February 1, 2002
View Full PaperBeing sceptical about meta-analyses: a Bayesian perspective on magnesium trials in myocardial infarction
Key Result
Applying a Bayesian sceptical prior combined with a random effects meta-analysis would have suitably tempered early enthusiasm for intravenous magnesium following myocardial infarction.
Study Design
Type
Meta-Analysis
Structured PICO
Does intravenous magnesium provide benefits following myocardial infarction, and how do Bayesian meta-analysis methods affect the interpretation of conflicting trial results?
P
Population
Patients following myocardial infarction (based on historical trials available before and after ISIS-4)
I
Intervention
Intravenous magnesium
C
Comparator
Control group
O
Outcome
Benefits following myocardial infarction (specific endpoint not detailed in abstract)
This methodological study demonstrates that applying a Bayesian framework with a sceptical prior and random effects model to meta-analyses can prevent premature enthusiasm for treatments like intravenous magnesium in myocardial infarction when early trials conflict with subsequent mega-trials.
Limitations
- Considerable sensitivity to apparently innocuous assumptions in the Bayesian framework.
Abstract
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.