Attributable Proportion Under the Exposed The attributable proportion under the exposed (APe ) is an indication of the proportion of patients who were exposed to the potential cause and got sick because of this exposure. It can only be used if the RR >1 and can be calculated by [(RR-1)/RR X 100\%]. When the CRR is based on an RR, these formulae also apply to the CRR. The result of the analysis, given as an RR, CRR, or APe , meets the legal standard of what is “more likely true than not,” when the RR or CRR is >2.0 (with a 95\% confidence interval lower boundary of >1.0), or the APe is >50\%. The APe is also known as the "Probability of Causation (PC)" a term that is defined in the US Code of Federal Regulations (Federal Register / Vol. 67, No. 85 / Thursday, May 2, 2002 / Rules and Regulations p. 22297) and elsewhere. United States Case Law on Injury Causation Methodology The 3-step methodology was challenged in United States District Court for the District of Colorado in Etherton v Auto-Owners Insurance Company. The defendant challenged, among other things, the reliability and fit of the methods described by the expert. After an extensive examination and discussion of the 3-step process used by the expert, the Court found that the methodology appropriately fit the specific facts of the case, and that a population-based (epidemiologic) approach was an appropriate part of the causal methodology. The Court denied the Defendant ’s motion to strike the expert’s testimony in the order, which was entered on 3/ 3l / l4. The Defendant appealed the ruling from the District Court, and in July 2016, the Tenth Circuit U.S. Court of Appeals affirmed the 3-step causal methodology as generally accepted and well established for assessing injury causation, under the Daubert standard. See Etherton v. Auto-Owners Insurance Company, No. 14-1164 (10th Cir, 7/ l9 / l6 ). Test Accuracy Test accuracy investigation is a standard practice in clinical epidemiology. In this setting, a diagnostic test is scrutinized to determine by various measures how often a test result is correct. In FE the same principles are used to evaluate the accuracy of proposed tests leading to conclusions that are central to fact finder determinations of guilt or innocence in criminal investigations, and of causality in civil matters. The utility of a test is highly dependent on its accuracy, which is determined by a measure of how often a positive or negative test result truly represents the actual status that is being tested. For any test or criterion there are typically four possible results: (1) a true positive (TP), in which the test correctly identifies tested subjects with the condition of interest; (2) a true negative (TN), in which the test correctly identifies test subjects who do not have the condition of interest; (3) a false positive (FP), in which the test is positive even though condition is not present, and; (4) a false negative (FN) in which the test is negative even though the condition is present. Fig. 3.19 is a contingency table illustrating the relationships between test results and condition presence, as well as the following test accuracy parameters: Sensitivity (the rate at which the test is positive when the condition is present) TP/(TP + FN) Specificity (the rate at which the test is negative when the condition is absent) TN/(TN + FP) Positive predictive value (the rate at which the condition is present when the test is positive) TP/(TP + FP) Negative predictive value (the rate at which the condition is absent when the test is negative) TN/(TN + FN)
Attributable proportion under the exposed The attributable proportion under the exposed (APe ) is an indication of the proportion of patients who were exposed to the potential cause and got sick because of this exposure. It can only be used if the RR >1 and can be calculated by [(RR-1)/RR X 100\%]. When the CRR is based on an RR, these formulae also apply to the CRR. The result of the analysis, given as an RR, CRR, or APe , meets the legal standard of what is “more likely true than not,” when the RR or CRR is >2.0 (with a 95\% confidence interval lower boundary of >1.0), or the APe is >50\%. The APe is also known as the "Probability of Causation (PC)" a term that is defined in the US Code of Federal Regulations (Federal Register / Vol. 67, No. 85 / Thursday, May 2, 2002 / Rules and Regulations p. 22297) and elsewhere. United States case law on injury causation methodology The 3-step methodology was challenged in United States District Court for the District of Colorado in Etherton v Auto-Owners Insurance Company. The defendant challenged, among other things, the reliability and fit of the methods described by the expert. After an extensive examination and discussion of the 3-step process used by the expert, the court found that the methodology appropriately fit the specific facts of the case, and that a population-based (epidemiologic) approach was an appropriate part of the causal methodology. The court denied the defendant ’s motion to strike the expert’s testimony in the order, which was entered on 3/ 31 / 14. The Defendant appealed the ruling from the District Court, and in July 2016, the Tenth Circuit U.S. Court of Appeals affirmed the 3-step causal methodology as generally accepted and well established for assessing injury causation, under the Daubert standard. See Etherton v. Auto-Owners Insurance Company, No. 14-1164 (10th Cir, 7/ 19 / 16 ). Test accuracy Test accuracy investigation is a standard practice in clinical epidemiology. In this setting, a diagnostic test is scrutinized to determine by various measures how often a test result is correct. In FE the same principles are used to evaluate the accuracy of proposed tests leading to conclusions that are central to fact finder determinations of guilt or innocence in criminal investigations, and of causality in civil matters. The utility of a test is highly dependent on its accuracy, which is determined by a measure of how often a positive or negative test result truly represents the actual status that is being tested. For any test or criterion there are typically four possible results: (1) a true positive (TP), in which the test correctly identifies tested subjects with the condition of interest; (2) a true negative (TN), in which the test correctly identifies test subjects who do not have the condition of interest; (3) a false positive (FP), in which the test is positive even though condition is not present, and; (4) a false negative (FN) in which the test is negative even though the condition is present. Fig. 3.19 is a contingency table illustrating the relationships between test results and condition presence, as well as the following test accuracy parameters: Sensitivity (the rate at which the test is positive when the condition is present) TP/(TP + FN) Specificity (the rate at which the test is negative when the condition is absent) TN/(TN + FP) Positive predictive value (the rate at which the condition is present when the test is positive) TP/(TP + FP) Negative predictive value (the rate at which the condition is absent when the test is negative) TN/(TN + FN)
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