A re-evaluation of the 'quantile approximation method' for random effects meta-analysis.
The quantile approximation method has recently been proposed as a simple method for deriving confidence intervals for the treatment effect in a random effects meta-analysis. Although easily implemented, the quantiles used to construct intervals are derived from a single simulation study. Here it is shown that altering the study parameters, and in particular introducing changes to the distribution of the within-study variances, can have a dramatic impact on the resulting quantiles. This is further illustrated analytically by examining the scenario where all trials are assumed to be the same size. A more cautious approach is therefore suggested, where the conventional standard normal quantile is used in the primary analysis, but where the use of alternative quantiles is also considered in a sensitivity analysis.
Jackson
Dan
D
MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 2SR, U.K. daniel.jackson@mrc-bsu.cam.ac.uk
Bowden
Jack
J
D000704:Analysis of Variance; D002986:Clinical Trials as Topic; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D005990:Glycerol / Q000008:administration & dosage / Q000627:therapeutic use; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D012997:Solvents / Q000008:administration & dosage / Q000627:therapeutic use; D020521:Stroke / Q000188:drug therapy; D016896:Treatment Outcome
D012997:Solvents; D005990:Glycerol