meta-analysis. Nor J Epidemiol 2013; 23 (2): 147-149.ENGLISH SUMMARY Meta-analysis is a quantitative method for summarizing single studies. In a meta-analysis, one tries to quantify the treatment effect, assigning more weight to large studies than to small studies. A much used method for weighting is the inverse variance method. If all studies have measured the results in the same way, the results can be used directly in the meta-analysis, but if the same outcome is measured in different ways across different studies, one has to use a standardized effect size where results are converted to a common scale. If it is believed that the effect is consistent across various populations and settings, one can employ a fixed-effect model. If systematic differences in effect can be expected, a random-effects model is used. Meta-analyses are often depicted as forest plots. Each line represents one study where the effect estimate is marked as a point on a line, with each end of the line representing the confidence interval around it. The meta-analysis is shown as a diamond where the width illustrates the uncertainty around the estimate. If all study results point in the same direction, the meta-analysis is considered “homogeneous”. But if the studies vary in their effect size and direction, the findings are “heterogeneous”. The strength of meta-analysis is that it can be used to summarize a large body of information in one number. This is also its limitation. One number cannot describe the variation that exists across different studies.

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Meta-analysis: How to quantify and explain heterogeneity?

European Journal of Cardiovascular Nursing ◽

10.1177/1474515120944014 ◽

2020 ◽

Vol 19 (7) ◽

pp. 646-652

Author(s):

Todd Ruppar

Keyword(s):

Random Effects ◽

Systematic Reviews ◽

Allied Health ◽

Meta Analysis ◽

Fixed Effect ◽

Analysis Model ◽

The Common ◽

Meta Analyses

The number of systematic reviews and meta-analyses submitted to nursing and allied health journals continues to grow. Well-conducted and reported syntheses of research are valuable to advancing science. One of the common critiques identified in these manuscripts involves how the authors addressed heterogeneity among the studies in their meta-analyses. Methodologically inappropriate approaches regarding heterogeneity introduce error and bias into analyses and may lead to incorrect findings and conclusions. This article will discuss some of the approaches to take as well as avoid when addressing heterogeneity in meta-analyses, including suggestions for how to choose a fixed-effect or random-effects meta-analysis model and steps to follow to address heterogeneity in meta-analysis results.

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