Literature DB >> 10025652

The importance of specifying the underlying biologic model in estimating the probability of causation.

J Beyea1, S Greenland.   

Abstract

There are a number of contexts in which interested parties wish to estimate the probability that an individual's injury was caused by radiation or a toxic chemical. It has been shown, however, that such calculations cannot be made based on epidemiologic data alone, without assumption of a biologic model for the disease process and without a specific definition of causation. To illustrate the relevant theorems, we present a number of examples in which different biologic models produce different values for the probability of causation for individuals from the same population-based epidemiologic data and dose-response curves. As a result of these ambiguities, it is important that anyone attempting to calculate probability of causation for individuals explicitly state the biologic model that has been assumed, as well as state the definition of causation being used. The analyst should test the robustness of the calculations by repeating them for a broad range of underlying biologic models.

Mesh:

Year:  1999        PMID: 10025652     DOI: 10.1097/00004032-199903000-00008

Source DB:  PubMed          Journal:  Health Phys        ISSN: 0017-9078            Impact factor:   1.316


  15 in total

1.  Relation of probability of causation to relative risk and doubling dose: a methodologic error that has become a social problem.

Authors:  S Greenland
Journal:  Am J Public Health       Date:  1999-08       Impact factor: 9.308

Review 2.  Causation in epidemiology.

Authors:  M Parascandola; D L Weed
Journal:  J Epidemiol Community Health       Date:  2001-12       Impact factor: 3.710

3.  Exposure-lag-response associations between lung cancer mortality and radon exposure in German uranium miners.

Authors:  Matthias Aßenmacher; Jan Christian Kaiser; Ignacio Zaballa; Antonio Gasparrini; Helmut Küchenhoff
Journal:  Radiat Environ Biophys       Date:  2019-06-19       Impact factor: 1.925

4.  Heterogeneity of cancer risk due to stochastic effects: emphasis on radiation-induced effects.

Authors:  Wolfgang F Heidenreich
Journal:  Radiat Environ Biophys       Date:  2006-04-05       Impact factor: 1.925

5.  Implications of Recent Epidemiological Studies for Compensation of Veterans Exposed to Plutonium.

Authors:  Jan Beyea
Journal:  Health Phys       Date:  2022-05-20       Impact factor: 2.922

6.  Author's response to Poole, C. Commentary: How Many Are Affected? A Real Limit of Epidemiology.

Authors:  Nicolle M Gatto; Ulka B Campbell; Sharon Schwartz
Journal:  Epidemiol Perspect Innov       Date:  2010-08-26

7.  Redundant causation from a sufficient cause perspective.

Authors:  Nicolle M Gatto; Ulka B Campbell
Journal:  Epidemiol Perspect Innov       Date:  2010-08-02

8.  The impact of mental illness on potentially preventable hospitalisations: a population-based cohort study.

Authors:  Qun Mai; C D'Arcy J Holman; Frank M Sanfilippo; Jonathan D Emery
Journal:  BMC Psychiatry       Date:  2011-10-10       Impact factor: 3.630

9.  Joint Statement of the Ad-hoc Committee of the Korean Society for Preventive Medicine and the Korean Society of Epidemiology on Tobacco Lawsuits on the Causal Link Between Tobacco Smoking and Lung Cancer.

Authors:  Ad-Hoc Committee Of The Korean Society For Preventive Medicine And The Korean Society Of Epidemiology On Tobacco Lawsuits Ad-Hoc
Journal:  Epidemiol Health       Date:  2015-06-15

10.  Joint Statement of the Ad-hoc Committee of the Korean Society for Preventive Medicine and the Korean Society of Epidemiology on Tobacco Lawsuits on the Causal Link Between Tobacco Smoking and Lung Cancer.

Authors: 
Journal:  J Prev Med Public Health       Date:  2015-05
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.