scholarly journals Multiple moderator meta-analysis using the R-package Meta-CART

2020 ◽  
Vol 52 (6) ◽  
pp. 2657-2673
Author(s):  
Xinru Li ◽  
Elise Dusseldorp ◽  
Xiaogang Su ◽  
Jacqueline J. Meulman
Keyword(s):  
Random Effects ◽  
Treatment Effectiveness ◽  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Tree Model ◽  
Effective Interventions ◽  
Look Ahead ◽  
Fixed And Random Effects ◽  
User Friendly

AbstractIn meta-analysis, heterogeneity often exists between studies. Knowledge about study features (i.e., moderators) that can explain the heterogeneity in effect sizes can be useful for researchers to assess the effectiveness of existing interventions and design new potentially effective interventions. When there are multiple moderators, they may amplify or attenuate each other’s effect on treatment effectiveness. However, in most meta-analysis studies, interaction effects are neglected due to the lack of appropriate methods. The method meta-CART was recently proposed to identify interactions between multiple moderators. The analysis result is a tree model in which the studies are partitioned into more homogeneous subgroups by combinations of moderators. This paper describes the R-package metacart, which provides user-friendly functions to conduct meta-CART analyses in R. This package can fit both fixed- and random-effects meta-CART, and can handle dichotomous, categorical, ordinal and continuous moderators. In addition, a new look ahead procedure is presented. The application of the package is illustrated step-by-step using diverse examples.

Meta-analysis of effect sizes reported at multiple time points: A multivariate approach

2012 ◽  
Vol 9 (5) ◽  
pp. 610-620 ◽  
Author(s):  
Thomas A Trikalinos ◽  
Ingram Olkin
Keyword(s):  
Random Effects ◽  
Treatment Effects ◽  
Multivariate Analyses ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Multiple Time ◽  
Time Points ◽  
Fixed And Random Effects ◽  
Multiple Time Points ◽  
Meta Analyses

Background Many comparative studies report results at multiple time points. Such data are correlated because they pertain to the same patients, but are typically meta-analyzed as separate quantitative syntheses at each time point, ignoring the correlations between time points. Purpose To develop a meta-analytic approach that estimates treatment effects at successive time points and takes account of the stochastic dependencies of those effects. Methods We present both fixed and random effects methods for multivariate meta-analysis of effect sizes reported at multiple time points. We provide formulas for calculating the covariance (and correlations) of the effect sizes at successive time points for four common metrics (log odds ratio, log risk ratio, risk difference, and arcsine difference) based on data reported in the primary studies. We work through an example of a meta-analysis of 17 randomized trials of radiotherapy and chemotherapy versus radiotherapy alone for the postoperative treatment of patients with malignant gliomas, where in each trial survival is assessed at 6, 12, 18, and 24 months post randomization. We also provide software code for the main analyses described in the article. Results We discuss the estimation of fixed and random effects models and explore five options for the structure of the covariance matrix of the random effects. In the example, we compare separate (univariate) meta-analyses at each of the four time points with joint analyses across all four time points using the proposed methods. Although results of univariate and multivariate analyses are generally similar in the example, there are small differences in the magnitude of the effect sizes and the corresponding standard errors. We also discuss conditional multivariate analyses where one compares treatment effects at later time points given observed data at earlier time points. Limitations Simulation and empirical studies are needed to clarify the gains of multivariate analyses compared with separate meta-analyses under a variety of conditions. Conclusions Data reported at multiple time points are multivariate in nature and are efficiently analyzed using multivariate methods. The latter are an attractive alternative or complement to performing separate meta-analyses.

scholarly journals Model Misspecification and Assumption Violations With the Linear Mixed Model: A Meta-Analysis

SAGE Open ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 215824401882038 ◽  
Author(s):  
Brandon LeBeau ◽  
Yoon Ah Song ◽  
Wei Cheng Liu
Keyword(s):  
Random Effects ◽  
Error Rate ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Type I Error ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Type I ◽  
Type I Error Rate ◽  
Fixed And Random Effects

This meta-analysis attempts to synthesize the Monte Carlo (MC) literature for the linear mixed model under a longitudinal framework. The meta-analysis aims to inform researchers about conditions that are important to consider when evaluating model assumptions and adequacy. In addition, the meta-analysis may be helpful to those wishing to design future MC simulations in identifying simulation conditions. The current meta-analysis will use the empirical type I error rate as the effect size and MC simulation conditions will be coded to serve as moderator variables. The type I error rate for the fixed and random effects will be explored as the primary dependent variable. Effect sizes were coded from 13 studies, resulting in a total of 4,002 and 621 effect sizes for fixed and random effects respectively. Meta-regression and proportional odds models were used to explore variation in the empirical type I error rate effect sizes. Implications for applied researchers and researchers planning new MC studies will be explored.

