Debate: A subversive view of subsets - a dissident clinician's opinion
© Current Controlled Trials Ltd 2000
Received: 10 May 2000
Accepted: 1 July 2000
Published: 31 July 2000
Clinical trialists and statisticians are very wary of subgroup analysis, for good reasons. Clinicians have to deal with situations in which subgroups of patients differ widely from one another in their prognosis and response to treatment. Few trials are large enough to demonstrate convincingly these differences in outcome, but often provide suggestive evidence. Should we ignore this and treat all patients as the same, or should we allow dubious statistical evidence to buttress biological plausibility in making clinical decisions?
When meta and mega agree
When a mega-trial comes up with the same findings as a meta-analysis, or vice versa, the evidence appears overwhelming. This was the case with the use of intravenous followed by oral beta-blockers in acute myocardial infarction in the International Study of Infarct Survival (ISIS)-1 study  and the subsequent systematic review . It is not surprising, therefore, that perhaps the most prestigious of cardiological organisations  came up with following the Class 1 recommendation for deciding who should receive this therapy:
"Patients without a contraindication to beta-adrenoceptor blocker therapy who can be treated within 12 hours of onset of infarction, irrespective of administration of concomitant thrombolytic therapy or performance of primary angioplasty".
There is no suggestion in this guideline that some patients conforming with those criteria might benefit or be harmed more than others. This may well have been because the ISIS-1 investigators were pioneers in highlighting the potential dangers of subgroup analysis. This they did most vividly by pointing out how the results varied with a patient's astrological sign.
Ignorance, inefficiency or incredulity?
In many countries, notably the UK, the contemporary use of intravenous followed by oral beta-blockers is very low. It was reported that the British investigators used this therapy in only 2% of the patients in ISIS-4. Were they ignorant or inefficient? It is unlikely that it was ignorance because many of the hospitals participating in ISIS-4 also participated in ISIS-1. Was it inefficiency? This is unlikely because the use of aspirin and thrombolysis (studied in ISIS-2) was very high, by international standards. So it seems probable that the physicians concerned were not wholly convinced by ISIS-1.
How much weight should we put on biological plausibility?
Relationship between blood pressure and clinical outcome
Systolic blood pressure
Control group mortality
Atenolol group mortality
95% confidence interval
≥ 160 mmHg
Would it be very wrong to use this information in helping to decide whom to treat?
Although not conventionally significant, the Metoprolol in Acute Myocardial Infarction (MIAMI) trial  leant support to the ISIS-1 trial and was the most important other contributor to the meta-analysis. Risk categories were predefined in this study, as shown in Table 2.
Would it be right to treat the more than 50% of patients with 0-2 risk factors with metoprolol? Yet that is what the pundits imply we should do. On the other hand, it would be brave to dismiss the findings in the high-risk subgroups.
Fifteen-day mortality in the MIAMI trial 
Number of risk factors (RFs)
95% confidence interval
Can we trust meta-analyses?
The first meta-analysis of 27 536 patients , undertaken in 1986, was significant at the 0.02 level, and demonstrated that intravenous followed by oral beta-blockade would save six lives in every 1000 patients treated. The second meta-analysis , of 29 260 patients, undertaken in 1999, showed no significant benefit (95% confidence interval 0.85-1.08).
The first led authorities to recommend intravenous followed by oral beta-blockers for all patients. By the same logic, one should now treat no patients with this regimen. Clearly, this is ridiculous. So do we recommend it then for subgroups? Of course, it is desirable for the subgroups to be defined in advance and for them to be few in number. It is also customary to say that subgroup findings should be hypothesis-generating, but these hypotheses are seldom tested in adequately sized trials.
It seems to me reasonable to look for evidence of benefit or harm in certain biologically plausible subgroups, even though the statistical basis for this evidence is not compelling. It is ludicrous and economically mad that we have to treat 1000 patients to benefit six because our trial methodology cannot cope. An intelligent review of subgroups would allow this ratio to be much more reasonable.
There are sound reasons for not drawing conclusions from subgroup analysis. Certainly, it is quite wrong use a computer to look for statistically significant subgroups, as was done in the case of the astrological signs. But cardiologists have to make decisions on the basis of the very imperfect evidence which clinical trials usually provide. Let them use their knowledge of the biology of the condition to interpret which subgroups are clinically relevant and look for (albeit imperfect) statistical support.
The author would like to thank Dr Ralph D'Agostino for providing a statistical analysis of the figures presented in this article.
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