title: Determining and visualising at-risk groups in case-control data.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Community Health, University of Auckland, New Zealand.
authors.forename: R J
authors.initials: RJ
authors.lastname: Marshall
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data; D000465:Algorithms; D016022:Case-Control Studies; D015340:Epidemiologic Research Design; D006331:Heart Diseases / Q000453:epidemiology; D006801:Humans; D007231:Infant, Newborn; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D013398:Sudden Infant Death / Q000453:epidemiology