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.

scholarly journals Specificity of Future Thinking in Depression: A Meta-Analysis

2018 ◽  
Author(s):  
Beau Gamble ◽  
David Moreau ◽  
Lynette J. Tippett ◽  
Donna Rose Addis
Keyword(s):  
Random Effects ◽  
Meta Analysis ◽  
Emotional Valence ◽  
Effect Sizes ◽  
Current Evidence ◽  
Future Research ◽  
Future Thinking ◽  
Moderator Variables ◽  
Search Terms ◽  
The Relationship

Reduced specificity of autobiographical memory has been well established in depression, but whether this ‘overgenerality’ extends to future thinking has not been the focus of a meta-analysis. Following a preregistered protocol, we searched six electronic databases, Google Scholar, personal libraries, and contacted authors in the field for studies matching search terms related to depression, future thinking, and specificity. We reduced an initial 7,332 results to 46 included studies, with 89 effect sizes and 4,813 total participants. Random effects meta-analytic modelling revealed a small but robust correlation between reduced future specificity and higher levels of depression (r = .13, p < .001). Of the 11 moderator variables examined, the most striking effects related to the emotional valence of future thinking (p < .001) and the sex of participants (p = .025). Namely, depression was linked to reduced specificity for positive (but not negative or neutral) future thinking, and the relationship was stronger in samples with a higher proportion of males. This meta-analysis contributes to our understanding of how prospection is altered in depression and dysphoria and, by revealing areas where current evidence is inconclusive, highlights key avenues for future research.

scholarly journals Effectiveness of Psychological Treatments for Borderline Personality Disorder and Predictors of Treatment Outcomes: A Multivariate Multilevel Meta-Analysis of Data from All Design Types

2021 ◽  
Vol 10 (23) ◽  
pp. 5622
Author(s):  
Sophie A. Rameckers ◽  
Rogier E. J. Verhoef ◽  
Raoul P. P. P. Grasman ◽  
Wouter R. Cox ◽  
Arnold A. P. van Emmerik ◽  
...  
Keyword(s):  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Dialectical Behavior Therapy ◽  
Treatment Effectiveness ◽  
Meta Analysis ◽  
Borderline Personality ◽  
Effect Sizes ◽  
Sensitivity Analyses ◽  
Treatment As Usual ◽  
Logarithmic Relationship

We examined the effectiveness of psychotherapies for adult Borderline Personality Disorder (BPD) in a multilevel meta-analysis, including all trial types (PROSPERO ID: CRD42020111351). We tested several predictors, including trial- and outcome type (continuous or dichotomous), setting, BPD symptom domain and mean age. We included 87 studies (N = 5881) from searches between 2013 and 2019 in four databases. We controlled for differing treatment lengths and a logarithmic relationship between treatment duration and effectiveness. Sensitivity analyses were conducted by excluding outliers and by prioritizing total scale scores when both subscale and total scores were reported. Schema Therapy, Mentalization-Based Treatment and reduced Dialectical Behavior Therapy were associated with higher effect sizes than average, and treatment-as-usual with lower effect sizes. General severity and affective instability showed the strongest improvement, dissociation, anger, impulsivity and suicidality/self-injury the least. Treatment effectiveness decreased as the age of participants increased. Dichotomous outcomes were associated to larger effects, and analyses based on last observation carried forward to smaller effects. Compared to the average, the highest reductions were found for certain specialized psychotherapies. All BPD domains improved, though not equally. These findings have a high generalizability. However, causal conclusions cannot be drawn, although the design type did not influence the results.

scholarly journals Metan: Fixed- and Random-Effects Meta-Analysis

2008 ◽  
Vol 8 (1) ◽  
pp. 3-28 ◽  
Author(s):  
Ross J. Harris ◽  
Jonathan J. Deeks ◽  
Douglas G. Altman ◽  
Michael J. Bradburn ◽  
Roger M. Harbord ◽  
...  
Keyword(s):  
Random Effects ◽  
Meta Analysis ◽  
Fixed And Random Effects

Systematic Review and Meta-Analysis of Classroom-Wide Social–Emotional Interventions for Preschool Children

2020 ◽  
pp. 027112142093557 ◽  
Author(s):  
Li Luo ◽  
Brian Reichow ◽  
Patricia Snyder ◽  
Jennifer Harrington ◽  
Joy Polignano
Keyword(s):  
Systematic Review ◽  
Preschool Children ◽  
Social Competence ◽  
Random Effects ◽  
Challenging Behavior ◽  
Emotional Competence ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Social Emotional ◽  
Meta Analyses

