The perils and pitfalls of subgroup analysis
Dr Chris Cates' article demonstrating why subgroup analysis can be untrustworthy.Key Concepts addressed:
Controlled clinical trials are designed to investigate the effect of a treatment in a given population of patients, for example aspirin is given to patients with ischaemic heart disease. Inevitably there will be differences between the patients included in the trial (men versus women, older versus younger, hypertensive versus non-hypertensive).
It is tempting to look at the effects of treatment separately in different types of patient in order to decide who will benefit most from being given the treatment. Although this analysis of the sub-groups of patients is widely carried out in the medical literature, it is not very reliable. And the ISIS-2 trial gives a clear example of how this can be misleading. The trial looked at the effect of aspirin given after acute myocardial infarction, and when the results were reported the editorial team at the Lancet wished to publish a table of sub-group analyses. The authors agreed as long as the first line in the table compared the effects in patients with different birth signs.
The analysis showed that aspirin was beneficial in all patients except those with the star signs of Libra and Gemini. This served as a warning against the over interpretation of the results of the other sub-groups reported in the paper. The problem is that the play of chance can lead to apparently significant differences between sub-groups, and these are really only helpful in very large trials which show really big overall differences in the treatment and control groups.
From Dr Chris Cates, EBM Website.