Use of quality control charts for detection of outliers and temporal trends in cumulative meta-analysis

Impact of Soccer-Based Training Interventions on Anthropometric Measures Among Children and Adolescents With Overweight/Obesity: A Systematic Review, Meta-Analysis, and Assessment of Certainty of Evidence

PURPOSE

This systematic review and meta-analysis aimed to systematically examine and summarize recent evidence on the effects of soccer-based training (SBT) on anthropometric measures in children and adolescents with overweight/obesity.

METHODS

Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 guidelines, a thorough literature search across 7 electronic databases was conducted on October 11, 2023. The studies' methodological quality was evaluated using the QualSyst tool, followed by conducting a meta-analysis with a random-effects model, and the certainty of evidence was assessed.

RESULTS

Six studies were included, with 4 studies of strong methodological quality and 2 studies of moderate methodological quality. The results of the meta-analysis revealed SBT decreases fat mass percentage (effect size [ES] = 0.47 [small]; P = .002), with no significant effect of SBT on body mass index (ES = 0.180 [small]; P = .275), body mass (ES = 0.183 [trivial]; P = .212), fat-free mass (ES = 0.074 [trivial]; P = .635), or waist circumference (ES = 0.358 [small]; P = .053). The certainty of evidence was moderate for all outcomes.

CONCLUSIONS

SBT appears to decrease fat mass percentage, without affecting body mass, body mass index, fat-free mass, or waist circumference in obese/overweight children and adolescents. These findings require further investigation given the moderate certainty of evidence.

REGISTRATION

The protocol of this review was registered in the Open Science Framework database (https://doi.org/10.17605/OSF.IO/8P4V2).

Heterogeneity in meta-analysis: a comprehensive overview

In recent years, meta-analysis has evolved to a critically important field of Statistics, and has significant applications in Medicine and Health Sciences. In this work we briefly present existing methodologies to conduct meta-analysis along with any discussion and recent developments accompanying them. Undoubtedly, studies brought together in a systematic review will differ in one way or another. This yields a considerable amount of variability, any kind of which may be termed heterogeneity. To this end, reports of meta-analyses commonly present a statistical test of heterogeneity when attempting to establish whether the included studies are indeed similar in terms of the reported output or not. We intend to provide an overview of the topic, discuss the potential sources of heterogeneity commonly met in the literature and provide useful guidelines on how to address this issue and to detect heterogeneity. Moreover, we review the recent developments in the Bayesian approach along with the various graphical tools and statistical software that are currently available to the analyst. In addition, we discuss sensitivity analysis issues and other approaches of understanding the causes of heterogeneity. Finally, we explore heterogeneity in meta-analysis for time to event data in a nutshell, pointing out its unique characteristics.

Acute Effects of Caffeine Supplementation on Physical Performance, Physiological Responses, Perceived Exertion, and Technical-Tactical Skills in Combat Sports: A Systematic Review and Meta-Analysis

Although the effects of caffeine supplementation on combat sports performance have been extensively investigated, there is currently no consensus regarding its ergogenic benefits.This systematic review with meta-analysis aimed to summarize the studies investigating the effects of caffeine supplementation on different aspects of performance in combat sports and to quantitatively analyze the results of these studies to better understand the ergogenic effect of caffeine on combat sports outcomes. A systematic search for randomized placebo-controlled studies investigating the effects of caffeine supplementation on combat sports' performance was performed through Scopus, Pubmed, Web of Science and Cochrane Library databases up to 18 April 2022. Random-effects meta-analyses of standardized mean differences (Hedge's g) were performed to analyze the data. Twenty-six studies of good and excellent methodological quality (based on the Pedro scale) fulfilled the inclusion criteria. The meta-analysis results revealed caffeine has a small but evident effect size (ES) on handgrip strength (ES = 0.28; 95% CI: 0.04 to 0.52; p = 0.02), and total number of throws during the special judo fitness test (SJFT) (ES = 0.42; 95% CI: 0.06 to 0.78; p = 0.02). Regarding the physiological responses, caffeine increased blood lactate concentration ([La]) in anaerobic exercise (ES = 1.23; 95% CI: 0.29 to 2.18; p = 0.01) and simulated combat (ES = 0.91; 95% CI: 0.34 to 1.47; p = 0.002). For Heart Rate (HR), caffeine increased HR final (ES = 0.31; 95% CI: 0.11 to 0.52; p = 0.003), and HR 1min (ES = 0.20; 95% CI 0.004 to 0.40; p = 0.045). However, caffeine had no impact on the countermovement jump height, the SJFT index, the judogi strength-endurance test, the number and duration of offensive actions, HR at the end of the fight, and the rating of perceived exertion. Caffeine supplementation may be ergogenic for a range of combat sports aspects involving isometric strength, anaerobic power, reaction time, and anaerobic metabolism. However, supplementation effects might be ineffective under certain circumstances, indicating supplementation needs to take into account the performance metric in question prior to creating a dosing protocol.

