scholarly journals Introducere în metaanaliză. Metaanaliza corelațiilor

2020 ◽  
Vol 6 (2) ◽  
pp. 112-127
Author(s):  
Laurențiu Maricuțoiu
Keyword(s):  
Statistical Method ◽  
Confidence Intervals ◽  
Effect Size ◽  
Meta Analysis ◽  
Relevant Information ◽  
Effect Sizes ◽  
The Mean

The present paper discusses the fundamental principles of meta-analysis, as a statistical method for summarising results of correlational studies. We approach fundamental issues such as: the finality of meta-analysis and the problems associated with study artefacts. The paper also contains recommendations for: selecting the studies for meta-analysis, identifying the relevant information within these studies, computing mean effect sizes, confidence intervals and heterogeneity indexes of the mean effect size. Finally, we present indications for reporting meta-analysis results.

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 Pharmacist-Led Interventions to Improve Medication Adherence in Older Adults: A Meta-Analysis

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 335-335
Author(s):  
Zachary Marcum ◽  
Shangqing Jiang ◽  
Jennifer Bacci ◽  
Todd Ruppar
Keyword(s):  
Older Adults ◽  
Medication Adherence ◽  
Effect Size ◽  
English Language ◽  
Randomized Clinical Trials ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Analysis Model ◽  
Improve Medication Adherence ◽  
The Mean

Abstract As pharmacists work to ensure reimbursement for chronic disease management services on the national (e.g., Medicare) level, summative evidence of their impact on important health metrics, such as medication adherence, is needed. The objective of this study was to assess the effectiveness of pharmacist-led interventions on medication adherence in older adults. In April 2020, a comprehensive search was conducted in six databases for publications of randomized clinical trials of pharmacist-led interventions to improve medication adherence in older adults. English-language studies with codable data on medication adherence and diverse adherence-promoting interventions targeting older adults (age 65+) were eligible. A standardized mean difference effect size (intervention vs. control) was calculated for the medication adherence outcome in each study. Study effect sizes were pooled using a random-effects meta-analysis model. Moderator analyses were then conducted to explore for differences in effect size due to intervention, sample, and study characteristics. The primary outcome was medication adherence using any method of measurement. This meta-analysis included 40 unique randomized trials of pharmacist-led interventions with data from 8,822 unique patients (mean age, range: 65 to 85 years). The mean effect size was 0.57 (95% Confidence Interval [CI]: 0.38-0.76). When two outlier studies were excluded from the analysis, the mean effect size decreased to 0.41 (95% CI: 0.27-0.54). Moderator analyses showed larger effect sizes for interventions containing medication education and when interventions had components delivered at least partly in patients’ homes. In conclusion, this meta-analysis found a significant improvement in medication adherence among older adults receiving pharmacist-led interventions.

scholarly journals Why All Researchers Should Report Effect Sizes and Their Confidence Intervals: Paving the Way for Meta–Analysis and Evidence–Based Management Practices

2015 ◽  
Vol 39 (4) ◽  
pp. 719-725 ◽  
Author(s):  
Andreas Schwab
Keyword(s):  
Confidence Intervals ◽  
Management Practices ◽  
Meta Analysis ◽  
Empirical Studies ◽  
Relevant Information ◽  
Effect Sizes ◽  
Evidence Based ◽  
Evidence Based Practices ◽  
Reporting Practices ◽  
Support Research

The growing body of empirical entrepreneurship studies and the advent of meta–analytic methodologies create new opportunities to develop evidence–based management practices. To support research on evidence–based practices, empirical studies should report meta–analysis relevant information, such as standardized effect–size measures and their confidence intervals. The corresponding changes in reporting practices are simple and straight forward—yet they promise strong contributions to the systematic accumulation of entrepreneurship knowledge over time.

scholarly journals Predictive physiological anticipation preceding seemingly unpredictable stimuli: An update of Mossbridge et al’s meta-analysis

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 407
Author(s):  
Michael Duggan ◽  
Patrizio Tressoldi
Keyword(s):  
Confidence Intervals ◽  
Publication Bias ◽  
Effect Size ◽  
Statistical Estimation ◽  
Meta Analysis ◽  
Random Model ◽  
Effect Sizes ◽  
Weighted Effect Size ◽  
Credible Intervals ◽  
Weighted Effect

Background: This is an update of the Mossbridge et al’s meta-analysis related to the physiological anticipation preceding seemingly unpredictable stimuli. The overall effect size observed was 0.21; 95% Confidence Intervals: 0.13 - 0.29 Methods: Eighteen new peer and non-peer reviewed studies completed from January 2008 to October 2017 were retrieved describing a total of 26 experiments and 34 associated effect sizes. Results: The overall weighted effect size, estimated with a frequentist multilevel random model, was: 0.29; 95% Confidence Intervals: 0.19-0.38; the overall weighted effect size, estimated with a multilevel Bayesian model, was: 0.29; 95% Credible Intervals: 0.18-0.39. Effect sizes of peer reviewed studies were slightly higher: 0.38; Confidence Intervals: 0.27-0.48 than non-peer reviewed articles: 0.22; Confidence Intervals: 0.05-0.39. The statistical estimation of the publication bias by using the Copas model suggest that the main findings are not contaminated by publication bias. Conclusions: In summary, with this update, the main findings reported in Mossbridge et al’s meta-analysis, are confirmed.

