Confounding in clinical trials2012; 345 doi: http://dx.doi.org/10.1136/bmj.e7951 (Published 23 November 2012) Cite this as: 2012;345:e7951
- Philip Sedgwick, reader in medical statistics and medical education
- 1Centre for Medical and Healthcare Education, St George’s, University of London, Tooting, London, UK
Researchers investigated whether a low glycaemic index diet in pregnancy reduced the incidence of macrosomia—babies large for their gestational age—in an at-risk group. A randomised controlled trial study design was used. The intervention was a low glycaemic index diet from early pregnancy. The control group received no dietary intervention. Participants were women without diabetes, aged 18 or over, all in their second pregnancy between January 2007 and January 2011, and who had previously delivered an infant weighing more than 4 kg. In total, 800 women were recruited, of whom 396 were randomised to intervention and 404 to control.1
The primary outcome was birth weight. Of the 396 women allocated to intervention, 372 (93.9%) provided data at follow-up, compared with 387 (95.8%) of the 404 women allocated to control. A per protocol analysis was performed. No significant difference existed between treatments in absolute birth weight, birthweight centile, or ponderal index.
Which of the following statements, if any, are true?
a) The random allocation of women to treatment minimised confounding at baseline.
b) For a variable to confound the association between treatment (intervention or control) and outcome, it must be associated with birth weight.
c) For a variable to confound the association between treatment (intervention or control) and birth weight, it must be unequally distributed between treatment groups.
d) The association between …
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