The literature search underpins data collection for all systematic reviews (SRs). The SR reporting guideline PRISMA, and its extensions, aim to facilitate research transparency and reproducibility, and ultimately improve the quality of research, by instructing authors to provide specific research materials and data upon publication of the…

We developed "metadta," a flexible, robust, and user-friendly statistical procedure that fuses established and innovative statistical methods for meta-analysis, meta-regression, and network meta-analysis of diagnostic test accuracy studies in Stata. Using data from published meta-analyses, we validate "metadta" by comparing and…

Estimation of within-trial interactions in meta-analysis is crucial for reliable assessment of how treatment effects vary across participant subgroups. However, current methods have various limitations. Patients, clinicians and policy-makers need reliable estimates of treatment effects within specific covariate subgroups, on relative and absolute…

Pathway-based differential expression analysis allows the incorporation of biological domain knowledge into transcriptomics analysis to enhance our understanding of disease mechanisms. To integrate information among multiple studies at the pathway level, pathway-based meta-analysis can be performed. Paired or partially paired samples are common in…

Abstract screening is one important aspect of conducting a high-quality and comprehensive systematic review and meta-analysis. Abstract screening allows the review team to conduct the tedious but vital first step to synthesize the extant literature: winnowing down the overwhelming amalgamation of citations discovered through research databases to…

Synthesizing findings about the indirect (mediated) effect plays an important role in determining the mechanism through which variables affect one another. This simulation study compared six methods for synthesizing indirect effects: correlation-based MASEM, parameter-based MASEM, marginal likelihood synthesis, an adjustment to marginal likelihood…

Many randomized trials evaluate an intervention effect on time-to-event outcomes. Individual participant data (IPD) from such trials can be obtained and combined in a so-called IPD meta-analysis (IPD-MA), to summarize the overall intervention effect. We performed a narrative literature review to provide an overview of methods for conducting an…

Although well developed to assess efficacy questions, meta-analyses and, more generally, systematic reviews, have received less attention in application to safety-related questions. As a result, many open questions remain on how best to apply meta-analyses in the safety setting. This appraisal attempts to: (i) summarize the current guidelines for…

The Hartung-Knapp method for random-effects meta-analysis, that was also independently proposed by Sidik and Jonkman, is becoming advocated for general use. This method has previously been justified by taking all estimated variances as known and using a different pivotal quantity to the more conventional one when making inferences about the…

Network meta-analysis (NMA) uses both direct and indirect evidence to compare the efficacy and harm between several treatments. Structural equation modeling (SEM) is a statistical method that investigates relations among observed and latent variables. Previous studies have shown that the contrast-based Lu-Ades model for NMA can be implemented in…

The vast majority of meta-analyses uses summary/aggregate data retrieved from published studies in contrast to meta-analysis of individual participant data (IPD). When the outcome is continuous and IPD are available, linear mixed modelling methods can be employed in a one-stage approach. This allows for flexible modelling of within-study…

Background: Model-based meta-analysis (MBMA) is increasingly used to inform drug-development decisions by synthesising results from multiple studies to estimate treatment, dose-response, and time-course characteristics. Network meta-analysis (NMA) is used in Health Technology Appraisals for simultaneously comparing effects of multiple treatments,…

Randomized clinical trials underpin evidence-based clinical practice, but flaws in their conduct may lead to biased estimates of intervention effects and hence invalid treatment recommendations. The main approach to the empirical study of bias is to collate a number of meta-analyses and, within each, compare the results of trials with and without…

Launching biomedical innovations based on clinical demands instead of translating basic research findings to practice reduces the risk that the results will not fit the clinical routine. To realize this type of innovation, a meta-analysis of the body of research is necessary to reveal demand-matching concepts. However, both the data deluge and the…

This paper critically explores how survey and routinely collected data could aid in assessing the generalisability of public health evidence. We propose developing approaches that could be employed in understanding the relevance of public health evidence, and investigate ways of producing meta-analytic estimates tailored to reflect local…