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Meta\u2010regression can be used to estimate such interactions using published data, but it is known to lack statistical power, and is prone to bias.<\/jats:p><jats:p>The use of individual patient data can improve estimation of such interactions, among other benefits, but it can be difficult and time\u2010consuming to collect and analyse. This paper derives, under certain conditions, the power of meta\u2010regression and IPD methods to detect treatment\u2013covariate interactions. These power formulae are shown to depend on heterogeneity in the covariate distributions across studies. This allows the derivation of simple tests, based on heterogeneity statistics, for comparing the statistical power of the analysis methods. Copyright \u00a9 2006 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/sim.2768","type":"journal-article","created":{"date-parts":[[2006,12,29]],"date-time":"2006-12-29T13:18:28Z","timestamp":1167398308000},"page":"2982-2999","source":"Crossref","is-referenced-by-count":143,"title":["Covariate heterogeneity in meta\u2010analysis: Criteria for deciding between meta\u2010regression and individual patient data"],"prefix":"10.1002","volume":"26","author":[{"given":"M. C.","family":"Simmonds","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. P. 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