Assessing Subgroup Effects with Binary Data: Can the Use of Different Effect Measures Lead to Different Conclusions?. White, I. R. & Elbourne, D. *BMC Med Res Methodol*, April, 2005. bibtex @article{whi05ass,
title = {Assessing Subgroup Effects with Binary Data: Can the Use of Different Effect Measures Lead to Different Conclusions?},
volume = {5},
number = {15},
journal = {BMC Med Res Methodol},
author = {White, Ian R. and Elbourne, Diana},
month = apr,
year = {2005},
citeulike-article-id = {13265806},
citeulike-linkout-0 = {http://www.biomedcentral.com/1471-2288/5/15},
posted-at = {2014-07-14 14:10:04},
priority = {0},
annote = {example of disagreement between variation in treatment effects over subgroups when using relative risks vs. odds ratio;large variation in baseline risk;"The test of interaction should therefore be applied to the effect measure which is least likely to exhibit an interaction.";doi:10.1186/1471-2288-5-15;note from Doug Altman:"I fully support the idea of using evidence as well as theoretical arguments in such situations. We have evidence that OR is not more likely to give homogeneity in meta-analysis than RR (Deeks JJ: Issues in the selection of a summary statistic for meta-analysis of clinical trials with binary outcomes. Stat Med 2002, 21:1575-1600.). It certainly is possible for RRs to be constant across a wide range of risk groups when one is heading towards zero so to speak - ie a beneficial effect gives RR{$<$}1. I agree that it is not possible when one goes the other way."}
}

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