BMJ  2007;335:96-97 (14 July), doi:10.1136/bmj.39265.596262.AD

Practice

Clinical epidemiology notes

Subgroup analyses: how to avoid being misled

John Fletcher, clinical epidemiologist

BMJ, London WC1H 9JR

jfletcher@bmj.com

Three simple examples from recent BMJ papers illustrate how to understand subgroup analyses and why they may be misleading

The first 150 words of the full text of this article appear below.

Introduction

Subgroup analyses are regarded with some suspicion because they can be misleading and less reliable than analyses based on all the people included in the research design. This is a wise precaution when the comparison was not planned at the outset. But when subgroups are described in the protocol of the trial or review along with a stated hypothesis, these secondary analyses may be used to show true differences in effect or to illustrate applicability across patient subgroups. Three recently published BMJ papers, including one in this issue, provide examples of each of these types of subgroup analysis.1 2 3

Cautious interpretation

In a trial that set out to examine the effect on birth weight of reduced caffeine intake during pregnancy, the overall analysis found little effect.1 The difference in birth weight between the women who had drunk caffeinated coffee and those who had drunk decaffeinated coffee was 16 g (95% confidence interval –40 . . . [Full text of this article]

Showing differences

Illustrating applicability

How to approach subgroup analyses


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