Background: All children benefit from intentional interactions and instruction to become socially and emotionally competent. Over the past 30 years, evidence-based intervention tactics and strategies have been integrated to establish comprehensive, multitiered, or hierarchical systems of support frameworks to guide social–emotional interventions for young children. Objectives: To review systematically the efficacy of classroom-wide social–emotional interventions for improving the social, emotional, and behavioral outcomes of preschool children and to use meta-analytic techniques to identify critical study characteristics associated with obtained effect sizes. Method: Four electronic databases (i.e., Academic Search Premier, Educational Resource Information Center, PsycINFO, and Education Full Text) were systematically searched in December 2015 and updated in January 2018. “Snowball methods” were used to locate additional relevant studies. Effect size estimates were pooled using random-effects meta-analyses for three child outcomes, and moderator analyses were conducted. Results: Thirty-nine studies involving 10,646 child participants met the inclusion criteria and were included in this systematic review, with 33 studies included in the meta-analyses. Random-effects meta-analyses showed improvements in social competence ( g = 0.42, 95% confidence interval [CI] = [0.28, 0.56]) and emotional competence ( g = 0.33, 95% CI = [0.10, 0.56]), and decreases in challenging behavior ( g = −0.31, 95% CI = [−0.43, −0.19]). For social competence and challenging behavior, moderator analyses suggested interventions with a family component had statistically significant and larger effect sizes than those without a family component. Studies in which classroom teachers served as the intervention agent produced statistically significant but smaller effect sizes than when researchers or others implemented the intervention for challenging behavior. Conclusion: This systematic review and meta-analysis support using comprehensive social–emotional interventions for all children in a preschool classroom to improve their social–emotional competence and reduce challenging behavior.

scholarly journals Nutraceutical supplements in management of pain and disability in osteoarthritis: a systematic review and meta-analysis of randomized clinical trials

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Dawood Aghamohammadi ◽  
Neda Dolatkhah ◽  
Fahimeh Bakhtiari ◽  
Fariba Eslamian ◽  
Maryam Hashemian
Keyword(s):  
Pain Intensity ◽  
Physical Function ◽  
Random Effects ◽  
Effect Size ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Cochrane Library ◽  
Random Effects Model ◽  
Womac Index ◽  
Pain Subscale

AbstractThis study designed to evaluate the effect of nutraceutical supplementation on pain intensity and physical function in patients with knee/hip OA. The MEDLINE, Web of Science, Cochrane Library, Scopus, EMBASE, Google Scholar, Science direct, and ProQuest in addition to SID, Magiran, and Iranmedex were searched up to March 2020. Records (n = 465) were screened via the PICOS criteria: participants were patients with hip or knee OA; intervention was different nutritional supplements; comparator was any comparator; the outcome was pain intensity (Visual analogue scale [VAS]) and physical function (Western Ontario and McMaster Universities Arthritis [WOMAC] index); study type was randomized controlled trials. The random effects model was used to pool the calculated effect sizes. The standardized mean difference (SMD) of the outcome changes was considered as the effect size. The random effects model was used to combine the effect sizes. Heterogeneity between studies was assessed by Cochran's (Q) and I2 statistics. A total of 42 RCTs were involved in the meta-analysis. Nutritional supplementation were found to improve total WOMAC index (SMD = − 0.23, 95% CI − 0.37 to − 0.08), WOMAC pain (SMD = − 0.36, 95% CI − 0.62 to − 0.10) and WOMAC stiffness (SMD = − 0.47, 95% CI − 0.71 to − 0.23) subscales and VAS (SMD = − 0.79, 95% CI − 1.05 to − 0.05). Results of subgroup analysis according to the supplementation duration showed that the pooled effect size in studies with < 10 months, 10–20 months and > 20 months supplementation duration were 0.05, 0.27, and 0.36, respectively for WOMAC total score, 0.14, 0.55 and 0.05, respectively for WOAMC pain subscale, 0.59, 0.47 and 0.41, respectively for WOMAC stiffness subscale, 0.05, 0.57 and 0.53, respectively for WOMAC physical function subscale and 0.65, 0.99 and 0.12, respectively for VAS pain. The result suggested that nutraceutical supplementation of patients with knee/hip OA may lead to an improvement in pain intensity and physical function.

Meta-Analyses as a Multi-Level Model

2019 ◽  
pp. 109442811985747
Author(s):  
Janaki Gooty ◽  
George C. Banks ◽  
Andrew C. Loignon ◽  
Scott Tonidandel ◽  
Courtney E. Williams
Keyword(s):  
Educational Psychology ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Independence Assumption ◽  
The Social ◽  
Level Model ◽  
Multi Level ◽  
Meta Analyses ◽  
User Friendly ◽  
Organizational Sciences

Meta-analyses are well known and widely implemented in almost every domain of research in management as well as the social, medical, and behavioral sciences. While this technique is useful for determining validity coefficients (i.e., effect sizes), meta-analyses are predicated on the assumption of independence of primary effect sizes, which might be routinely violated in the organizational sciences. Here, we discuss the implications of violating the independence assumption and demonstrate how meta-analysis could be cast as a multilevel, variance known (Vknown) model to account for such dependency in primary studies’ effect sizes. We illustrate such techniques for meta-analytic data via the HLM 7.0 software as it remains the most widely used multilevel analyses software in management. In so doing, we draw on examples in educational psychology (where such techniques were first developed), organizational sciences, and a Monte Carlo simulation (Appendix). We conclude with a discussion of implications, caveats, and future extensions. Our Appendix details features of a newly developed application that is free (based on R), user-friendly, and provides an alternative to the HLM program.

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