An informed reference prior for between-study heterogeneity in meta-analyses of binary outcomes

2011 ◽  
Vol 30 (26) ◽  
pp. 3082-3094 ◽  
Author(s):  
Eleanor M. Pullenayegum
Keyword(s):  
Binary Outcomes ◽  
Reference Prior ◽  
Study Heterogeneity ◽  
Meta Analyses

scholarly journals Contribution of Informed Reference Prior Using Primary and Summary Measure Data in Bayesian Meta-Analysis

2015 ◽  
Vol 63 (2) ◽  
pp. 61-65
Author(s):  
Most Sifat Muntaha Soni ◽  
Md Belal Hossain
Keyword(s):  
Primary Outcome ◽  
Meta Analysis ◽  
Measure Data ◽  
Binary Outcomes ◽  
Reference Prior ◽  
Summary Measure ◽  
Prior Distributions ◽  
Study Heterogeneity ◽  
The Common

When meta-analysis includes a small number of trials, inferences are sensitive to the choice of prior distributions for between-study heterogeneity. The common practice is to use vague prior but inferences depend on the degree of vagueness. Pullenayegum (2011) proposed an informed reference prior for between-study heterogeneity of binary outcomes. We employ a model for applying this prior for both primary outcome and summary measure data in Bayesian meta-analysis. We have found the same inference using both primary outcome and summary measure data. This study also suggests that the informed reference prior for between-study heterogeneity represents more relevant conclusion as compared to commonly used prior distributions.Dhaka Univ. J. Sci. 63(2): 61-65, 2015 (July)

scholarly journals Beta-binomial models for meta-analysis with binary outcomes: Variations, extensions, and additional insights from econometrics

2021 ◽  
pp. 263208432199622
Author(s):  
Tim Mathes ◽  
Oliver Kuss
Keyword(s):  
Simulation Study ◽  
Count Data ◽  
Negative Binomial ◽  
Meta Analysis ◽  
Negative Binomial Regression ◽  
Binary Outcomes ◽  
Small Scale ◽  
Panel Count Data ◽  
Count Data Models ◽  
Meta Analyses

Background Meta-analysis of systematically reviewed studies on interventions is the cornerstone of evidence based medicine. In the following, we will introduce the common-beta beta-binomial (BB) model for meta-analysis with binary outcomes and elucidate its equivalence to panel count data models. Methods We present a variation of the standard “common-rho” BB (BBST model) for meta-analysis, namely a “common-beta” BB model. This model has an interesting connection to fixed-effect negative binomial regression models (FE-NegBin) for panel count data. Using this equivalence, it is possible to estimate an extension of the FE-NegBin with an additional multiplicative overdispersion term (RE-NegBin), while preserving a closed form likelihood. An advantage due to the connection to econometric models is, that the models can be easily implemented because “standard” statistical software for panel count data can be used. We illustrate the methods with two real-world example datasets. Furthermore, we show the results of a small-scale simulation study that compares the new models to the BBST. The input parameters of the simulation were informed by actually performed meta-analysis. Results In both example data sets, the NegBin, in particular the RE-NegBin showed a smaller effect and had narrower 95%-confidence intervals. In our simulation study, median bias was negligible for all methods, but the upper quartile for median bias suggested that BBST is most affected by positive bias. Regarding coverage probability, BBST and the RE-NegBin model outperformed the FE-NegBin model. Conclusion For meta-analyses with binary outcomes, the considered common-beta BB models may be valuable extensions to the family of BB models.

scholarly journals Probiotics in Pediatrics. A Review and Practical Guide

Nutrients ◽  
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.

