SynthesisBMC health services research2023
Overlapping research efforts in a global pandemic: a rapid systematic review of COVID-19-related individual participant data meta-analyses.
Synthesis in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundIndividual participant data meta-analyses (IPD-MAs), which involve harmonising and analysing participant-level data from related studies, provide several advantages over aggregate data meta-analyses, which pool study-level findings. IPD-MAs are especially important for building and evaluating diagnostic and prognostic models, making them an important tool for informing the research and public health responses to COVID-19.
methodsWe conducted a rapid systematic review of protocols and publications from planned, ongoing, or completed COVID-19-related IPD-MAs to identify areas of overlap and maximise data request and harmonisation efforts. We searched four databases using a combination of text and MeSH terms. Two independent reviewers determined eligibility at the title-abstract and full-text stages. Data were extracted by one reviewer into a pretested data extraction form and subsequently reviewed by a second reviewer. Data were analysed using a narrative synthesis approach. A formal risk of bias assessment was not conducted.
resultsWe identified 31 COVID-19-related IPD-MAs, including five living IPD-MAs and ten IPD-MAs that limited their inference to published data (e.g., case reports). We found overlap in study designs, populations, exposures, and outcomes of interest. For example, 26 IPD-MAs included RCTs; 17 IPD-MAs were limited to hospitalised patients. Sixteen IPD-MAs focused on evaluating medical treatments, including six IPD-MAs for antivirals, four on antibodies, and two that evaluated convalescent plasma.
conclusionsCollaboration across related IPD-MAs can leverage limited resources and expertise by expediting the creation of cross-study participant-level data datasets, which can, in turn, fast-track evidence synthesis for the improved diagnosis and treatment of COVID-19.
trial registration10.17605/OSF.IO/93GF2.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.