Umbrella reviews (URs) synthesize findings from multiple systematic reviews on a specific topic. Methodological approaches for analyzing and presenting UR results vary, and reviewers often adapt methods to align with research objectives. This study examined the characteristics of analysis and presentation methods used in healthcare-related URs. A systematic PubMed search identified URs published between 2023 and 2024. Inclusion criteria focused on healthcare URs using systematic reviews as the unit of analysis. A random sample of 100 eligible URs was included. A customized, piloted data extraction form was used to collect bibliographic, conduct, and reporting data independently. Descriptive analysis and narrative synthesis summarized findings. The most common terminology for eligible studies was “umbrella reviews” (65%) or “overviews” (30%). Question frameworks included PICO (43%) and PICOS (14%), with quantitative systematic reviews included in most URs (98%), and 68% including randomized controlled trials. The most frequent methodological guidance source was Cochrane (32%). Data analysis commonly used narrative synthesis and meta-analysis, with Stata, RevMan, and GRADEPro GDT employed for presentation. Information about study overlap and certainty assessment was rarely reported.Variation exists in how data are analyzed and presented in URs, with key elements often omitted. These findings highlight the need for clearer methodological guidance to enhance consistency and reporting in future URs.

The ctrdata package has been created to boost the use of data available in public registers of clinical trials. It enables user-friendly, reproducible workflows to identify trials of interest, download protocol- and results-related data, and conduct sophisticated analyses, across multiple registers and trials. ctrdata works in the widely used R environment, and its databases can be used with other tools. The package is open source with a permissive licence, to facilitate collaboration.

This report provides an overview of ctrdata, including its implementation, cases of interest to researchers in public health, medicines, and regulatory science, as well as potential limitations and further developments. At this time, ctrdata works with the European Union (EU) Clinical Trials Information System (CTIS), the EU Clinical Trials Register (EUCTR), the US Clinicaltrials.Gov (CTGOV), and the ISRCTN—the UK’s Clinical Study Registry. The registers are complementary in scope and scientific value, yet differences in data models, variable definitions, search parametrisations, and retrieval options hamper efficient scientific workflows, calling for a scientific-technical, programmatic solution and driving the development of ctrdata.

By employing ctrdata to comprehensively use and easily leverage trial register data, researchers can effectively address a variety of questions, gain useful insights into evolving policies and practices of drug development, and inform further clinical research. Patients and their organisations, developers, policymakers, and other interested parties can build on ctrdata to create solutions for their use cases.

In random-effects meta-analysis, the between-study heterogeneity variance, $\tau ^2$, is often reported but is not easy to interpret. For meta-analyses of differences (such as mean differences, standardized mean differences, or risk differences), the standard deviation (SD), $\tau $, indicates the extent to which studies’ true effects vary about their average. For meta-analyses of (natural) log-transformed measures of effect (such as log risk ratios [RRs]), we explain how the geometric SD, $\exp (\tau )$, is helpful to understand how untransformed measures (such as RRs) vary multiplicatively about their average. We recommend that authors and software developers report $\tau $ for differences and $\exp (\tau )$ for ratios, rather than $\tau ^2$. This will facilitate the interpretation of the magnitude of heterogeneity values, for example, the interpretation of heterogeneity estimates and confidence intervals beyond simple binary statements about the presence or absence of heterogeneity.

Existing guidelines on overviews of reviews and umbrella reviews recommend an assessment of the certainty of evidence, but provide limited guidance on ‘how to’ apply the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) to such a complex evidence synthesis. We share our experience of developing a ‘general principles’ approach to applying GRADE to a complex overview of reviews. The approach was developed in an iterative and exploratory manner during the planning and conduct of an overview of reviews of a novel molecular imaging technique for the staging of prostate cancer, involving a formal review by a group of 11 methodologists/health services researchers. This approach was developed during the evidence synthesis process, piloted, and then applied to our ongoing overview of reviews. A ‘general principles’ approach of applying the domains of GRADE to an overview of reviews and arriving at an overall summary judgement for each outcome is presented. Our approach details additional factors to consider, including addressing both the primary study risk of bias as assessed by the included reviews and the risk of bias of the systematic reviews themselves, as well as the statistical heterogeneity observed in meta-analyses conducted within the included reviews. Our approach distilled key principles from the relevant GRADE guidelines and allowed us to apply GRADE to a complex body of evidence in a consistent and transparent way. The approach taken and the methods used to develop our approach may inform researchers working on overviews of reviews, umbrella reviews, or future methodological guidelines.

Network meta-analysis (NMA) is a vital methodology for synthesizing evidence across multiple treatments and informing medical decision-making. However, effective visualization and interpretation of results from large networks of interventions remain challenging, particularly for non-specialists. NMAstudio 2.0 is an innovative, interactive web application designed to address these difficulties by streamlining NMA workflows and enhancing result visualization. Developed using Python and R, NMAstudio 2.0 seamlessly integrates with established NMA frameworks. Our exemplar application of NMAstudio 2.0 using a Cochrane Review comparing several treatments for chronic plaque psoriasis demonstrates its capacity to facilitate all crucial steps of an NMA. The application features an intuitive interface for uploading data, automating analyses, generating interactive visualizations such as network diagrams, forest plots, ranking plots, and producing unique outputs like boxplots for transitivity checks and bidimensional forest plots. Most outputs are dynamically linked with the network diagram, enabling users to interactively explore evidence networks, apply advanced filtering, and highlight specific features by selecting nodes or edges within the diagram. While NMAstudio 2.0 aims to simplify NMAs, it also incorporates steps during the data upload process to mitigate the risk of producing poorly reported NMAs. NMAstudio 2.0 represents a significant step forward in improving the usability and accessibility of NMA, offering researchers a robust, versatile platform for evidence synthesis. Its integration of advanced features with an emphasis on user experience positions it as a valuable resource for enhancing decision-making and promoting evidence-based practice across diverse contexts.