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

BMC Medicine ◽  
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.

scholarly journals Estimates of Between-Study Heterogeneity for 705 Meta-Analyses Reported in Psychological Bulletin From 1990–2013

2017 ◽  
Vol 5 (1) ◽  
pp. 4 ◽  
Author(s):  
Sara Van Erp ◽  
Josine Verhagen ◽  
Raoul P. P. P. Grasman ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Psychological Bulletin ◽  
Study Heterogeneity ◽  
Meta Analyses

scholarly journals Non-pharmacological treatments for irritable bowel syndrome: study protocol of an umbrella review of systematic review and meta-analyses

BMJ Open ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. e027778 ◽  
Author(s):  
Song Jin ◽  
Yi-Fan Li ◽  
Di Qin ◽  
Dan-Qing Luo ◽  
Hong Guo ◽  
...  
Keyword(s):  
Irritable Bowel Syndrome ◽  
Meta Analysis ◽  
Small Study ◽  
Cochrane Central Register ◽  
Pharmacological Treatments ◽  
Umbrella Review ◽  
Study Heterogeneity ◽  
Ethical Approval ◽  
Irritable Bowel ◽  
Meta Analyses

IntroductionNon-pharmacological treatments are used in the management of irritable bowel syndrome, and their effectiveness has been evaluated in multiple meta-analyses. The robustness of the results in the meta-analyses was not evaluated. We aimed to assess whether there is evidence of diverse biases in the meta-analyses and to identify the treatments without evidence of risk of bias.Methods and analysisWe will search MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Web of Science and CINAHL Plus for meta-analyses that evaluate the effectiveness of non-pharmacological treatments. The time of publication will be limited from inception to December 2018. The credibility of the meta-analyses will be evaluated by assessing between-study heterogeneity, small-study effect and excess significance bias. The between-study heterogeneity will be assessed using the Cochrane’s Q test, and the extent of the heterogeneity will be classified using the I2statistics. The existence of a small-study effect in a meta-analysis will be evaluated using the funnel plot method and confirmed by Egger’s test. Excess significance bias will be evaluated by comparing the expected number of clinical studies with positive findings with the observed number.Ethics and disseminationNo formal ethical approval is required since we will use publicly available data. We will disseminate the findings of the umbrella review through publication in a peer-reviewed journal and conference presentations.PROSPERO registration numberCRD42018111516.

scholarly journals The impact of covariance priors on arm‐based Bayesian network meta‐analyses with binary outcomes

2020 ◽  
Vol 39 (22) ◽  
pp. 2883-2900 ◽  
Author(s):  
Zhenxun Wang ◽  
Lifeng Lin ◽  
James S. Hodges ◽  
Haitao Chu
Keyword(s):  
Bayesian Network ◽  
Binary Outcomes ◽  
Meta Analyses ◽  
The Impact

scholarly journals Empirical comparison of univariate and multivariate meta‐analyses in Cochrane Pregnancy and Childbirth reviews with multiple binary outcomes

2019 ◽  
Vol 10 (3) ◽  
pp. 440-451
Author(s):  
Malcolm J. Price ◽  
Helen A. Blake ◽  
Sara Kenyon ◽  
Ian R. White ◽  
Dan Jackson ◽  
...  
Keyword(s):  
Binary Outcomes ◽  
Empirical Comparison ◽  
Pregnancy And Childbirth ◽  
Meta Analyses

Meta-analysis: a critical appraisal of the methodology, benefits and drawbacks

2019 ◽  
Vol 80 (11) ◽  
pp. 636-641 ◽  
Author(s):  
Shahjahan Khan ◽  
Breda Memon ◽  
Muhammed A Memon
Keyword(s):  
Data Transfer ◽  
Meta Analysis ◽  
Summary Statistics ◽  
Level Of Evidence ◽  
Common Effect ◽  
Summary Effect ◽  
Study Heterogeneity ◽  
Decision Making Processes ◽  
Meta Analyses ◽  
Medical Disciplines

Meta-analysis has become an integral part of evidence-based decision-making processes and is being increasingly used in medical and non-medical disciplines. Aggregate data or summary statistics continue to be the mainstay of meta-analysis and are used by many professional societies to support clinical practice guidelines. Meta-analyses synthesize the summary statistics from independent trials by pooling them to estimate the underlying common effect size. The results represent the highest level of evidence but only if the chosen studies are of high quality and the selection criteria are fully satisfied. It is important to address the issues of defining an explicit and relevant question, exhaustively searching for the totality of evidence, meticulous and unbiased data transfer or extraction, assessment of between study heterogeneity and the use of appropriate statistical methods for estimating summary effect measures. This article reviews the methodology, benefits and drawbacks of performing a meta-analysis.

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