Incorporating Genuine Prior Information about Between-Study Heterogeneity in Random Effects Pairwise and Network Meta-analyses

Abstract. Background: In recent years, the rate of death by suicide has been increasing disproportionately among females and young adults in the United States. Presumably this trend has been mirrored by the proportion of individuals with suicidal ideation who attempted suicide. Aim: We aimed to investigate whether the proportion of individuals in the United States with suicidal ideation who attempted suicide differed by age and/or sex, and whether this proportion has increased over time. Method: Individual-level data from the National Survey on Drug Use and Health (NSDUH), 2008–2017, were used to estimate the year-, age category-, and sex-specific proportion of individuals with past-year suicidal ideation who attempted suicide. We then determined whether this proportion differed by age category, sex, and across years using random-effects meta-regression. Overall, age category- and sex-specific proportions across survey years were estimated using random-effects meta-analyses. Results: Although the proportion was found to be significantly higher among females and those aged 18–25 years, it had not significantly increased over the past 10 years. Limitations: Data were self-reported and restricted to past-year suicidal ideation and suicide attempts. Conclusion: The increase in the death by suicide rate in the United States over the past 10 years was not mirrored by the proportion of individuals with past-year suicidal ideation who attempted suicide during this period.

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Association of bisphenol A with puberty timing: a meta-analysis

Reviews on Environmental Health ◽

10.1515/reveh-2020-0091 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Hui Meng ◽

Yunping Zhou ◽

Yunxia Jiang

Keyword(s):

Bisphenol A ◽

Confidence Intervals ◽

Random Effects ◽

Strong Correlation ◽

Fixed Effects ◽

Meta Analysis ◽

Random Effects Models ◽

Study Heterogeneity ◽

The Relationship ◽

Prevalence Ratios

AbstractObjectivesThe results of existing studies on bisphenol A (BPA) and puberty timing did not reach a consensus. Thereby we performed this meta-analytic study to explore the association between BPA exposure in urine and puberty timing.MethodsMeta-analysis of the pooled odds ratios (OR), prevalence ratios (PR) or hazards ratios (HR) with 95% confidence intervals (CI) were calculated and estimated using fixed-effects or random-effects models based on between-study heterogeneity.ResultsA total of 10 studies involving 5621 subjects were finally included. The meta-analysis showed that BPA exposure was weakly associated with thelarche (PR: 0.96, 95% CI: 0.93–0.99), while no association was found between BPA exposure and menarche (HR: 0.99, 95% CI: 0.89–1.12; OR: 1.02, 95% CI: 0.73–1.43), and pubarche (OR: 1.00, 95% CI: 0.79–1.26; PR: 1.00, 95% CI: 0.95–1.05).ConclusionsThere was no strong correlation between BPA exposure and puberty timing. Further studies with large sample sizes are needed to verify the relationship between BPA and puberty timing.

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Probiotics in Pediatrics. A Review and Practical Guide

Nutrients ◽

10.3390/nu13072176 ◽

2021 ◽

Vol 13 (7) ◽

pp. 2176

Author(s):

Leontien Depoorter ◽

Yvan Vandenplas

Keyword(s):

