title: Generalized approaches to partitioning the attributable risk of interacting risk factors can remedy existing pitfalls.
abstract: OBJECTIVE
  To clarify the properties of different approaches to estimate the contribution of single-risk factors to the disease load in a population.
  
  
  METHODS
  Three methods of partitioning attributable risks are reviewed and two additional procedures as modifications of the existing algorithms are introduced. Basis properties of the approaches are outlined in the simplest setting with two exposure variables. The extension to more complex settings is illustrated by an example involving three risk factors.
  
  
  RESULTS
  The quantification of the impact of single-risk factors can vary considerably according to the method used. Different orderings of the risk factors with respect to their impact can occur. Approaches can be classified according to two features: (i) inclusion or exclusion of partial interactions between risk factors, (ii) equal or proportional distribution of the surplus resulting from the combined action of risk factors. Practical applications have to carefully consider intrinsic limitations of all partitioning approaches.
  
  
  CONCLUSIONS
  The decision on which concept to use when partitioning attributable risks on the population level should be based on the desired properties the solution ought to have. Arguments from game-theoretical reasoning can help to guide further research in this area, especially in exploring the methods using proportional division rules that are not yet fully understood.
authors.affiliation: Department of Medical Informatics, Biometry and Epidemiology, University of Erlangen-Nuremberg, Waldstrasse 6, 91054 Erlangen, Germany. christina.rabe@imbe.imed.uni-erlangen.de
authors.forename: Christina
authors.initials: C
authors.lastname: Rabe
authors.forename: Andrea
authors.initials: A
authors.lastname: Lehnert-Batar
authors.forename: Olaf
authors.initials: O
authors.lastname: Gefeller
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D004194:Disease / Q000209:etiology*; D015340:Epidemiologic Research Design; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors