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It is unclear how many covariates can reliably be investigated, and how this might depend on the number of studies, the extent of the heterogeneity and the relative weights awarded to the different studies. Our objectives in this paper are two\u2010fold. First, we use simulation to investigate the type I error rate of meta\u2010regression in various situations. Second, we propose a permutation test approach for assessing the true statistical significance of an observed meta\u2010regression finding. Standard meta\u2010regression methods suffer from substantially inflated false\u2010positive rates when heterogeneity is present, when there are few studies and when there are many covariates. These are typical of situations in which meta\u2010regressions are routinely employed. We demonstrate in particular that fixed effect meta\u2010regression is likely to produce seriously misleading results in the presence of heterogeneity. The permutation test appropriately tempers the statistical significance of meta\u2010regression findings. We recommend its use before a statistically significant relationship is claimed from a standard meta\u2010regression analysis. Copyright \u00a9 2004 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/sim.1752","type":"journal-article","created":{"date-parts":[[2004,5,18]],"date-time":"2004-05-18T06:01:01Z","timestamp":1084860061000},"page":"1663-1682","source":"Crossref","is-referenced-by-count":1094,"title":["Controlling the risk of spurious findings from meta\u2010regression"],"prefix":"10.1002","volume":"23","author":[{"given":"Julian P. 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