scholarly journals Statistical Models and Methods for Network Meta-Analysis

2016 ◽  
Vol 106 (8) ◽  
pp. 792-806 ◽  
Author(s):  
L. V. Madden ◽  
H.-P. Piepho ◽  
P. A. Paul
Keyword(s):  
Effect Size ◽  
Treatment Effects ◽  
Meta Analysis ◽  
Model Fitting ◽  
Treatment Plant ◽  
Analytical Models ◽  
Data Set ◽  
Single Effect ◽  
Treatment Comparisons ◽  
Meta Analyses

Meta-analysis, the methodology for analyzing the results from multiple independent studies, has grown tremendously in popularity over the last four decades. Although most meta-analyses involve a single effect size (summary result, such as a treatment difference) from each study, there are often multiple treatments of interest across the network of studies in the analysis. Multi-treatment (or network) meta-analysis can be used for simultaneously analyzing the results from all the treatments. However, the methodology is considerably more complicated than for the analysis of a single effect size, and there have not been adequate explanations of the approach for agricultural investigations. We review the methods and models for conducting a network meta-analysis based on frequentist statistical principles, and demonstrate the procedures using a published multi-treatment plant pathology data set. A major advantage of network meta-analysis is that correlations of estimated treatment effects are automatically taken into account when an appropriate model is used. Moreover, treatment comparisons may be possible in a network meta-analysis that are not possible in a single study because all treatments of interest may not be included in any given study. We review several models that consider the study effect as either fixed or random, and show how to interpret model-fitting output. We further show how to model the effect of moderator variables (study-level characteristics) on treatment effects, and present one approach to test for the consistency of treatment effects across the network. Online supplemental files give explanations on fitting the network meta-analytical models using SAS.

scholarly journals Internet- and Mobile-Based Interventions for Mental and Somatic Conditions in Children and Adolescents

2020 ◽  
Vol 48 (1) ◽  
pp. 33-46 ◽  
Author(s):  
Matthias Domhardt ◽  
Lena Steubl ◽  
Harald Baumeister
Keyword(s):  
Mental Disorders ◽  
Children And Adolescents ◽  
Effect Size ◽  
Treatment Effects ◽  
Meta Analysis ◽  
Mental Healthcare ◽  
Quality Ratings ◽  
Meta Review ◽  
Mean Differences ◽  
Meta Analyses

Abstract. This meta-review integrates the current meta-analysis literature on the efficacy of internet- and mobile-based interventions (IMIs) for mental disorders and somatic diseases in children and adolescents. Further, it summarizes the moderators of treatment effects in this age group. Using a systematic literature search of PsycINFO and MEDLINE/PubMed, we identified eight meta-analyses (N = 8,417) that met all inclusion criteria. Current meta-analytical evidence of IMIs exists for depression (range of standardized mean differences, SMDs = .16 to .76; 95 % CI: –.12 to 1.12; k = 3 meta-analyses), anxiety (SMDs = .30 to 1.4; 95 % CI: –.53 to 2.44; k = 5) and chronic pain (SMD = .41; 95 % CI: .07 to .74; k = 1) with predominantly nonactive control conditions (waiting-list; placebo). The effect size for IMIs across mental disorders reported in one meta-analysis is SMD = 1.27 (95 % CI: .96 to 1.59; k = 1), the effect size of IMIs for different somatic conditions is SMD = .49 (95 % CI: .33 to .64; k = 1). Moderators of treatment effects are age (k = 3), symptom severity (k = 1), and source of outcome assessment (k = 1). Quality ratings with the AMSTAR-2-checklist indicate acceptable methodological rigor of meta-analyses included. Taken together, this meta-review suggests that IMIs are efficacious in some health conditions in youths, with evidence existing primarily for depression and anxiety so far. The findings point to the potential of IMIs to augment evidence based mental healthcare for children and adolescents.

scholarly journals Vibration of effects from diverse inclusion/exclusion criteria and analytical choices: 9216 different ways to perform an indirect comparison meta-analysis

BMC Medicine ◽  
2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Clément Palpacuer ◽  
Karima Hammas ◽  
Renan Duprez ◽  
Bruno Laviolle ◽  
John P. A. Ioannidis ◽  
...  
Keyword(s):  
Effect Size ◽  
Meta Analysis ◽  
Indirect Comparison ◽  
Analytical Models ◽  
Cochrane Library ◽  
European Medicines Agency ◽  
Contradictory Result ◽  
Exclusion Criteria ◽  
Methodological Choices ◽  
Meta Analyses

