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

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.

scholarly journals Health-Related Quality of Life in Athletes: A Systematic Review With Meta-Analysis

2016 ◽  
Vol 51 (6) ◽  
pp. 442-453 ◽  
Author(s):  
Megan N. Houston ◽  
Matthew C. Hoch ◽  
Johanna M. Hoch
Keyword(s):  
Quality Of Life ◽  
Confidence Interval ◽  
Random Effects ◽  
Meta Analysis ◽  
Collegiate Athletes ◽  
Health Related Quality ◽  
Related Quality ◽  
Health Related ◽  
History Of

Context: Assessment of health-related quality of life (HRQOL) after injury is important. Differences in HRQOL between nonathletes and athletes and between injured and uninjured athletes have been demonstrated; however, the evidence has not been synthesized. Objective: To answer the following questions: (1) Does HRQOL differ among adolescent and collegiate athletes and nonathletes? (2) Does HRQOL differ between injured adolescent and collegiate athletes or between athletes with a history of injury and uninjured athletes or those without a history of injury? Data Sources: We systematically searched CINAHL, MEDLINE, SPORTDiscus, and PubMed. A hand search of references was also conducted. Study Selection: Studies were included if they used generic instruments to compare HRQOL outcomes between athletes and nonathletes and between uninjured and injured athletes. Studies were excluded if they did not use a generic instrument, pertained to instrument development, or included retired athletes or athletes with a chronic disease. Data Extraction: We assessed study quality using the modified Downs and Black Index Tool. Bias-corrected Hedges g effect sizes and 95% confidence intervals (CIs) were calculated. The Strength of Recommendation Taxonomy (SORT) was used to determine the overall strength of the recommendation. A random-effects meta-analysis was performed for all studies using the composite or total score. Data Synthesis: Eight studies with modified Downs and Black scores ranging from 70.6% to 88.4% were included. For question 1, the overall random-effects meta-analysis was weak (effect size = 0.27, 95% confidence interval = 0.14, 0.40; P < .001). For question 2, the overall random-effects meta-analysis was moderate (effect size = 0.68, 95% confidence interval = 0.42, 0.95; P < .001). Conclusions: Grade A evidence indicates that athletes reported better HRQOL than nonathletes and that uninjured athletes reported better HRQOL than injured athletes. However, the overall effect for question 1 was weak, suggesting that the differences between athletes and nonathletes may not be clinically meaningful. Clinicians should monitor HRQOL after injury to ensure that all dimensions of health are appropriately treated.

scholarly journals Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease

2019 ◽  
Author(s):  
Lerato E Magosi ◽  
Anuj Goel ◽  
Jemma C Hopewell ◽  
Martin Farrall
Keyword(s):  
Random Effects ◽  
Complex Traits ◽  
Large Scale ◽  
Meta Analysis ◽  
Genetic Effect ◽  
High Sensitivity ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Supplementary Information ◽  
Genetic Associations

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.

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

2010 ◽  
Vol 1 (2) ◽  
pp. 97-111 ◽  
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

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

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.

scholarly journals Effect of surface treatment on the mechanical stability of orthodontic miniscrews:

2021 ◽  
Author(s):  
Yousef Al-Thomali ◽  
Sakeenabi Basha ◽  
Roshan Noor Mohamed
Keyword(s):  
Confidence Interval ◽  
Surface Treatment ◽  
Mechanical Stability ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Mean Difference ◽  
Forest Plot ◽  
Total N ◽  
Standard Mean Difference ◽  
Effect Of Surface

