Research Synthesis Methods
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To assess the conditions under which employing an overview of systematic reviews is likely to lead to a high risk of bias.
To synthesise existing guidance concerning overview practice, a scoping review was conducted. Four electronic databases were searched with a pre‐specified strategy (PROSPERO 2015:CRD42015027592) ending October 2015. Included studies needed to describe or develop overview methodology. Data were narratively synthesised to delineate areas highlighted as outstanding challenges or where methodological recommendations conflict.
Twenty‐four papers met the inclusion criteria. There is emerging debate regarding overlapping systematic reviews; systematic review scope; quality of included research; updating; and synthesizing and reporting results. While three functions for overviews have been proposed—identify gaps, explore heterogeneity, summarize evidence—overviews cannot perform the first; are unlikely to achieve the second and third simultaneously; and can only perform the third under specific circumstances. Namely, when identified systematic reviews meet the following four conditions: (1) include primary trials that do not substantially overlap, (2) match overview scope, (3) are of high methodological quality, and (4) are up‐to‐date.
Considering the intended function of proposed overviews with the corresponding methodological conditions may improve the quality of this burgeoning publication type. Copyright © 2017 John Wiley & Sons, Ltd.
Meta‐analyses are fundamental tools for collating and synthesizing large amounts of information, and graphical displays have become the principal tool for presenting the results of multiple studies of the same research question. We review standard and proposed graphical displays for presentation of meta‐analytic data, and offer our recommendations on how they might be presented to provide the most useful and user‐friendly illustrations. We concentrate on graphs that specifically aim to present similar sorts of univariate results from multiple studies. We start with forest plots and funnel plots, and proceed to Galbraith (or radial) plots, L'Abbé (and related) plots, further plots useful for investigating heterogeneity, plots useful for model diagnostics and plots for illustrating likelihoods and Bayesian meta‐analyses. Copyright © 2010 John Wiley & Sons, Ltd.
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 approaches into user‐friendly software, low prevalence of open‐source software, and poor integration among distinct software tools. These issues hinder the utility and transparency of new methods to the research community. Here, I present revtools, an R package to support article screening during evidence synthesis projects. It provides tools for the import and deduplication of bibliographic data, screening of articles by title or abstract, and visualization of article content using topic models. The software is entirely open‐source and combines command‐line scripting for experienced programmers with custom‐built user interfaces for casual users, with further methods to support article screening to be added over time. revtools provides free access to novel methods in an open‐source environment and represents a valuable step in expanding the capacity of R to support evidence synthesis projects.
Existing methods for meta‐analysis of diagnostic test accuracy focus primarily on a single index test. We propose models for the joint meta‐analysis of studies comparing multiple index tests on the same participants in paired designs. These models respect the grouping of data by studies, account for the within‐study correlation between the tests' true‐positive rates (TPRs) and between their false‐positive rates (FPRs) (induced because tests are applied to the same participants), and allow for between‐study correlations between TPRs and FPRs (such as those induced by threshold effects). We estimate models in the Bayesian setting. We demonstrate using a meta‐analysis of screening for Down syndrome with two tests: shortened humerus (arm bone), and shortened femur (thigh bone). Separate and joint meta‐analyses yielded similar TPR and FPR estimates. For example, the summary TPR for a shortened humerus was 35.3% (95% credible interval (CrI): 26.9, 41.8%) versus 37.9% (27.7, 50.3%) with joint versus separate meta‐analysis. Joint meta‐analysis is more efficient when calculating comparative accuracy: the difference in the summary TPRs was 0.0% (−8.9, 9.5%; TPR higher for shortened humerus) with joint versus 2.6% (−14.7, 19.8%) with separate meta‐analyses. Simulation and empirical analyses are needed to refine the role of the proposed methodology. Copyright © 2014 John Wiley & Sons, Ltd.
– Meta‐analyses of health‐related quality of life (HRQL) outcomes present difficulties in interpretation when studies use different instruments to measure the same construct. Presentation of results in standard deviation units (standardized mean difference) is widely used but is limited by vulnerability to differential variability in populations enrolled and interpretational challenges.
– The objective of this study is to identify and describe the available approaches for enhancing interpretability of meta‐analyses involving HRQL outcomes.
– We identified 12 approaches in three categories:
Summary estimates derived from the pooled standardized mean difference: conversion to units of the most familiar instrument or to risk difference or odds ratio. These approaches remain vulnerable to differential variability in populations. Summary estimates derived from the individual trial summary statistics: conversion to units of the most familiar instrument or to ratio of means. Both are appropriate complementary approaches to measures derived from converted probabilities. Summary estimates derived from the individual trial summary statistics and established minimally important differences for all instruments: presentation in minimally important difference units or conversion to risk difference or odds ratio. Risk differences are ideal for balancing desirable and undesirable consequences of alternative interventions.
– The use of these approaches may enhance the interpretability and the usefulness of systematic reviews involving HRQL outcomes. Copyright © 2011 John Wiley & Sons, Ltd.
There is ongoing interest in including grey literature in systematic reviews. Including grey literature can broaden the scope to more relevant studies, thereby providing a more complete view of available evidence. Searching for grey literature can be challenging despite greater access through the Internet, search engines and online bibliographic databases. There are a number of publications that list sources for finding grey literature in systematic reviews. However, there is scant information about how searches for grey literature are executed and how it is included in the review process. This level of detail is important to ensure that reviews follow explicit methodology to be systematic, transparent and reproducible.
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. Copyright © 2013 John Wiley & Sons, Ltd.
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