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Article Alert

The free Article Alert service delivers a weekly email to your inbox containing the most recently published articles on all aspects of systematic review and comparative effectiveness review methodologies.

  • Medical, psychological, educational, etc., methodology research literatures covered
  • Curated by our seasoned research staff from a wide array of sources: PubMed, journal table of contents, author alerts, bibliographies, and prominent international methodology and grey literature Web sites
  • Averages 20 citations/week (pertinent citations screened from more than 1,500 citations weekly)
  • Saves you time AND keeps you up to date on the latest research

Article Alert records include:

  • Citation information/abstract
  • Links: PMID (PubMed ID) and DOI (Digital Object Identifier)
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The Article Alert for the week of January 26, 2015 (sample articles)

Katikireddi SV, Egan M, Petticrew M. How do systematic reviews incorporate risk of bias assessments into the synthesis of evidence? A methodological study. J.Epidemiol.Community Health. 2015 Feb;69(2):189-95. PMID: 25481532.

Background: Systematic reviews (SRs) are expected to critically appraise included studies and privilege those at lowest risk of bias (RoB) in the synthesis. This study examines if and how critical appraisals inform the synthesis and interpretation of evidence in SRs.
Methods: All SRs published in March-May 2012 in 14 high-ranked medical journals and a sample from the Cochrane library were systematically assessed by two reviewers to determine if and how: critical appraisal was conducted; RoB was summarised at study, domain and review levels; and RoB appraisals informed the synthesis process.
Results: Of the 59 SRs studied, all except six (90%) conducted a critical appraisal of the included studies, with most using or adapting existing tools. Almost half of the SRs reported critical appraisal in a manner that did not allow readers to determine which studies included in a review were most robust. RoB assessments were not incorporated into synthesis in one-third (20) of the SRs, with their consideration more likely when reviews focused on randomised controlled trials. Common methods for incorporating critical appraisals into the synthesis process were sensitivity analysis, narrative discussion and exclusion of studies at high RoB. Nearly half of the reviews which investigated multiple outcomes and carried out study-level RoB summaries did not consider the potential for RoB to vary across outcomes.
Conclusions: The conclusions of the SRs, published in major journals, are frequently uninformed by the critical appraisal process, even when conducted. This may be particularly problematic for SRs of public health topics that often draw on diverse study designs.
Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to


Brown S, Hutton B, Clifford T, Coyle D, Grima D, Wells G, Cameron C. A Microsoft-Excel-based tool for running and critically appraising network meta-analyses--an overview and application of NetMetaXL. Syst.Rev. 2014 Sep 29;3(1):110. PMID: 25267416.

Background: The use of network meta-analysis has increased dramatically in recent years. WinBUGS, a freely available Bayesian software package, has been the most widely used software package to conduct network meta-analyses. However, the learning curve for WinBUGS can be daunting, especially for new users. Furthermore, critical appraisal of network meta-analyses conducted in WinBUGS can be challenging given its limited data manipulation capabilities and the fact that generation of graphical output from network meta-analyses often relies on different software packages than the analyses themselves.
Methods: We developed a freely available Microsoft-Excel-based tool called NetMetaXL, programmed in Visual Basic for Applications, which provides an interface for conducting a Bayesian network meta-analysis using WinBUGS from within Microsoft Excel. . This tool allows the user to easily prepare and enter data, set model assumptions, and run the network meta-analysis, with results being automatically displayed in an Excel spreadsheet. It also contains macros that use NetMetaXL's interface to generate evidence network diagrams, forest plots, league tables of pairwise comparisons, probability plots (rankograms), and inconsistency plots within Microsoft Excel. All figures generated are publication quality, thereby increasing the efficiency of knowledge transfer and manuscript preparation.
Results: We demonstrate the application of NetMetaXL using data from a network meta-analysis published previously which compares combined resynchronization and implantable defibrillator therapy in left ventricular dysfunction. We replicate results from the previous publication while demonstrating result summaries generated by the software.
Conclusions: Use of the freely available NetMetaXL successfully demonstrated its ability to make running network meta-analyses more accessible to novice WinBUGS users by allowing analyses to be conducted entirely within Microsoft Excel. NetMetaXL also allows for more efficient and transparent critical appraisal of network meta-analyses, enhanced standardization of reporting, and integration with health economic evaluations which are frequently Excel-based.


Chen DG, Peace KE. . Applied Meta-Analysis with R. Boca Raton, FL: CRC Press; 2013. ISBN: 9781466506008; 1466506008.

[Description from Publisher website]

In biostatistical research and courses, practitioners and students often lack a thorough understanding of how to apply statistical methods to synthesize biomedical and clinical trial data. Filling this knowledge gap, Applied Meta-Analysis with R shows how to implement statistical meta-analysis methods to real data using R.

Drawing on their extensive research and teaching experiences, the authors provide detailed, step-by-step explanations of the implementation of meta-analysis methods using R. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R packages and functions. This systematic approach helps readers thoroughly understand the analysis methods and R implementation, enabling them to use R and the methods to analyze their own meta-data.

Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R) in public health, medical research, governmental agencies, and the pharmaceutical industry.


Introduction to R
What Is R?
Steps on Installing R and Updating R Packages
Database Management and Data Manipulations
A Simple Simulation on Multi-Center Studies
Summary and Recommendations for Further Reading

Research Protocol for Meta-Analyses
Defining the Research Objective
Criteria for Identifying Studies to Include in the Meta-Analysis
Searching For and Collecting the Studies
Data Abstraction and Extraction
Meta-Analysis Methods
Summary and Discussion

Fixed Effects and Random Effects in Meta-Analysis
Two Datasets from Clinical Studies
Fixed-Effects and Random-Effects Models in Meta-Analysis
Data Analysis in R
Which Model Should We Use? Fixed Effects or Random Effects?
Summary and Conclusions

Meta-Analysis with Binary Data
Meta-Analysis Methods
Meta-Analysis of Lamotrigine Studies

Meta-Analysis for Continuous Data
Two Published Datasets
Methods for Continuous Data
Meta-Analysis of Tubeless versus Standard Percutaneous Nephrolithotomy

Heterogeneity in Meta-Analysis
Heterogeneity Quantity Q and the Test of heterogeneity in R meta
The Quantifying Heterogeneity in R meta
Step-By-Step Implementations in R

Data Analysis Using R

Individual Patient-Level Data Analysis versus Meta-Analysis
Treatment Comparison for Changes in HAMD
Treatment Comparison for Changes in MADRS
Simulation Study on Continuous Outcomes

Meta-Analysis for Rare Events
The Rosiglitazone Meta-Analysis
Step-by-Step Data Analysis in R

Other R Packages for Meta-Analysis
Combining p-Values in Meta-Analysis
R Packages for Meta-Analysis of Correlation Coefficients
Multivariate Meta-Analysis

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