Rapid review methodology aims to facilitate faster conduct of systematic reviews to meet the needs of the decision-maker, while also maintaining quality and credibility. This systematic review aimed to determine the impact of different methodological shortcuts for undertaking rapid reviews on the risk of bias (RoB) of the results of the review.…

One of the reasons for the popularity of meta-analysis is the notion that these analyses will possess more power to detect effects than individual studies. This is inevitably the case under a fixed-effect model. However, the inclusion of the between-study variance in the random-effects model, and the need to estimate this parameter, can have…

Introduction: Registration and publication of trial protocols has become increasingly important and a requirement in some sources of funding and publication. Increased access to protocols yields many potential benefits, but there are issues regarding identification of published protocols. The aim of this investigation is to compare methods of…

Authors and indexers are increasingly including terms for adverse "drug" effects in the titles, abstracts, or indexing of records in MEDLINE and Embase. However, it is not clear if this is the same for studies with "nondrug" adverse effects data. We therefore assessed the feasibility of using adverse effects terms when…

In meta-analysis, the random-effects model is often used to account for heterogeneity. The model assumes that heterogeneity has an additive effect on the variance of effect sizes. An alternative model, which assumes multiplicative heterogeneity, has been little used in the medical statistics community, but is widely used by particle physicists. In…

Introduction: Previous studies suggest that many systematic reviews contain meta-analyses that display temporal trends, such as the first study's result being more extreme than later studies' or a drift in the pooled estimate. We assessed the extent and characteristics of temporal trends using all Cochrane intervention reports published 2008-2012.…

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…

Background: It is unclear which terms should be included in bibliographic searches for randomized controlled trials (RCTs) of drugs, and identifying relevant drug terms can be extremely laborious. The aim of our analysis was to determine whether a bibliographic search using only the generic drug name produces sufficient results for the generation…