Determining and visualising at-risk groups in case-control data

Case-control research is often exploratory; to determine factors that increase risk. Often, regression methods are used to determine combinations of risk factors that predispose to excess risk. Recently, tree-based methods have also been proposed. Both have limitations. An alternative approach is suggested, based on a search algorithm to identify at-risk subgroups.

Statistical methods to determine and visualise at-risk sub-groups in case-control studies are presented. The method of determining sub-groups--search partition analysis (SPAN)--searches among different Boolean combinations of risk factors. Sub-groups that have been identified are visualised by scaled rectangle diagrams. These show the size of sub-groups and the extent to which they overlap.

Theory is presented for applying the method to case-control data. The methods are illustrated by analysis of three case-control studies: one on sudden infant death syndrome, a second on heart disease and a third on child pedestrian injuries.

The methods provide a useful alternative to regression and tree-based analysis. They demarcate subgroups that, in the three examples, are easy to interpret and would not have been found by other methods. Scaled rectangle diagrams are a useful way to visualise the results.