Abstract 3789: Nessun Dorma : Have the Risks of Rosiglitazone been Exaggerated?

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
George A Diamond ◽  
Sanjay Kaul
Keyword(s):  
Confidence Intervals ◽  
Effect Size ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Cardiovascular Death ◽  
Sensitivity Analyses ◽  
Odds Ratios ◽  
True Effect Size ◽  
Index Study ◽  
The Stability

Background A highly publicized meta-analysis of 42 clinical trials comprising 27,844 diabetics ignited a firestorm of controversy by charging that treatment with rosiglitazone was associated with a “…worrisome…” 43% greater risk of myocardial infarction ( p =0.03) and a 64% greater risk of cardiovascular death ( p =0.06). Objective The investigators excluded 4 trials from the infarction analysis and 19 trials from the mortality analysis in which no events were observed. We sought to determine if these exclusions biased the results. Methods We compared the index study to a Bayesian meta-analysis of the entire 42 trials (using odds ratio as the measure of effect size) and to fixed-effects and random-effects analyses with and without a continuity correction that adjusts for values of zero. Results The odds ratios and confidence intervals for the analyses are summarized in the Table . Odds ratios for infarction ranged from 1.43 to 1.22 and for death from 1.64 to 1.13. Corrected models resulted in substantially smaller odds ratios and narrower confidence intervals than did uncorrected models. Although corrected risks remain elevated, none are statistically significant (*p<0.05). Conclusions Given the fragility of the effect sizes and confidence intervals, the charge that roziglitazone increases the risk of adverse events is not supported by these additional analyses. The exaggerated values observed in the index study are likely the result of excluding the zero-event trials from analysis. Continuity adjustments mitigate this error and provide more consistent and reliable assessments of true effect size. Transparent sensitivity analyses should therefore be performed over a realistic range of the operative assumptions to verify the stability of such assessments especially when outcome events are rare. Given the relatively wide confidence intervals, additional data will be required to adjudicate these inconclusive results.

scholarly journals Changes of Ankle Dorsiflexion Using Compression Tissue Flossing: A Systematic Review and Meta-Analysis

2020 ◽  
pp. 1-9
Author(s):  
Devin S. Kielur ◽  
Cameron J. Powden
Keyword(s):  
Confidence Interval ◽  
Range Of Motion ◽  
Effect Size ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Lower Extremity Injury ◽  
Level Of Evidence ◽  
Physically Active ◽  
Evidence Database

Context: Impaired dorsiflexion range of motion (DFROM) has been established as a predictor of lower-extremity injury. Compression tissue flossing (CTF) may address tissue restrictions associated with impaired DFROM; however, a consensus is yet to support these effects. Objectives: To summarize the available literature regarding CTF on DFROM in physically active individuals. Evidence Acquisition: PubMed and EBSCOhost (CINAHL, MEDLINE, and SPORTDiscus) were searched from 1965 to July 2019 for related articles using combination terms related to CTF and DRFOM. Articles were included if they measured the immediate effects of CTF on DFROM. Methodological quality was assessed using the Physiotherapy Evidence Database scale. The level of evidence was assessed using the Strength of Recommendation Taxonomy. The magnitude of CTF effects from pre-CTF to post-CTF and compared with a control of range of motion activities only were examined using Hedges g effect sizes and 95% confidence intervals. Randomeffects meta-analysis was performed to synthesize DFROM changes. Evidence Synthesis: A total of 6 studies were included in the analysis. The average Physiotherapy Evidence Database score was 60% (range = 30%–80%) with 4 out of 6 studies considered high quality and 2 as low quality. Meta-analysis indicated no DFROM improvements for CTF compared with range of motion activities only (effect size = 0.124; 95% confidence interval, −0.137 to 0.384; P = .352) and moderate improvements from pre-CTF to post-CTF (effect size = 0.455; 95% confidence interval, 0.022 to 0.889; P = .040). Conclusions: There is grade B evidence to suggest CTF may have no effect on DFROM when compared with a control of range of motion activities only and results in moderate improvements from pre-CTF to post-CTF. This suggests that DFROM improvements were most likely due to exercises completed rather than the band application.

Effect Sizes: Conventional Choices and Calculations

2013 ◽  
Author(s):  
Michael S. Rosenberg ◽  
Hannah R. Rothstein ◽  
Jessica Gurevitch
Keyword(s):  
Critical Point ◽  
Effect Size ◽  
Evolutionary Biology ◽  
Meta Analysis ◽  
Statistical Parameter ◽  
Effect Sizes ◽  
Common Effect ◽  
Inference Methods ◽  
Conventional Effect ◽  
Estimate Effect Size

One of the fundamental concepts in meta-analysis is that of the effect size. An effect size is a statistical parameter that can be used to compare, on the same scale, the results of different studies in which a common effect of interest has been measured. This chapter describes the conventional effect sizes most commonly encountered in ecology and evolutionary biology, and the types of data associated with them. While choice of a specific measure of effect size may influence the interpretation of results, it does not influence the actual inference methods of meta-analysis. One critical point to remember is that one cannot combine different measures of effect size in a single meta-analysis: once you have chosen how you are going to estimate effect size, you need to use it for all of the studies to be analyzed.