Abstract Background Different methodological choices such as inclusion/exclusion criteria and analytical models can yield different results and inferences when meta-analyses are performed. We explored the range of such differences, using several methodological choices for indirect comparison meta-analyses to compare nalmefene and naltrexone in the reduction of alcohol consumption as a case study. Methods All double-blind randomized controlled trials (RCTs) comparing nalmefene to naltrexone or one of these compounds to a placebo in the treatment of alcohol dependence or alcohol use disorders were considered. Two reviewers searched for published and unpublished studies in MEDLINE (August 2017), the Cochrane Library, Embase, and ClinicalTrials.gov and contacted pharmaceutical companies, the European Medicines Agency, and the Food and Drug Administration. The indirect comparison meta-analyses were performed according to different inclusion/exclusion criteria (based on medical condition, abstinence of patients before inclusion, gender, somatic and psychiatric comorbidity, psychological support, treatment administered and dose, treatment duration, outcome reported, publication status, and risk of bias) and different analytical models (fixed and random effects). The primary outcome was the vibration of effects (VoE), i.e. the range of different results of the indirect comparison between nalmefene and naltrexone. The presence of a “Janus effect” was investigated, i.e. whether the 1st and 99th percentiles in the distribution of effect sizes were in opposite directions. Results Nine nalmefene and 51 naltrexone RCTs were included. No study provided a direct comparison between the drugs. We performed 9216 meta-analyses for the indirect comparison with a median of 16 RCTs (interquartile range = 12–21) included in each meta-analysis. The standardized effect size was negative at the 1st percentile (− 0.29, favouring nalmefene) and positive at the 99th percentile (0.29, favouring naltrexone). A total of 7.1% (425/5961) of the meta-analyses with a negative effect size and 18.9% (616/3255) of those with a positive effect size were statistically significant (p < 0.05). Conclusions The choice of inclusion/exclusion criteria and analytical models for meta-analysis can result in entirely opposite results. VoE evaluations could be performed when overlapping meta-analyses on the same topic yield contradictory result. Trial registration This study was registered on October 19, 2016, in the Open Science Framework (OSF, protocol available at https://osf.io/7bq4y/).

scholarly journals Efficacy of new-generation antidepressants assessed with the Montgomery-Asberg Depression Rating Scale, the gold standard clinician rating scale: A meta-analysis of randomised placebo-controlled trials

2019 ◽  
Author(s):  
Michael P. Hengartner ◽  
Janus Christian Jakobsen ◽  
Anders Sorensen ◽  
Martin Plöderl
Keyword(s):  
Effect Size ◽  
Gold Standard ◽  
Treatment Effects ◽  
Rating Scale ◽  
Meta Analysis ◽  
Standard Procedure ◽  
Controlled Trials ◽  
Summary Estimate ◽  
Minimal Improvement ◽  
Meta Analyses

Background: It has been claimed that efficacy estimates based in the Hamilton Depression Rating-Scale (HDRS) underestimate antidepressants true treatment effects due to the instrument’s poor psychometric properties. The aim of this study is to compare efficacy estimates based on the HDRS with the gold standard procedure, the Montgomery-Asberg Depression Rating-Scale (MADRS).Methods and findings: We conducted a meta-analysis based on the comprehensive dataset of acute antidepressant trials provided by Cipriani et al. We included all placebo-controlled trials that reported continuous outcomes based on either the HDRS 17-item version or the MADRS. We computed standardised mean difference effect size estimates and raw score drug-placebo differences to evaluate thresholds for clinician-rated minimal improvements (clinical significance). We selected 109 trials (n=32,399) that assessed the HDRS-17 and 28 trials (n=11,705) that assessed the MADRS. The summary estimate (effect size) for the HDRS-17 was 0.27 (0.23 to 0.30) compared to 0.30 (0.22 to 0.38) for the MADRS. The difference between HDRS-17 and MADRS was not statistically significant according to both subgroup analysis (p=0.47) and meta-regression (p=0.44). Drug-placebo raw score difference was 2.07 (1.76 to 2.37) points on the HDRS-17 (threshold for minimal improvement: 7 points) and 2.99 (2.24-3.74) points on the MADRS (threshold for minimal improvement: 8 points). Conclusions: Overall there was no difference between the HDRS-17 and the MADRS. These findings suggest that previous meta-analyses that were mostly based on the HDRS did not underestimate the drugs’ true treatment effect as assessed with MADRS, the preferred outcome rating scale. Moreover, the drug-placebo differences in raw scores suggest that treatment effects are indeed marginally small and with questionable importance for the average patient.

scholarly journals Predictability of rotational tooth movement with orthodontic aligners comparing software-based and achieved data: A systematic review and meta-analysis of observational studies

2021 ◽  
pp. 146531252110272
Author(s):  
Despina Koletsi ◽  
Anna Iliadi ◽  
Theodore Eliades
Keyword(s):  
Effect Size ◽  
Tooth Movement ◽  
Data Extraction ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Patient Treatment ◽  
Qualitative Synthesis ◽  
Meta Analyses ◽  
Enamel Reduction

