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73:poster Living Systematic Reviews (LSR) and Prospective Meta Analysis (PMA): a call-of-duty for Bayesian analysis
  1. Gian Luca Di Tanna1,2,
  2. Anthony Paulo Sunjaya1,
  3. Joseph Alvin Santos1,
  4. Soumyadeep Bhaumik2,3,
  5. Robert Grant1
  1. 1The George Institute for Global Health, Australia
  2. 2University of New South Wales, Sydney, Australia
  3. 3The George Institute for Global Health, India


Background The recent Covid-19 pandemic has accelerated the use of LSRs and PMAs, viewed as the ‘next generation systematic reviews and meta-analyses’. LSRs and PMAs are prospective designs that can reduce the problems of traditional retrospective meta-analyses (MA) such as selective outcome reporting and publication bias, missing data, etc., and thus offer a better option for incorporating and generating new evidence.

Objectives We propose the Bayesian approach as a method for analysing LSRs and PMAs. Bayesian Meta Analysis (BMA) is particularly appealing - actually, natural - for these designs as it clearly reflects the process of learning, defined as new evidence coming to update the previous knowledge, that is intrinsic to LSRs and PMAs.

Methods Results pooled in the previous update of the LSR, or derived from the studies already known in the PMA, can be used to provide an objective/historical prior distribution. The combination of this information with the accumulated results (conditioning on these) provides the posterior probability distribution that can be used as the prior in the next iteration of the LSR/PMA (yesterday’s posterior becomes tomorrow’s prior).

Results We will show an example of BMA on a LSR of the association between Covid-19 and asthmatic patients and give practical suggestions for its use.

Discussion Without relying on asymptomatic normality assumptions, BMA is suitable as it is a coherent and flexible framework that, in comparison with frequentist MAs, allows a better assessment of the between-study variance and overcomes some common issues as dealing with missing data and publication bias.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: .

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