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A senior colleague asks me to critique a paper which reports to have used multivariate statistical methods to suggest an inhibitory effect of maternal smoking on the development of severe retinopathy of prematurity (ROP).1 S/he is concerned by the paper because the abstract suggests a positive effect of maternal smoking which flies very much against public health messages in general regarding smoking but is reassured by the fact that complex statistical methods, namely multivariate techniques, have been employed.
I access the internet and find that the paper has been published in a peer-reviewed journal of high repute and that it reports an analysis conducted using data from 86 premature (<32 weeks’ gestation) infants. ROP grading had been evaluated in accordance with the International Classification of Retinopathy of Prematurity.2 The authors explored clinical characteristics associated with the proportions of babies who had developed severe ROP (defined as stage 3 with plus disease). Several characteristics had been recorded for each baby or mother—including birth weight, gestational age, gender of the baby, oxygen supplementation and maternal smoking. The authors report results of both univariate and multivariate logistic regression analyses and that analyses were conducted using STATA V.10 and R V.2.71.3 4
I am not familiar with the term multivariate and so I consult the internet and statistical books.5–7 I learn that multivariate techniques are very different to univariate techniques. I learn that the term ‘multivariate’ in general means ‘many variables’, but in statistical jargon, it has come to have a more specific meaning—many dependent (response) variables or alternatively variables where there is no hierarchy, that is, variables are not classified into response and predictors but are regarded as being on an equal footing.8
In univariate techniques, there is a single outcome or dependent (response) variable (in this instance, development …
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