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Next-generation systematic reviews: prospective meta-analysis, individual-level data, networks and umbrella reviews
  1. John Ioannidis
  1. Correspondence to Dr John Ioannidis, Stanford Prevention Research Center, Department of Medicine, and Department of Health Research and Policy, Stanford University School of Medicine and Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, and Meta-Research Innovation Center at Stanford (METRICS), Stanford, California, USA; jioannid{at}

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Systematic reviews and meta-analyses have reached a critical point. Their success is solid and dreadful at the same time. They are widely considered the highest level of evidence. There are tens of thousands of systematic reviews already published, but their production is still increasing geometrically. The problem is that the majority of systematic reviews are flawed, misleading, redundant, useless or all of the above,1 and this applies to almost all medical fields, including sports and exercise medicine.

Part of the problem with systematic reviews stems from the poor, misleading primary evidence2 that authors try to synthesise and make sense of. However, if this were the only major problem, systematic reviews and meta-analyses would still be extremely useful. They could focus exactly on showing how flawed, misleading and useless this evidence is. This could lead to suggestions on how to improve research in the field. Instead, systematic reviews often sanctify results from poor studies, by making these seem even more statistically significant and (spuriously) conclusive. They can also compound the problems seen in the primary studies, if they are driven by reviewers and sponsors with conflicts of interest, financial or academic.

There are four types of ‘next-generation’ systematic reviews that may raise the bar and help shape a new generation of more reliable evidence synthesis: prospective meta-analysis, individual-level data, network meta-analyses and umbrella reviews. They are not necessarily brand new ideas, but in the current circumstances of uncontrollable overproduction and unchecked quality, they have a fresh opportunity for impact. This large-scale impact would have been unimaginable in the past due to constrains in access to data, limited availability of sophisticated methods and fewer opportunities for their application. None of these next-generation tools are bullet proof, but they hold promise and some distinct advantages. Their disadvantages also need to be …

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  • Competing interests None declared.

  • Provenance and peer review Commissioned; internally peer reviewed.

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