Researchers may have at their disposal the raw data of the studies they wish to meta-analyze. The goal of this study is to identify, illustrate, and compare a range of possible analysis options for researchers to whom raw data are available, wanting to fit a structural equation model (SEM) to these data. This study illustrates techniques that…

An overview of reviews aims to collect, assess, and synthesize evidence from multiple systematic reviews (SRs) on a specific topic using rigorous and reproducible methods. An important methodological challenge in conducting an overview of reviews is the management of overlapping data due to the inclusion of the same primary studies in SRs. We…

Meta-analysis is a widely used methodology to combine evidence from different sources examining a common research phenomenon, to obtain a quantitative summary of the studied phenomenon. In the medical field, multiple studies investigate the effectiveness of new treatments and meta-analysis is largely performed to generate the summary (average)…

When searching for scholarly documents, researchers often stick with the same familiar handful of databases. Yet, just beyond these limited horizons lie dozens of alternatives with which they could search more effectively, whether for quick lookups or thorough searches in systematic reviews or meta-analyses. Searchsmart.org is a free website that…

Systematic searching aims to find all possibly relevant research from multiple sources, the basis for an unbiased and comprehensive evidence base. Along with bibliographic databases, systematic reviewers use a variety of additional methods to minimise procedural bias. Citation chasing exploits connections between research articles to identify…

The field of evidence synthesis is growing rapidly, with a corresponding increase in the number of software tools and workflows to support the construction of systematic reviews, systematic maps, and meta-analyses. Despite much progress, however, a number of problems remain, including slow integration of new statistical or methodological…

Machine learning (ML) algorithms have proven highly accurate for identifying Randomized Controlled Trials (RCTs) but are not used much in practice, in part because the best way to make use of the technology in a typical workflow is unclear. In this work, we evaluate ML models for RCT classification (support vector machines, convolutional neural…