scholarly journals Intervention meta-analysis: application and practice using R software

2019 ◽  
Vol 41 ◽  
pp. e2019008 ◽  
Author(s):  
Sung Ryul Shim ◽  
Seong-Jang Kim
Keyword(s):  
Publication Bias ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Forest Plot ◽  
R Software ◽  
Data Synthesis ◽  
Moderator Effect ◽  
Theoretical Concepts ◽  
Analysis Application ◽  
Meta Regression Analysis

The objective of this study was to describe general approaches for intervention meta-analysis available for quantitative data synthesis using the R software. We conducted an intervention meta-analysis using two types of data, continuous and binary, characterized by mean difference and odds ratio, respectively. The package commands for the R software were “metacont”, “metabin”, and “metagen” for the overall effect size, “forest” for forest plot, “metareg” for meta-regression analysis, and “funnel” and “metabias” for the publication bias. The estimated overall effect sizes, test for heterogeneity and moderator effect, and the publication bias were reported using the R software. In particular, the authors indicated methods for calculating the effect sizes of the target studies in intervention meta-analysis. This study focused on the practical methods of intervention meta-analysis, rather than the theoretical concepts, for researchers with no major in statistics. Through this study, the authors hope that many researchers will use the R software to more readily perform the intervention meta-analysis and that this will in turn generate further related research.

scholarly journals Network meta-analysis: application and practice using R software

2019 ◽  
Vol 41 ◽  
pp. e2019013 ◽  
Author(s):  
Sung Ryul Shim ◽  
Seong-Jang Kim ◽  
Jonghoo Lee ◽  
Gerta Rücker
Keyword(s):  
Meta Analysis ◽  
Effect Sizes ◽  
Analysis Model ◽  
R Software ◽  
Data Synthesis ◽  
Theoretical Concepts ◽  
Moderator Effects ◽  
Analysis Application ◽  
R Packages ◽  
Meta Analyses

The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.

scholarly journals Dose-response meta-analysis: application and practice using the R software

2019 ◽  
Vol 41 ◽  
pp. e2019006 ◽  
Author(s):  
Sung Ryul Shim ◽  
Jonghoo Lee
Keyword(s):  
Dose Response ◽  
Binary Data ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Quadratic Model ◽  
R Software ◽  
Theoretical Concepts ◽  
Quantitative Synthesis ◽  
Analysis Application ◽  
The Difference

The objective of this study was to describe the general approaches of dose-response meta-analysis (DRMA) available for the quantitative synthesis of data using the R software. We conducted a DRMA using two types of data, the difference of means in continuous data and the odds ratio in binary data. The package commands of the R software were “doseresmeta” for the overall effect sizes that were separated into a linear model, quadratic model, and restricted cubic split model for better understanding. The effect sizes according to the dose and a test for linearity were demonstrated and interpreted by analyzing one-stage and two-stage DRMA. The authors examined several flexible models of exposure to pool study-specific trends and made a graphical presentation of the dose-response trend. This study focused on practical methods of DRMA rather than theoretical concepts for researchers who did not major in statistics. The authors hope that this study will help many researchers use the R software to perform DRMAs more easily, and that related research will be pursued.

scholarly journals Diagnostic test accuracy: application and practice using R software

2019 ◽  
Vol 41 ◽  
pp. e2019007 ◽  
Author(s):  
Sung Ryul Shim ◽  
Seong-Jang Kim ◽  
Jonghoo Lee
Keyword(s):  
Diagnostic Test ◽  
Roc Curve ◽  
Univariate Analysis ◽  
Diagnostic Test Accuracy ◽  
Effect Sizes ◽  
Bivariate Analysis ◽  
Forest Plot ◽  
Test Accuracy ◽  
R Software ◽  
Moderator Effect

The objective of this paper is to describe general approaches of diagnostic test accuracy (DTA) that are available for the quantitative synthesis of data using R software. We conduct a DTA that summarizes statistics for univariate analysis and bivariate analysis. The package commands of R software were “metaprop” and “metabin” for sensitivity, specificity, and diagnostic odds ratio; forest for forest plot; reitsma of “mada” for a summarized receiver-operating characteristic (ROC) curve; and “metareg” for meta-regression analysis. The estimated total effect sizes, test for heterogeneity and moderator effect, and a summarized ROC curve are reported using R software. In particular, we focus on how to calculate the effect sizes of target studies in DTA. This study focuses on the practical methods of DTA rather than theoretical concepts for researchers whose fields of study were non-statistics related. By performing this study, we hope that many researchers will use R software to determine the DTA more easily, and that there will be greater interest in related research.

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

A Meta-Analysis of the Facial Feedback Literature: Effects of Facial Feedback on Emotional Experience Are Small and Variable

2017 ◽  
Author(s):  
Nicholas Alvaro Coles ◽  
Jeff T. Larsen ◽  
Heather Lench
Keyword(s):  
Publication Bias ◽  
Emotional Experience ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Detection Methods ◽  
Feedback Effects ◽  
Facial Feedback ◽  
Facial Movements ◽  
Facial Feedback Hypothesis ◽  
Variance Estimates

The facial feedback hypothesis suggests that an individual’s experience of emotion is influenced by feedback from their facial movements. To evaluate the cumulative evidence for this hypothesis, we conducted a meta-analysis on 286 effect sizes derived from 138 studies that manipulated facial feedback and collected emotion self-reports. Using random effects meta-regression with robust variance estimates, we found that the overall effect of facial feedback was significant, but small. Results also indicated that feedback effects are stronger in some circumstances than others. We examined 12 potential moderators, and three were associated with differences in effect sizes. 1. Type of emotional outcome: Facial feedback influenced emotional experience (e.g., reported amusement) and, to a greater degree, affective judgments of a stimulus (e.g., the objective funniness of a cartoon). Three publication bias detection methods did not reveal evidence of publication bias in studies examining the effects of facial feedback on emotional experience, but all three methods revealed evidence of publication bias in studies examining affective judgments. 2. Presence of emotional stimuli: Facial feedback effects on emotional experience were larger in the absence of emotionally evocative stimuli (e.g., cartoons). 3. Type of stimuli: When participants were presented with emotionally evocative stimuli, facial feedback effects were larger in the presence of some types of stimuli (e.g., emotional sentences) than others (e.g., pictures). The available evidence supports the facial feedback hypothesis’ central claim that facial feedback influences emotional experience, although these effects tend to be small and heterogeneous.

scholarly journals Is psychotherapy effective? A re-analysis of treatments for depression

2018 ◽  
Vol 28 (03) ◽  
pp. 268-274 ◽  
Author(s):  
T. Munder ◽  
C. Flückiger ◽  
F. Leichsenring ◽  
A. A. Abbass ◽  
M. J. Hilsenroth ◽  
...  
Keyword(s):  
Natural History ◽  
Publication Bias ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Wait List ◽  
Control Groups ◽  
Data Set ◽  
A Value ◽  
History Of ◽  
History Of Depression

AbstractAimsThe aim of this study was to reanalyse the data from Cuijpers et al.'s (2018) meta-analysis, to examine Eysenck's claim that psychotherapy is not effective. Cuijpers et al., after correcting for bias, concluded that the effect of psychotherapy for depression was small (standardised mean difference, SMD, between 0.20 and 0.30), providing evidence that psychotherapy is not as effective as generally accepted.MethodsThe data for this study were the effect sizes included in Cuijpers et al. (2018). We removed outliers from the data set of effects, corrected for publication bias and segregated psychotherapy from other interventions. In our study, we considered wait-list (WL) controls as the most appropriate estimate of the natural history of depression without intervention.ResultsThe SMD for all interventions and for psychotherapy compared to WL controls was approximately 0.70, a value consistent with past estimates of the effectiveness of psychotherapy. Psychotherapy was also more effective than care-as-usual (SMD = 0.31) and other control groups (SMD = 0.43).ConclusionsThe re-analysis reveals that psychotherapy for adult patients diagnosed with depression is effective.

scholarly journals A meta-analytical answer to the crisis of confidence of Psychology

2019 ◽  
Vol 35 (2) ◽  
pp. 350-356 ◽  
Author(s):  
Juan Botella ◽  
Juan I. Durán
Keyword(s):  
Publication Bias ◽  
Methodological Quality ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Effect Sizes ◽  
Natural Unit ◽  
Valuable Contribution ◽  
Analytical Perspective ◽  
Object Of Study ◽  
Personal Interests

Meta-analysis is a firmly established methodology and an integral part of the process of generating knowledge across the empirical sciences. Meta-analysis has also focused on methodology and has become a dominant critic of methodological shortcomings. We highlight several problematic issues on how we research in psychology: excess of heterogeneity in the results and difficulties for replication, publication bias, suboptimal methodological quality, and questionable practices of the researchers. These and other problems led to a “crisis of confidence” in psychology. We discuss how the meta-analytical perspective and its procedures can help to overcome the crisis. A more cooperative perspective, instead of a competitive one, can shift to consider replication as a more valuable contribution. Knowledge cannot be based in isolated studies. Given the nature of the object of study of psychology the natural unit to generate knowledge must be the estimated distribution of the effect sizes, not the dichotomous decision on statistical significance in specific studies. Some suggestions are offered on how to redirect researchers' research and practices, so that their personal interests and those of science as such are better aligned.

scholarly journals Lowest wealth quintiles predispose under-five children to stunting in Ethiopia: Updates of systematic review and meta-analysis

2020 ◽  
Author(s):  
Mesfin Wudu Kassaw ◽  
Aschalew Afework ◽  
Alemayehu Digssie ◽  
Netsanet Fentahun ◽  
Murat Açık ◽  
...  
Keyword(s):  
Systematic Review ◽  
Publication Bias ◽  
Meta Analysis ◽  
Public Health Problem ◽  
Wealth Index ◽  
Forest Plot ◽  
Major Public Health Problem ◽  
Under Five ◽  
Under Five Children ◽  
Pooled Prevalence

Abstract Background: Malnutrition remains as a major public health problem in the world, particularly in developing countries such as Ethiopia. The prevalence of stunting in Ethiopia has been decreased considerably from 58% in 2000 to 44% in 2011 and 38% in 2016. The aim of this systematic review and meta-analysis is to assess the prevalence of stunting and its associations with wealth index among under-five children in Ethiopia. Methodology: The databases screened were PubMed/MEDLINE, Scopus, HINARI and grey literatures. The studies’ qualities were assessed by two reviewers independently, and any controversy was handled by other reviewers using the JBI critical appraisal checklist. In the statistical analysis, the funnel plot, Egger’s test, and Begg’s test were used to assess publication bias. The I2 statistic, forest plot, and Cochran’s Q test were used to deal with heterogeneity. Results: The pooled prevalence of stunting was 41.5% among under-five children, despite its considerable heterogeneity (I2=97.6%, p<0.001). However, the included studies had no publication bias in calculating the pooled prevalence (Egger’s test p=0.26; Begg’s test p=0.87). Children from households with a medium or low/poor wealth index had higher odds of stunting (AOR 1.33, 95% CI: 1.07, 1.65 or AOR 1.92, 95% CI: 1.46, 2.54, respectively) compared to children from households with a high/rich wealth index. Conclusions: The pooled prevalence of stunting is great. In the subgroup analysis, the Amhara region, followed by the Oromia region and then the Tigray region had the highest prevalence of stunting

scholarly journals A Series of Meta-Analytic Tests of the Efficacy of Long-Term Psychoanalytic Psychotherapy

2020 ◽  
Vol 25 (1) ◽  
pp. 51-72 ◽  
Author(s):  
Christian Franz Josef Woll ◽  
Felix D. Schönbrodt
Keyword(s):  
Mental Disorders ◽  
Publication Bias ◽  
Psychiatric Symptoms ◽  
Meta Analysis ◽  
Research Process ◽  
Open Science ◽  
Effect Sizes ◽  
Psychoanalytic Psychotherapy ◽  
Meta Analyses

Abstract. Recent meta-analyses come to conflicting conclusions about the efficacy of long-term psychoanalytic psychotherapy (LTPP). Our first goal was to reproduce the most recent meta-analysis by Leichsenring, Abbass, Luyten, Hilsenroth, and Rabung (2013) who found evidence for the efficacy of LTPP in the treatment of complex mental disorders. Our replicated effect sizes were in general slightly smaller. Second, we conducted an updated meta-analysis of randomized controlled trials comparing LTPP (lasting for at least 1 year and 40 sessions) to other forms of psychotherapy in the treatment of complex mental disorders. We focused on a transparent research process according to open science standards and applied a series of elaborated meta-analytic procedures to test and control for publication bias. Our updated meta-analysis comprising 191 effect sizes from 14 eligible studies revealed small, statistically significant effect sizes at post-treatment for the outcome domains psychiatric symptoms, target problems, social functioning, and overall effectiveness (Hedges’ g ranging between 0.24 and 0.35). The effect size for the domain personality functioning (0.24) was not significant ( p = .08). No signs for publication bias could be detected. In light of a heterogeneous study set and some methodological shortcomings in the primary studies, these results should be interpreted cautiously. In conclusion, LTPP might be superior to other forms of psychotherapy in the treatment of complex mental disorders. Notably, our effect sizes represent the additional gain of LTPP versus other forms of primarily long-term psychotherapy. In this case, large differences in effect sizes are not to be expected.

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