Statistics Notes: Interaction 2: compare effect sizes not P values

BMJ 1996; 313 doi: http://dx.doi.org/10.1136/bmj.313.7060.808 (Published 28 September 1996)
Cite this as: BMJ 1996;313:808

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  1. John N S Matthews, senior lecturer in medical statisticsa,
  2. Douglas G Altman, headb
  1. a Department of Medical Statistics, University of Newcastle, Newcastle upon Tyne NE2 4HH
  2. b ICRF Medical Statistics Group, Centre for Statistics in Medicine, Institute of Health Sciences, PO Box 777, Oxford OX3 7LF
  1. Correspondence to: Dr Matthews.

    As we have previously described,1 the statistical term interaction relates to the non-independence of the effects of two variables on the outcome of interest. For example, in a controlled trial comparing a new treatment with a standard treatment we may want to examine whether the observed benefit was the same for different subgroups of patients. A common approach to answering this question is to analyse the data separately in each subgroup. Here we illustrate this approach and explain why it is incorrect.

    One of several subgroup analyses in a trial of antenatal steroids for preventing neonatal respiratory distress syndrome2 was performed to see whether the effect of treatment was different in mothers who did or did not develop pre-eclampsia. Among mothers with preeclampsia 21.2% (7/33) of babies whose mothers were given dexamethasone developed neonatal respiratory …

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