Cumulative meta-analysis: What works

To present time-varying evidence, cumulative meta-analysis (CMA) updates results of previous meta-analyses to incorporate new study results. We investigate the properties of CMA, suggest possible improvements and provide the first in-depth simulation study of the use of CMA and CUSUM methods for detection of temporal trends in random-effects meta-analysis. We use the standardized mean difference (SMD) as an effect measure of interest. For CMA, we compare the standard inverse-variance-weighted estimation of the overall effect using REML-based estimation of between-study variance τ 2 with the sample-size-weighted estimation of the effect accompanied by Kulinskaya-Dollinger-Bjørkestøl (Biometrics. 2011; 67:203-212) (KDB) estimation of τ 2 . For all methods, we consider Type 1 error under no shift and power under a shift in the mean in the random-effects model. To ameliorate the lack of power in CMA, we introduce two-stage CMA, in which τ 2 is estimated at Stage 1 (from the first 5-10 studies), and further CMA monitors a target value of effect, keeping the τ 2 value fixed. We recommend this two-stage CMA combined with cumulative testing for positive shift in τ 2 . In practice, use of CMA requires at least 15-20 studies.

Association between PPAR-γ2 gene polymorphisms and diabetic retinopathy risk: a meta-analysis

A close association between peroxisome proliferator-activated receptor-γ2 (PPAR-γ2) and the development of diabetic retinopathy (DR) has been previously suggested. Herein, a meta-analysis was conducted to explore the association between PPAR-γ2 polymorphisms and DR risk by performing a systematic search and quantitative analysis. Overall, fourteen articles involving 10,527 subjects were included. The pooled results did not reveal an association between PPAR-γ2 rs1801282 C/G and DR susceptibility in the overall population (e.g., the dominant model: CG+GG vs. CC, OR=0.85, 95% CI=0.69-1.06, P=0.15, I²=62.9%). Furthermore, heterogeneity tests, cumulative analyses, sensitivity analyses, and publication bias analyses were conducted and showed that the results were robust. Similarly, race-based subgroup analyses and other subgroup analyses did not reveal an association between the rs1801282 C/G and DR susceptibility. In addition, no significant association was observed between PPAR-γ2 rs3856806 C/T polymorphism and DR risk (e.g., the dominant model: CT+TT vs. CC, OR=1.12, 95%CI=0.91-1.37, P=0.28, I²=27.0%). Overall, based on the current sample size and the level of evidence presented in the study, the results suggest that PPAR-γ2 gene polymorphisms are not associated with DR risk.

Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis

BACKGROUND

Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable.

METHODS

We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes.

RESULTS

We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features.

CONCLUSIONS

The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.

A large-scale assessment of temporal trends in meta-analyses using systematic review reports from the Cochrane Library

INTRODUCTION

Previous studies suggest that many systematic reviews contain meta-analyses that display temporal trends, such as the first study's result being more extreme than later studies' or a drift in the pooled estimate. We assessed the extent and characteristics of temporal trends using all Cochrane intervention reports published 2008-2012.

METHODS

We selected the largest meta-analysis within each report and analysed trends using methods including a Z-test (first versus subsequent estimates); generalised least squares; and cumulative sum charts. Predictors considered include meta-analysis size and review group.

RESULTS

Of 1288 meta-analyses containing at least 4 studies, the point estimate from the first study was more extreme and in the same direction as the pooled estimate in 738 (57%), with a statistically significant difference (first versus subsequent) in 165 (13%). Generalised least squares indicated trends in 717 (56%); 18% of fixed effects analyses had at least one violation of cumulative sum limits. For some methods, meta-analysis size was associated with temporal patterns and use of a random effects model, but there was no consistent association with review group.

CONCLUSIONS

All results suggest that more meta-analyses demonstrate temporal patterns than would be expected by chance. Hence, assuming the standard meta-analysis model without temporal trend is sometimes inappropriate. Factors associated with trends are likely to be context specific.

Sequential change detection and monitoring of temporal trends in random-effects meta-analysis

Temporal changes in magnitude of effect sizes reported in many areas of research are a threat to the credibility of the results and conclusions of meta-analysis. Numerous sequential methods for meta-analysis have been proposed to detect changes and monitor trends in effect sizes so that meta-analysis can be updated when necessary and interpreted based on the time it was conducted. The difficulties of sequential meta-analysis under the random-effects model are caused by dependencies in increments introduced by the estimation of the heterogeneity parameter τ2 . In this paper, we propose the use of a retrospective cumulative sum (CUSUM)-type test with bootstrap critical values. This method allows retrospective analysis of the past trajectory of cumulative effects in random-effects meta-analysis and its visualization on a chart similar to CUSUM chart. Simulation results show that the new method demonstrates good control of Type I error regardless of the number or size of the studies and the amount of heterogeneity. Application of the new method is illustrated on two examples of medical meta-analyses. © 2016 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.