Geir E Eide1. 1. Centre for Clinical Research, Haukeland University Hospital, Bergen, Norway. Geir.Egil.Eide@Haukeland.No
Abstract
INTRODUCTION: The attributable fraction (AF) is used for quantifying the fraction of diseased ascribable to one or more exposures. The methodology and software for its estimation has undergone a considerable development during the last decades. OBJECTIVES: To introduce methods for: (i) apportioning excess risk to multiple exposures, groups of exposures and subpopulations; (ii) graphical description; and (iii) survival data. RESULTS: Adjusted, sequential and average AFs are reasonable measures obtainable with standard software. The latter two both sum up to the combined AF for a set of exposures. The average AFs are independent of the exposures' ordering. For an ordered, preventive strategy, scaled sample space cubes illustrate the effects on the risk of disease from stepwise exposure removal. Pie charts illustrate the portions of the total risk ascribed to different exposures or risk-profiles. Attributable hazard fraction, AF before time t, and AF within study incorporate time to disease and interventions. CONCLUSIONS: The practice of crude calculations of AFs in epidemiology should be abandoned. Further development of methods for AFs with survival data and possibly linking it to causal modelling is of interest.
INTRODUCTION: The attributable fraction (AF) is used for quantifying the fraction of diseased ascribable to one or more exposures. The methodology and software for its estimation has undergone a considerable development during the last decades. OBJECTIVES: To introduce methods for: (i) apportioning excess risk to multiple exposures, groups of exposures and subpopulations; (ii) graphical description; and (iii) survival data. RESULTS: Adjusted, sequential and average AFs are reasonable measures obtainable with standard software. The latter two both sum up to the combined AF for a set of exposures. The average AFs are independent of the exposures' ordering. For an ordered, preventive strategy, scaled sample space cubes illustrate the effects on the risk of disease from stepwise exposure removal. Pie charts illustrate the portions of the total risk ascribed to different exposures or risk-profiles. Attributable hazard fraction, AF before time t, and AF within study incorporate time to disease and interventions. CONCLUSIONS: The practice of crude calculations of AFs in epidemiology should be abandoned. Further development of methods for AFs with survival data and possibly linking it to causal modelling is of interest.
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