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Noteworthy statistics
Modelling with multiple explanatory variables
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  1. Pamela Warner
  1. Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
  1. Correspondence to Dr Pamela Warner, Centre for Population Health Sciences, University of Edinburgh Medical School, Teviot Place, Edinburgh EH8 9AG, UK; p.warner{at}ed.ac.uk

https://doi.org/10.1136/jfprhc.2010.0034

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    This statistical technique has been used in two papers in this issue of the Journal, namely those by Kotb et al.1 and Schembri et al.2 These notes are intended to provide a supplementary explanation of this method. [See Box 1 for a glossary of terms used in this article.]

    Box 1 Glossary of statistical terms used in this article

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    What is it?

    Multivariable modelling is the use of statistical modelling techniques, applied to a dataset for a group of individuals – that dataset comprising some known outcome or group membership (usually binary), plus a set of variables that potentially ‘explain’ that outcome/group membership.3

    When/why is it useful?

    It is often the case that research seeks to understand better the nature of the association between some condition/group of interest and a set of potential explanatory variables – which might be demographic, behavioural, exposures, and so on.4,–,7 This understanding can be difficult to achieve because of the fact that the outcome is associated with numerous potential explanatory variables, and because, often, these …

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