Research Synthesis Methods

Cover image for Vol. 8 Issue 2

Edited By: Tasha Beretvas and Ian Shrier

Impact Factor: 3.018

ISI Journal Citation Reports © Ranking: 2016: 9/57 (Mathematical & Computational Biology); 13/64 (Multidisciplinary Sciences)

Online ISSN: 1759-2887

Virtual Issue Social Behavioral and Health Sciences

Research Synthesis Methods Virtual Issue A Sample for the Social, Behavioral and Health Sciences

Welcome to the new Research Synthesis Methods Virtual Issue: A Sample for the Social, Behavioral and Health Sciences. Enjoy a selection of top papers:

Learning by doing—teaching systematic review methods in 8 weeks
Tianjing Li, Ian J. Saldanha, S. Swaroop Vedula, Tsung Yu, Lori Rosman, Claire Twose, Steven N. Goodman and Kay Dickersin
Abstract: The objective of this paper is to describe the course “Systematic Reviews and Meta-analysis” at the Johns Hopkins Bloomberg School of Public Health. The course has been offered to more than 800 students since 1995. In our view, aspects that worked well include the hands-on approach, students working in a multidisciplinary group, intensive interaction with the teaching team, moving to an online approach, and continuous updates of the course content. A persistent issue is the constraint of time. 193 of 211 (91%) survey participants reported that the course is currently useful or as having an impact on their work.

Searching for grey literature for systematic reviews: challenges and benefits
Quenby Mahood, Dwayne Van Eerd and Emma Irvin
Abstract: The purpose of this paper is to provide a detailed account of one systematic review team's experience in searching for grey literature and including it throughout the review. We provide a brief overview of grey literature before describing our search and review approach. We also discuss the benefits and challenges of including grey literature in our systematic review, as well as the strengths and limitations to our approach. Detailed information about incorporating grey literature in reviews is important in advancing methodology as review teams adapt and build upon the approaches described.

Techniques for identifying cross-disciplinary and ‘hard-to-detect’ evidence for systematic review
Alison O'Mara-Eves, Ginny Brunton, David McDaid, Josephine Kavanagh, Sandy Oliver and James Thomas, Vol 5:1
Abstract:Driven by necessity in our own complex review, we developed alternative systematic ways of identifying relevant evidence where the key concepts are generally not focal to the primary studies' aims and are found across multiple disciplines—that is, hard-to-detect evidence. Specifically, we sought to identify evidence on community engagement in public health interventions that aim to reduce health inequalities.

Opportunities and challenges in using studies without a control group in comparative effectiveness reviews
Jessica K. Paulus, Issa J. Dahabreh, Ethan M. Balk, Esther E. Avendano, Joseph Lau and Stanley Ip
This article provides an overview of issues related to the interpretation of single group studies with a focus on the assumptions required to support their consideration in comparative effectiveness reviews. We discuss the various settings in which single group studies are employed, common research designs that systematic reviewers need to interpret, and challenges associated with using these designs to inform comparative effectiveness questions.

The impact of multiple endpoint dependency on Q and I2 in meta-analysis
Christopher Glen Thompson and Betsy Jane Becker
A common assumption in meta-analysis is that effect sizes are independent. When correlated effect sizes are analyzed using traditional univariate techniques, this assumption is violated. This research assesses the impact of dependence arising from treatment-control studies with multiple endpoints on homogeneity measures Q and I2 in scenarios using the unbiased standardized-mean-difference effect size.

Robust variance estimation with dependent effect sizes: practical considerations including a software tutorial in Stata and SPSS
Emily E. Tanner-Smith and Elizabeth Tipton, Vol 5:1
This paper provides a brief tutorial on the implementation of the Stata and spss macros and discusses practical issues meta-analysts should consider when estimating meta-regression models with robust variance estimates. Two example databases are used in the tutorial to illustrate the use of meta-analysis with robust variance estimates.