Reasons or excuses for avoiding meta-analysis in forest plots

Ref ID 474
First Author J. P. Ioannidis
Journal BMJ
Year Of Publishing 2008
Keywords Statistical
General medical
Problem(s) Poor execution of narrative synthesis
Lack of statistical expertise in handling of quantitative data
Number of systematic reviews included 135
Summary of Findings 135 of Cochrane reviews (8%) had 559 forest plots with no summary estimate. Reasons provided for avoiding quantitative synthesis typically revolved around heterogeneity. However, these typically revolve around statistical heterogeneity, such as the I squared statistic, which is not adequate to fully explain or explore heterogeneity. Conducting random effects meta-analysis, meta-regression or Bayesian meta-analysis are methods highlighted by the authors as potential methodological approaches to heterogenous data. Specifying clearer reasons would improve transparency of the systematic reviewers' implicit judgments about heterogeneity and the ability to meta-analyse.
Did the article find that the problem(s) led to qualitative changes in interpretation of the results? Not Applicable
Are the methods of the article described in enough detail to replicate the study?