PhilPharma

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Stephen Senn
Head, Methodology and Statistics Group,
Competence Center for Methodology and Statistics (CCMS), Luxembourg

Is Pooling Fooling?

‘And take the case of a man who is ill. I call two physicians: they differ in opinion. I am not to lie down, and die between them: I must do something.’ Samuel Johnson, in Boswell’s A Journal of a Tour to the Hebrides

A common dilemma facing meta-analysts is what to put together with what? One may have a set of trials that seem to be approximately addressing the same question but some features may differ. For example, the inclusion criteria might have differed with some trials only admitting patients who were extremely ill but with other trials treating the moderately ill as well. Or it might be the case that different measurements have been taken in different trials. An even more extreme case occurs when different, if presumed similar, treatments have been used.

It is helpful to make a point of terminology here. In what follows I shall be talking about pooling results from various trials. This does not involve naïve pooling of patients across trials. I assume that each trial will provide a valid within- trial comparison of treatments. It is these comparisons that are to be pooled (appropriately).

A possible way to think of this is in terms of a Bayesian model with a prior distribution covering the extent to which results might differ as features of trials are changed. I don’t deny that this is sometimes an interesting way of looking at things (although I do maintain that it is much more tricky than many might suppose[1]) but I would also like to draw attention to the fact that there is a frequentist way of looking at this problem that is also useful.

Suppose that we have k ‘null’ hypotheses that we are interested in testing, each being capable of being tested in one of k trials. We can label these H_n1, H_n2, … H_nk. We are perfectly entitled to test the null hypothesis H_joint that they are all jointly true. In doing this we can use appropriate judgement to construct a composite statistic based on all the trials whose distribution is known under the null. This is a justification for pooling. Continue reading →