M. Meta-Analysis

2021 ◽  
pp. 163-165
Author(s):  
Noémie Laurens
Keyword(s):  
Literature Review ◽  
Empirical Research ◽  
Effect Size ◽  
Meta Analysis ◽  
Research Synthesis ◽  
Effect Sizes ◽  
Primary Research ◽  
Unit Of Analysis ◽  
Research Studies

This chapter illustrates meta-analysis, which is a specific type of literature review, and more precisely a type of research synthesis, alongside traditional narrative reviews. Unlike in primary research, the unit of analysis of a meta-analysis is the results of individual studies. And unlike traditional reviews, meta-analysis only applies to: empirical research studies with quantitative findings hat are conceptually comparable and configured in similar statistical forms. What further distinguishes meta-analysis from other research syntheses is the method of synthesizing the results of studies — i.e. the use of statistics and, in particular, of effect sizes. An effect size represents the degree to which the phenomenon under study exists.

scholarly journals A-17 Category Test performance in individuals with alcohol versus methamphetamine dependence

2019 ◽  
Vol 34 (6) ◽  
pp. 876-876
Author(s):  
A Walker ◽  
A Hauson ◽  
S Sarkissians ◽  
A Pollard ◽  
C Flora-Tostado ◽  
...  
Keyword(s):  
Effect Size ◽  
Test Performance ◽  
Neuropsychological Functioning ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Significant Heterogeneity ◽  
Category Test ◽  
Methamphetamine Dependence ◽  
Significant Difference ◽  
Axis I

Abstract Objective The Category Test (CT) has consistently been found to be sensitive at detecting the effects of alcohol on the brain. However, this test has not been as widely used in examining the effects of methamphetamine. The current meta-analysis compared effect sizes of studies that have examined performance on the CT in alcohol versus methamphetamine dependent participants. Data selection Three researchers independently searched nine databases (e.g., PsycINFO, Pubmed, ProceedingsFirst), extracted required data, and calculated effect sizes. Inclusion criteria identified studies that had (a) compared alcohol or methamphetamine dependent groups to healthy controls and (b) matched groups on either age, education, or IQ (at least 2 out of 3). Studies were excluded if participants were reported to have Axis I diagnoses (other than alcohol or methamphetamine dependence) or comorbidities known to impact neuropsychological functioning. Sixteen articles were coded and analyzed for the current study. Data synthesis Alcohol studies showed a large effect size (g = 0.745, p < 0.001) while methamphetamine studies evidenced a moderate effect size (g = 0.406, p = 0.001); both without statistically significant heterogeneity (I2 = 0). Subgroup analysis revealed a statistically significant difference between the effect sizes from alcohol versus methamphetamine studies (Q-between = 5.647, p = 0.017). Conclusions The CT is sensitive to the effects of both alcohol and methamphetamine and should be considered when examining dependent patients who might exhibit problem solving, concept formation, and set loss difficulties in everyday living.

Test Frequency, Stakes, and Feedback in Student Achievement: A Meta-Analysis

2019 ◽  
Vol 43 (3-4) ◽  
pp. 111-151 ◽  
Author(s):  
Richard P. Phelps
Keyword(s):  
Student Achievement ◽  
Early Childhood ◽  
Effect Size ◽  
English Language ◽  
Variance Estimation ◽  
Meta Analysis ◽  
The United States ◽  
Research Literature ◽  
Effect Sizes ◽  
Test Frequency

Background: Test frequency, stakes associated with educational tests, and feedback from test results have been identified in the research literature as relevant factors in student achievement. Objectives: Summarize the separate and joint contribution to student achievement of these three treatments and their interactions via multivariable meta-analytic techniques using a database of English-language studies spanning a century (1910–2010), comprising 149 studies and 509 effect size estimates. Research design: Analysis employed robust variance estimation. Considered as potential moderators were hundreds of study features comprising various test designs and test administration, demographic, and source document characteristics. Subjects: Subjects were students at all levels, from early childhood to adult, mostly from the United States but also eight other countries. Results: We find a summary effect size of 0.84 for the three treatments collectively. Further analysis suggests benefits accrue to the incremental addition of combinations of testing and feedback or stakes and feedback. Moderator analysis shows higher effect sizes associated with the following study characteristics: more recent year of publication, summative (rather than formative) testing, constructed (rather than selected) item response formats, alignment of subject matter between pre- and posttests, and recognition/recall (rather than core subjects, art, or physical education). Conversely, lower effect sizes are associated with postsecondary students (rather than early childhood–upper secondary), special education population, larger study population, random assignment (rather than another sampling method), use of shadow test as outcome measure, designation of individuals (rather than groups) as units of analysis, and academic (rather than corporate or government) research.

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