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Abstract
Objectives: Results of metaanalyses typically conclude that future large studies may be mandated. However, the predictive ability of these estimates is deficient. We explored metaanalytic prediction intervals as means for providing a clear and appropriate future treatment summary reflecting current estimates. Study Design: A metaepidemiological study of binary outcome critical care metaanalyses published between 2002 and 2010. Computation of 95% DerSimonianLaird and Bayesian randomeffects metaanalytic confidence intervals (CI) and 95% credible intervals (CrI), respectively, and frequentist (PI) and Bayesian (PrI) prediction intervals for odds ratio (OR) and risk ratio (RR) were undertaken. Bayesian calculations included the probability that the OR and RR point estimates ≥1. Results: Seventytwo metaanalyses from 70 articles were identified, containing between three and 80 studies each, with median nine studies. For both frequentist and Bayesian settings, 4969% of the metaanalyses excluded the null. All significant CrI had high probabilities of efficacy/harm. The number of PI vs. PrI excluding 1 was 25% vs. 3% (OR), 26% vs. 3% (RR) of the total metaanalyses. Unsurprisingly, PI/PrI width was greater than CI/CrI width and increased with increasing heterogeneity and combination of fewer studies. Conclusion: Robust metaanalytic conclusions and determination of studies warranting new large trials may be more appropriately signaled by consideration of initial interval estimates with prediction intervals. Substantial heterogeneity results in exceedingly wide PIs. More caution should be exercised regarding the conclusions of a metaanalysis.
Original language  English 

Pages (fromto)  503510 
Number of pages  8 
Journal  Journal of Clinical Epidemiology 
Volume  65 
Issue number  5 
DOIs  
Publication status  Published  May 2012 
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Utility of Bayesian and frequentist prediction intervals in metaanalyses
Petra Graham (Speaker) & John L. Moran (Speaker)
Aug 2010Activity: Talk or presentation › Presentation