BACKGROUND: There has been extensive discussion of the apparent conflict between meta-analyses and a mega-trial investigating the benefits of intravenous magnesium following myocardial infarction, in which the early trial results have been said to be 'too good to be true'. METHODS: We apply Bayesian methods of meta-analysis to the trials available before and after the publication of the ISIS-4 results. We show how scepticism can be formally incorporated into an analysis as a Bayesian prior distribution, and how Bayesian meta-analysis models allow appropriate exploration of hypotheses that the treatment effect depends on the size of the trial or the risk in the control group. RESULTS: Adoption of a sceptical prior would have led early enthusiasm for magnesium to be suitably tempered, but only if combined with a random effects meta-analysis, rather than the fixed effect analysis that was actually conducted. CONCLUSIONS: We argue that neither a fixed effect nor a random effects analysis is appropriate when the mega-trial is included. The Bayesian framework provides many possibilities for flexible exploration of clinical hypotheses, but there can be considerable sensitivity to apparently innocuous assumptions.
BACKGROUND: There has been extensive discussion of the apparent conflict between meta-analyses and a mega-trial investigating the benefits of intravenous magnesium following myocardial infarction, in which the early trial results have been said to be 'too good to be true'. METHODS: We apply Bayesian methods of meta-analysis to the trials available before and after the publication of the ISIS-4 results. We show how scepticism can be formally incorporated into an analysis as a Bayesian prior distribution, and how Bayesian meta-analysis models allow appropriate exploration of hypotheses that the treatment effect depends on the size of the trial or the risk in the control group. RESULTS: Adoption of a sceptical prior would have led early enthusiasm for magnesium to be suitably tempered, but only if combined with a random effects meta-analysis, rather than the fixed effect analysis that was actually conducted. CONCLUSIONS: We argue that neither a fixed effect nor a random effects analysis is appropriate when the mega-trial is included. The Bayesian framework provides many possibilities for flexible exploration of clinical hypotheses, but there can be considerable sensitivity to apparently innocuous assumptions.
Authors: Rebecca M Turner; Jonathan Davey; Mike J Clarke; Simon G Thompson; Julian Pt Higgins Journal: Int J Epidemiol Date: 2012-03-29 Impact factor: 7.196
Authors: Marija Barbateskovic; Olav L Schjørring; Sara Russo Krauss; Janus C Jakobsen; Christian S Meyhoff; Rikke M Dahl; Bodil S Rasmussen; Anders Perner; Jørn Wetterslev Journal: Cochrane Database Syst Rev Date: 2019-11-27
Authors: Rajesh M Shetty; Antonio Bellini; Dhuleep S Wijayatilake; Mark A Hamilton; Rajesh Jain; Sunil Karanth; ArunKumar Namachivayam Journal: Cochrane Database Syst Rev Date: 2018-02-21
Authors: Yiqing Song; Lu Wang; Anastassios G Pittas; Liana C Del Gobbo; Cuilin Zhang; Joann E Manson; Frank B Hu Journal: Diabetes Care Date: 2013-05 Impact factor: 19.112