ABSTRACT Objectives To provide collective quantitative evidence about the effect of surface treatments on the mechanical stability of orthodontic miniscrews (MSs). Materials and Methods The study was registered in PROSPERO (No. CRD42020209652). The research question was defined according to the PICO (population, intervention, control, and outcomes) format. Various research databases were searched for animal and human studies on effects of surface treatment on the mechanical stability of MSs. Both prospective and retrospective in vivo clinical studies published in English were included. The risk of bias was assessed using SYRCLE's risk of bias tool for animal studies. The meta-analysis was conducted using RevMan 5.4. Results A total of 109 articles were identified; 14 were included in the systematic review, and seven studies with sandblasting, acid etching (SLA) methods of surface treatment were included for meta-analysis. The number of study participants ranged from 6 to 24 (total n = 185), with a mean of 13.2. A total of 949 MSs were used with a mean of 67.8. The overall success rate for surface-treated MSs ranged from 47.9% to 100%. Forest plot of removal torque values showed significantly higher values for SLA surface-treated MSs compared with controls with a standard mean difference of 2.61 (95% confidence interval = 1.49–3.72, I2 = 85%). Forest plot of insertion torque showed a standard mean difference of –6.19 (95% confidence interval = –13.63–1.25, I2 = 98%, P = .10). Conclusions Surface treatment of MSs improved primary and secondary stability with good osseointegration at the bone-implant surface. However, significant heterogeneity across the studies included in the meta-analysis made it difficult to draw conclusions.

scholarly journals 325The incidence of primary postpartum haemorrhage following in-hospital birth: a systematic review and meta-analysis

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Bewket Tadesse Tiruneh ◽  
Gayle McLelland ◽  
Virginia Plummer
Keyword(s):  
Systematic Review ◽  
Confidence Interval ◽  
Random Effects ◽  
Postpartum Haemorrhage ◽  
Meta Analysis ◽  
Random Effects Model ◽  
Preventive Strategies ◽  
Heterogeneity Test ◽  
Ovid Medline ◽  
Hospital Births

Abstract Background Although primary postpartum haemorrhage is a leading cause of maternal mortality, in the wider literature, there is inconsistent results in studies about its incidence. This study was designed to determine the pooled incidence of primary postpartum haemorrhage among women following in-hospital births. Methods This review used a systematic review and meta-analysis approach. We systematically searched electronic databases of Ovid MEDLINE, Ovid Emcare, Embase, PsycINFO, and CINAHL. Studies reporting the incidence of primary postpartum haemorrhage following in-hospital births were included. The pooled incidence of primary postpartum haemorrhage with a 95% confidence interval was calculated using random-effects model. Heterogeneity test between studies, and publication bias were assessed with I-square statistics and Egger’s test respectively. Results Ten of the 1548 studies were found to be eligible and were included in this systematic review and meta-analysis. The pooled incidence of primary postpartum haemorrhage following in-hospital births was nearly 12% (95% CI: 7.74- 17.61), with no evidence of between studies heterogeneity. Conclusions The incidence of primary postpartum haemorrhage following in-hospital births was high, and suggest that preventive strategies implemented to reduce its occurrence needs further strengthening using training. Key messages The result of this review suggests that globally at least one in ten women experience a primary postpartum haemorrhage following in-hospital births. This is higher than anticipated. The application of the recommended strategies for the prevention of primary postpartum haemorrhage should be re-emphasized.

Bias and Precision of Alternate Estimators in Meta-Analysis: Benefits of Blending Schmidt-Hunter and Hedges Approaches

2017 ◽  
Vol 22 (2) ◽  
pp. 490-514 ◽  
Author(s):  
Michael T. Brannick ◽  
Sean M. Potter ◽  
Bryan Benitez ◽  
Scott B. Morris
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Interval ◽  
Root Mean Square Error ◽  
Random Effects ◽  
Meta Analysis ◽  
Size Distributions ◽  
Mean Square ◽  
The Mean

We describe a new estimator (labeled Morris) for meta-analysis. The Morris estimator combines elements of both the Schmidt-Hunter and Hedges estimators. The new estimator is compared to (a) the Schmidt-Hunter estimator, (b) the Schmidt-Hunter estimator with variance correction for the number of studies (“ k correction”), (c) the Hedges random-effects estimator, and (d) the Bonett unit weights estimator in a Monte Carlo simulation. The simulation was designed to represent realistic conditions faced by researchers, including population random-effects distributions, numbers of studies, and skewed sample size distributions. The simulation was used to evaluate the estimators with respect to bias, coverage of the 95% confidence interval of the mean, and root mean square error of estimates of the population mean. We also evaluated the quality of credibility intervals. Overall, the new estimator provides better coverage and slightly better credibility values than other commonly used methods. Thus it has advantages of both commonly used approaches without the apparent disadvantages. The new estimator can be implemented easily with existing software; software used in the study is available online, and an example is included in the appendix in the Supplemental Material available online.