Objective: To evaluate all available evidence on the prediction of rotational tooth movements with aligners. Data sources: Seven databases of published and unpublished literature were searched up to 4 August 2020 for eligible studies. Data selection: Studies were deemed eligible if they included evaluation of rotational tooth movement with any type of aligner, through the comparison of software-based and actually achieved data after patient treatment. Data extraction and data synthesis: Data extraction was done independently and in duplicate and risk of bias assessment was performed with the use of the QUADAS-2 tool. Random effects meta-analyses with effect sizes and their 95% confidence intervals (CIs) were performed and the quality of the evidence was assessed through GRADE. Results: Seven articles were included in the qualitative synthesis, of which three contributed to meta-analyses. Overall results revealed a non-accurate prediction of the outcome for the software-based data, irrespective of the use of attachments or interproximal enamel reduction (IPR). Maxillary canines demonstrated the lowest percentage accuracy for rotational tooth movement (three studies: effect size = 47.9%; 95% CI = 27.2–69.5; P < 0.001), although high levels of heterogeneity were identified (I2: 86.9%; P < 0.001). Contrary, mandibular incisors presented the highest percentage accuracy for predicted rotational movement (two studies: effect size = 70.7%; 95% CI = 58.9–82.5; P < 0.001; I2: 0.0%; P = 0.48). Risk of bias was unclear to low overall, while quality of the evidence ranged from low to moderate. Conclusion: Allowing for all identified caveats, prediction of rotational tooth movements with aligner treatment does not appear accurate, especially for canines. Careful selection of patients and malocclusions for aligner treatment decisions remain challenging.

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 A Statistical Method for Synthesizing Meta-Analyses

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Liansheng Larry Tang ◽  
Michael Caudy ◽  
Faye Taxman
Keyword(s):  
Statistical Method ◽  
Effect Size ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Weighted Mean ◽  
Summary Effect ◽  
Different Types ◽  
Meta Analyses ◽  
Size Estimates ◽  
Similar Search

Multiple meta-analyses may use similar search criteria and focus on the same topic of interest, but they may yield different or sometimes discordant results. The lack of statistical methods for synthesizing these findings makes it challenging to properly interpret the results from multiple meta-analyses, especially when their results are conflicting. In this paper, we first introduce a method to synthesize the meta-analytic results when multiple meta-analyses use the same type of summary effect estimates. When meta-analyses use different types of effect sizes, the meta-analysis results cannot be directly combined. We propose a two-step frequentist procedure to first convert the effect size estimates to the same metric and then summarize them with a weighted mean estimate. Our proposed method offers several advantages over existing methods by Hemming et al. (2012). First, different types of summary effect sizes are considered. Second, our method provides the same overall effect size as conducting a meta-analysis on all individual studies from multiple meta-analyses. We illustrate the application of the proposed methods in two examples and discuss their implications for the field of meta-analysis.

scholarly journals Cluster Wild Bootstrapping to Handle Dependent Effect Sizes in Meta-Analysis with a Small Number of Studies

2021 ◽  
Author(s):  
Megha Joshi ◽  
James E Pustejovsky ◽  
S. Natasha Beretvas
Keyword(s):  
Effect Size ◽  
Type I Error ◽  
Meta Analysis ◽  
Error Rates ◽  
Small Sample ◽  
Type I ◽  
Hypothesis Tests ◽  
Type I Error Rates ◽  
Meta Analyses ◽  
Small Sample Correction

The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include multiple effect size estimates per primary study, leading to dependence in the estimates. Some meta-analyses also include multiple studies conducted by the same lab or investigator, creating another potential source of dependence. An increasingly popular method to handle dependence is robust variance estimation (RVE), but this method can result in inflated Type I error rates when the number of studies is small. Small-sample correction methods for RVE have been shown to control Type I error rates adequately but may be overly conservative, especially for tests of multiple-contrast hypotheses. We evaluated an alternative method for handling dependence, cluster wild bootstrapping, which has been examined in the econometrics literature but not in the context of meta-analysis. Results from two simulation studies indicate that cluster wild bootstrapping maintains adequate Type I error rates and provides more power than extant small sample correction methods, particularly for multiple-contrast hypothesis tests. We recommend using cluster wild bootstrapping to conduct hypothesis tests for meta-analyses with a small number of studies. We have also created an R package that implements such tests.

