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Cited by 194

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Covariate heterogeneity in meta‐analysis: Criteria for deciding between meta‐regression and individual patient data

Simmonds, M. C., Higgins, J. P. T.·2007·Statistics in Medicine

Abstract

Meta‐analyses of clinical trials are increasingly seeking to go beyond estimating the effect of a treatment and may also aim to investigate the effect of other covariates and how they alter treatment effectiveness. This requires the estimation of treatment‐covariate interactions. Meta‐regression can be used to estimate such interactions using published data, but it is known to lack statistical power, and is prone to bias. The use of individual patient data can improve estimation of such interactions, among other benefits, but it can be difficult and time‐consuming to collect and analyse. This paper derives, under certain conditions, the power of meta‐regression and IPD methods to detect treatment–covariate 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 © 2006 John Wiley & Sons, Ltd.

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Metadata

Authors
Simmonds, M. C., & Higgins, J. P. T.
Venue
Statistics in Medicine
Publisher
Wiley
Published
2006-12-29
Volume
26
Issue
15
Pages
2982-2999
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
OpenAlex
W2160672817
Cited by
194
References
2
FWCI
3.76
Type
journal article
Language
en
Open access
closed

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