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

2014 ◽  
Vol 17 (2) ◽  
pp. 64-64 ◽  
Author(s):  
Adriani Nikolakopoulou ◽  
Dimitris Mavridis ◽  
Georgia Salanti
Keyword(s):  
Random Effects ◽  
Meta Analysis ◽  
Fixed Effect ◽  
Meta Analyses ◽  
Analysis Models

scholarly journals A Primer on Bayesian Model-Averaged Meta-Analysis

2020 ◽  
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.

scholarly journals Do Various Treatment Modalities of Vesicoureteral Reflux Have Any Adverse Effects in Pediatric Patients? A Meta-Analysis

2021 ◽  
pp. 1-9
Author(s):  
Min Xie ◽  
Xiaogai Xu ◽  
Zhenjie Cao ◽  
Huijie Xiao
Keyword(s):  
Vesicoureteral Reflux ◽  
Random Effects ◽  
Renal Scarring ◽  
Meta Analysis ◽  
Forest Plot ◽  
Controlled Trials ◽  
Cochrane Central Register ◽  
Random Effects Model ◽  
Recurrent Utis ◽  
Tract Infections

<b><i>Purpose:</i></b> Vesicoureteral reflux (VUR) is a risk factor for various renal problems like recurrent urinary tract infections (UTIs), pyelonephritis, renal scarring, hypertension, and other renal parenchymal defects. The interventions followed by pediatricians include low-dose antibiotic treatment, surgical correction, and endoscopy. This meta-analysis aimed to assess the advantages and drawbacks of various primary VUR treatment options. <b><i>Search Strategy:</i></b> The Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, reference lists of journals, and abstracts from conference proceedings were all used to find randomized controlled trials. The articles were retrieved from 1985 till 2020. Twenty articles were used for the data analysis. <b><i>Criteria for Selection:</i></b> Surgery, long-term antibiotic prophylaxis, noninvasive techniques, and any mix of therapies are also options for treating VUR. <b><i>Collection and Interpretation of Data:</i></b> Two authors searched the literature separately, determining research qualifications, assessing accuracy, and extracting and entering results. The odds ratio (OR) of these studies was used to construct the forest plot. The random-effects model was used to pool the data. Also, the random-effects model was used with statistical significance at a <i>p</i> value &#x3c; 0.05 to assess the difference in side effects after treatment of VUR using different modalities. <b><i>Results:</i></b> We found no statistically significant differences between surgery plus antibiotics and antibiotic alone-treated patients in terms of recurrent UTIs (OR = 0.581; 95% confidence interval [CI] 0.259–1.30), renal parenchymal defects (OR = 1.149; 95% CI 0.75–1.754), and renal scarring (OR = 1.042; 95% CI 0.72–1.50). However, the risk of developing pyelonephritis after surgical treatment of VUR was lesser than that in the conservative approach, that is, antibiotics (OR = 0.345; 95% CI 0.126–0.946.), positive urine culture (OR = 0.617; 95% CI 0.428–0.890), and recurrent UTIs were more common in the placebo group than in the antibiotic group (<i>p</i> &#x3c; 0.05; OR = 0.639; 95% CI 0.436–0.936) which is statistically significant. <b><i>Conclusion:</i></b> Based on current research, we recommend that a child with a UTI and significant VUR be treated conservatively at first, with surgical care reserved for children who have issues with antimicrobials or have clinically significant VUR that persists after several years of follow-up.

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