Subgroup analysis

Nearly all randomized trials will recruit patients with a variety of characteristics, and therefore there is often interest in investigating whether we can identify subgroups of patients in whom the relative effects of treatment are different. For example, in a trial showing an experimental treatment is better than a control treatment, we may ask whether the experimental treatment is more effective in younger patients than older patients. There are many difficulties in undertaking such analyses and considerable care needs to be taken in performing and interpreting these analyses.

The first and main problem is that usually such analyses will (quite properly) be exploratory in nature and therefore there is a danger that the data will be analysed in a number of different ways in the hope of finding some significant result. The problems with doing this are similar to those for interim analysis below, in particular the chance of finding a significant result increases as the number of tests we perform increases. An example of the dangers of searching through multiple subgroups has been shown by Collins and colleagues [13], who showed that in a trial for patients with suspected acute myocardial infarction the benefit of treatment was restricted to patients born under Scorpio and four times as great as compared to the effect of treatment for patients born under all the other birth signs considered together. This is of course quite implausible. A further reason for being wary of subgroup analyses claiming positive results is because the trial is usually powered for detecting a difference in all patients. As a consequence when examining subgroups of patients there is usually very limited power to detect different sizes of effect in these subgroups.

Some guidance to the analysis of data in subgroups is given in Box 9.4.

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