A meta-analysis of the variables related to turnover intention among IT personnel

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmet Hakan Özkan
Keyword(s):  
Job Satisfaction ◽  
Organizational Commitment ◽  
Effect Size ◽  
Turnover Intention ◽  
Meta Analysis ◽  
It Industry ◽  
Data Set ◽  
Content Type ◽  
It Personnel ◽  
The Relationship

PurposeThis study aims to investigate the relationships between job satisfaction, organizational commitment and turnover intention of information technology (IT) personnel.Design/methodology/approach3,844 studies which are published between 1998 and 2019 are screened on ScienceDirect, Scopus and ProQuest databases. 10,523 subjects formed the first data set regarding the relationship between job satisfaction and turnover intention, 7,903 subjects formed the second data set regarding the relationship between organizational commitment and turnover intention, 843 subjects formed the third data set regarding the relationship between empowerment and turnover intention, and 3,430 subjects formed the fourth data set regarding the relationship between job satisfaction and organizational commitment.FindingsResults showed that the effect size of the relationship between job satisfaction and organizational commitment is the strongest (r = 0.59). The effect size of the relationship between job satisfaction and turnover intention (r = −0.50), and the effect size of the relationship between organizational commitment and turnover intention r = −0.51) were also large. But the effect size of the relationship between empowerment and turnover intention was medium (r = −0.34).Originality/valueThis study is rare, and it can be used by the managers working in the IT industry.

scholarly journals Considering the methodological limitations in the evidence base of antidepressants for depression: a reanalysis of a network meta-analysis

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e024886 ◽  
Author(s):  
Klaus Munkholm ◽  
Asger Sand Paludan-Müller ◽  
Kim Boesen
Keyword(s):  
Systematic Review ◽  
Clinical Study ◽  
Effect Size ◽  
Meta Analysis ◽  
Evidence Base ◽  
Risk Of Bias ◽  
Depression Symptom ◽  
Meta Analyses ◽  
The Impact ◽  
Clinical Study Reports

ObjectivesTo investigate whether the conclusion of a recent systematic review and network meta-analysis (Ciprianiet al) that antidepressants are more efficacious than placebo for adult depression was supported by the evidence.DesignReanalysis of a systematic review, with meta-analyses.Data sources522 trials (116 477 participants) as reported in the systematic review by Ciprianiet aland clinical study reports for 19 of these trials.AnalysisWe used the Cochrane Handbook’s risk of bias tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to evaluate the risk of bias and the certainty of evidence, respectively. The impact of several study characteristics and publication status was estimated using pairwise subgroup meta-analyses.ResultsSeveral methodological limitations in the evidence base of antidepressants were either unrecognised or underestimated in the systematic review by Ciprianiet al. The effect size for antidepressants versus placebo on investigator-rated depression symptom scales was higher in trials with a ‘placebo run-in’ study design compared with trials without a placebo run-in design (p=0.05). The effect size of antidepressants was higher in published trials compared with unpublished trials (p<0.0001). The outcome data reported by Ciprianiet aldiffered from the clinical study reports in 12 (63%) of 19 trials. The certainty of the evidence for the placebo-controlled comparisons should be very low according to GRADE due to a high risk of bias, indirectness of the evidence and publication bias. The mean difference between antidepressants and placebo on the 17-item Hamilton depression rating scale (range 0–52 points) was 1.97 points (95% CI 1.74 to 2.21).ConclusionsThe evidence does not support definitive conclusions regarding the benefits of antidepressants for depression in adults. It is unclear whether antidepressants are more efficacious than placebo.

scholarly journals Are Community Gardening and Horticultural Interventions Beneficial for Psychosocial Well-Being? A Meta-Analysis

2020 ◽  
Vol 17 (10) ◽  
pp. 3584
Author(s):  
Giuseppina Spano ◽  
Marina D’Este ◽  
Vincenzo Giannico ◽  
Giuseppe Carrus ◽  
Mario Elia ◽  
...  
Keyword(s):  
Human Health ◽  
Effect Size ◽  
Meta Analysis ◽  
Well Being ◽  
City Management ◽  
Community Gardening ◽  
Quantitative Studies ◽  
Bibliographic Records ◽  
Study Heterogeneity ◽  
Meta Analyses

Recent literature has revealed the positive effect of gardening on human health; however, empirical evidence on the effects of gardening-based programs on psychosocial well-being is scant. This meta-analysis aims to examine the scientific literature on the effect of community gardening or horticultural interventions on a variety of outcomes related to psychosocial well-being, such as social cohesion, networking, social support, and trust. From 383 bibliographic records retrieved (from 1975 to 2019), seven studies with a total of 22 effect sizes were selected on the basis of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Meta-analytic findings on 11 comparisons indicate a positive and moderate effect of horticultural or gardening interventions on psychosocial well-being. Moderation analysis shows a greater effect size in individualistic than collectivistic cultures. A greater effect size was also observed in studies involving community gardening compared to horticultural intervention. Nevertheless, an effect of publication bias and study heterogeneity has been detected. Despite the presence of a large number of qualitative studies on the effect of horticulture/gardening on psychosocial well-being, quantitative studies are lacking. There is a strong need to advance into further high-quality studies on this research topic given that gardening has promising applied implications for human health, the community, and sustainable city management.

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