TY - JOUR T1 - Modelling with multiple explanatory variables JF - Journal of Family Planning and Reproductive Health Care JO - J Fam Plann Reprod Health Care SP - 32 LP - 34 DO - 10.1136/jfprhc.2010.0034 VL - 37 IS - 1 AU - Pamela Warner Y1 - 2011/01/01 UR - http://jfprhc.bmj.com/content/37/1/32.abstract N2 - 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 View this table:In this windowIn a new window 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 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 … ER -