gms | German Medical Science

51. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (gmds)

10. - 14.09.2006, Leipzig

Relation of Probability and Causation to Relative Risk and Doubling Dose

Meeting Abstract

Suche in Medline nach

  • Karl-Heinz Jöckel - Universitätsklinikum Essen, Essen
  • Markus Neuhäuser - Universitätsklinikum Essen, Essen

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (gmds). 51. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. Leipzig, 10.-14.09.2006. Düsseldorf, Köln: German Medical Science; 2006. Doc06gmds114

Die elektronische Version dieses Artikels ist vollständig und ist verfügbar unter: http://www.egms.de/de/meetings/gmds2006/06gmds115.shtml

Veröffentlicht: 1. September 2006

© 2006 Jöckel et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

According to Egilman et al. [1] court decisions refer to a “doubling of the risk” to quantify the causal relationship between exposure and disease in an individual. When courts use this term “doubling of the risk”, they are often speaking of the risk fraction (RF) which is defined as

Equation 1 ,

where d stands for dose and RR(d) for the relative risk comparing exposed and unexposed populations depending on dose d. The RF may be used as a proxy to the probability of causation (PC). This probability is nothing what we can observe, although we have to derive it from observational studies. What we need is a theoretical construct.

Under relatively simple, realistic, and understandable conditions we show that in case of a rare disease the RF for an infinitesimal small time window is exactly the PC. Hence, it seems justified to consider Equation 2 as a universal measure for PC if the relative risk model holds true and the disease is rare.

This model, for which RF = PC can be shown, is one of an infinitude of models discussed by Robins and Greenland [2], [3]. Most of the models do not have the characteristic RF = PC. Our approach demonstrates that relatively simple, realistic, and understandable conditions give a model with RF = PC. However, in general, the unreflecting use of the risk fraction as a surrogate for the probability of causation may give misleading results.


References

1.
Egilman D, Kim J, Biklen M. Proving causation: the use and abuse of medical and scientific evidence inside the courtroom – an epidemiologist’s critique of the judical interpretation of the Daubert ruling. Food and Drug Law Journal 2003, 58:223-250.
2.
Robins J, Greenland S. The probability of causation under a stochastic model for individual risk. Biometrics 1989, 45:1125-1138.
3.
Robins J, Greenland S. Estimability and estimation of excess and etiologic fractions. Statistics in Medicine 1989, 8:845-859.