Health Benefit ◽

Beneficial Effects ◽

Treatment Regimens ◽

Clostridioides Difficile ◽

Nosocomial Diarrhea ◽

Infectious Gastroenteritis ◽

Study Heterogeneity ◽

Probiotic Strains ◽

Prophylactic Use ◽

Meta Analyses

The potential benefit of the administration of probiotics in children has been studied in many settings globally. Probiotics products contain viable micro-organisms that confer a health benefit on the host. Beneficial effects of selected probiotic strains for the management or prevention of selected pediatric conditions have been demonstrated. The purpose of this paper is to provide an overview of current available evidence on the efficacy of specific probiotics in selected conditions to guide pediatricians in decision-making on the therapeutic or prophylactic use of probiotic strains in children. Evidence to support the use of certain probiotics in selected pediatric conditions is often available. In addition, the administration of probiotics is associated with a low risk of adverse events and is generally well tolerated. The best documented efficacy of certain probiotics is for treatment of infectious gastroenteritis, and prevention of antibiotic-associated, Clostridioides difficile-associated and nosocomial diarrhea. Unfortunately, due to study heterogeneity and in some cases high risk of bias in published studies, a broad consensus is lacking for specific probiotic strains, doses and treatment regimens for some pediatric indications. The current available evidence thus limits the systematic administration of probiotics. The most recent meta-analyses and reviews highlight the need for more well-designed, properly powered, strain-specific and dedicated-dose response studies.

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Validity of observational evidence on putative risk and protective factors: appraisal of 3744 meta-analyses on 57 topics

BMC Medicine ◽

10.1186/s12916-021-02020-6 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Perrine Janiaud ◽

Arnav Agarwal ◽

Ioanna Tzoulaki ◽

Evropi Theodoratou ◽

Konstantinos K. Tsilidis ◽

...

Keyword(s):

Protective Factors ◽

Observational Studies ◽

Prediction Interval ◽

Statistical Significance ◽

Median Number ◽

Small Study ◽

Study Heterogeneity ◽

Large Heterogeneity ◽

Meta Analyses ◽

No Gold Standard

Abstract Background The validity of observational studies and their meta-analyses is contested. Here, we aimed to appraise thousands of meta-analyses of observational studies using a pre-specified set of quantitative criteria that assess the significance, amount, consistency, and bias of the evidence. We also aimed to compare results from meta-analyses of observational studies against meta-analyses of randomized controlled trials (RCTs) and Mendelian randomization (MR) studies. Methods We retrieved from PubMed (last update, November 19, 2020) umbrella reviews including meta-analyses of observational studies assessing putative risk or protective factors, regardless of the nature of the exposure and health outcome. We extracted information on 7 quantitative criteria that reflect the level of statistical support, the amount of data, the consistency across different studies, and hints pointing to potential bias. These criteria were level of statistical significance (pre-categorized according to 10−6, 0.001, and 0.05 p-value thresholds), sample size, statistical significance for the largest study, 95% prediction intervals, between-study heterogeneity, and the results of tests for small study effects and for excess significance. Results 3744 associations (in 57 umbrella reviews) assessed by a median number of 7 (interquartile range 4 to 11) observational studies were eligible. Most associations were statistically significant at P < 0.05 (61.1%, 2289/3744). Only 2.6% of associations had P < 10−6, ≥1000 cases (or ≥20,000 participants for continuous factors), P < 0.05 in the largest study, 95% prediction interval excluding the null, and no large between-study heterogeneity, small study effects, or excess significance. Across the 57 topics, large heterogeneity was observed in the proportion of associations fulfilling various quantitative criteria. The quantitative criteria were mostly independent from one another. Across 62 associations assessed in both RCTs and in observational studies, 37.1% had effect estimates in opposite directions and 43.5% had effect estimates differing beyond chance in the two designs. Across 94 comparisons assessed in both MR and observational studies, such discrepancies occurred in 30.8% and 54.7%, respectively. Conclusions Acknowledging that no gold-standard exists to judge whether an observational association is genuine, statistically significant results are common in observational studies, but they are rarely convincing or corroborated by randomized evidence.

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

Clinical Trials ◽

10.1177/1740774512453218 ◽

2012 ◽

Vol 9 (5) ◽

pp. 610-620 ◽

Cited By ~ 28

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.

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Alternative Tendon Coaptations to the Pulvertaft Weave Technique: A Systematic Review and Meta-Analysis of Biomechanical Studies

Hand ◽

10.1177/15589447211043213 ◽

2021 ◽

pp. 155894472110432

Author(s):

Emily M. Graham ◽

Jeremie D. Oliver ◽

Russell Hendrycks ◽

Dino Maglic ◽

Shaun D. Mendenhall

Keyword(s):

Systematic Review ◽

Yield Strength ◽

Random Effects ◽

Meta Analysis ◽

Dichotomous Data ◽

Inverse Variance ◽

Significant Difference ◽

Alternative Techniques ◽

Meta Analyses

Background The Pulvertaft weave technique (PT) is frequently used during tendon repairs and transfers. However, this technique is associated with limitations. In this systematic review and meta-analysis, quantitative and qualitative analyses were performed on in vitro, biomechanical studies that compared the PT with alternative techniques. Methods Articles included for qualitative and/or qualitative analysis were identified following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies included in the meta-analysis were analyzed either as continuous data with inverse variance and random effects or as dichotomous data using a Mantel-Haenszel analysis assuming random effects to calculate an odds ratio. Results A comprehensive electronic search yielded 8 studies meeting inclusion criteria for meta-analysis. Two studies with a total of 65 tendon coaptations demonstrated no significant difference in strength between the PT and traditional side-to-side (STS) techniques ( P = .92). Two studies with a total of 43 tendon coaptations showed that the STS with 1 weave has a higher yield strength than the PT ( P = .03). Two studies with a total of 62 tendon repairs demonstrated no significant difference in strength between the PT and the step-cut (SC) techniques ( P = .70). The final 2 studies included 46 tendon repairs and demonstrated that the wrap around (WA) technique has a higher yield strength than the PT ( P < .001). Conclusions The STS, SC, and WA techniques are preferred for improving tendon form. The STS and WA techniques have superior yield strengths than the PT, and the SC technique withstands similar stress to failure as the PT.

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Exact inference on the random‐effects model for meta‐analyses with few studies

Biometrics ◽

10.1111/biom.12998 ◽

2019 ◽

Vol 75 (2) ◽

pp. 485-493

Author(s):

Haben Michael ◽

Suzanne Thornton ◽

Minge Xie ◽

Lu Tian

Keyword(s):

Random Effects ◽

Random Effects Model ◽

Exact Inference ◽

Meta Analyses

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Interval estimation of the overall treatment effect in random-effects meta-analyses: Recommendations from a simulation study comparing frequentist, Bayesian, and bootstrap methods

10.31219/osf.io/5zbh6 ◽

2020 ◽

Author(s):

Frank Weber ◽

Guido Knapp ◽

Anne Glass ◽

Günther Kundt ◽

Katja Ickstadt

Keyword(s):

Literature Review ◽

Random Effects ◽

Simulation Study ◽

Treatment Effect ◽

Profile Likelihood ◽

Meta Analysis ◽

Artery Bypass ◽

Interval Length ◽

Interval Estimators ◽

Meta Analyses

There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study is still lacking. Thus, we conduct such a simulation study for continuous and binary outcomes, focusing on the medical field for application.Based on the literature review and some new theoretical considerations, a practicable number of interval estimators is selected for this comparison: the classical normal-approximation interval using the DerSimonian-Laird heterogeneity estimator, the HKSJ interval using either the Paule-Mandel or the Sidik-Jonkman heterogeneity estimator, the Skovgaard higher-order profile likelihood interval, a parametric bootstrap interval, and a Bayesian interval using different priors. We evaluate the performance measures (coverage and interval length) at specific points in the parameter space, i.e. not averaging over a prior distribution. In this sense, our study is conducted from a frequentist point of view.We confirm the main finding of the literature review, the general recommendation of the HKSJ method (here with the Sidik-Jonkman heterogeneity estimator). For meta-analyses including only 2 studies, the high length of the HKSJ interval limits its practical usage. In this case, the Bayesian interval using a weakly informative prior for the heterogeneity may help. Our recommendations are illustrated using a real-world meta-analysis dealing with the efficacy of an intramyocardial bone marrow stem cell transplantation during coronary artery bypass grafting.

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