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Supp , rev'd on other grounds, F. The court held that each plaintiffs should meet at the preponderance of the evidence test for i probability of exposure to fallout resulting in doses in significant excess of background, ii injury is consistent with those known to be caused by ionizing radiation; iii and that the individual claimants resided near the Nevada Test Site for at least some, if not all, of the years when the tests were conducted.
The Allen court also recognized that lack of statistical significance does not mean that the adverse effect is not present. As a process for environmental decision-making, risk assessment combines reasoning with uncertain data complex mathematical and statistical methods to describe and extrapolate far from the relevant range of the observations, often basing those extrapolations on incomplete or limited theoretical bases.
The difficulties arise from having to base legal or policy decisions on scientific information and conjectures that can mystify, rather than clarify, the causal link sought. The discussions that follow use US law as examples.
The question that we are addressing is: What is the legal test that governs the admissibility of scientific evidence? In , the U. Supreme Court, in Daubert,35 addressed the extent to which scientific results could be allowed in court. Before Daubert was decided, it had been held, for instance given epidemiological evidence of the effect of Bendectin,36 that scientific evidence based on chemical structure activity, in vitro, animal tests, and recalculations that had not been peer-reviewed, did nott meet the test that governs the admissibility of the evidence, generally known as the Frye test.
The Frye test states that:. Somewhere in this twilight zone the evidential force of the principle must be recognized, and while the courts will go a long way in admitting expert testimony deduced from well-recognized scientific principle or discovery, the thing from which the deduction is made must be sufficiently established to have gained general acceptance in the particular field in which it belongs.
American Tobacco Co. Bowman v. Twin Falls, P. Johns-Manville Products Corp. Johns-Manville Sales Corp. Merrell Dow Pharmaceuticals, Inc. Weekk In Christophersen v. Allied-Signal, No. And yet, some American state courts have allowed into evidence scientific explanations that would fail Frye- type tests if those explanations are sound, adequately founded Witco Chemical, A.
Merrell Dow Pharmaceuticals, F. Pertinent evidence based on scientifically valid principles will satisfy those demands. The Court explains the role of peer-review as relevant, though not dispositive, consideration in assessing the scientific validity of a particular technique or methodology on which a particular technique is premised.
The Court went on to discuss the difference between legal and scientific analysis, and gave the reason for the difference, stating that scientific conclusions are subject to perpetual revision.
Law, on the other hand, must resolve disputes finally and quickly. The former is unquestionable; the latter is troublesome because the judicial process is notoriously slow. The proper inquiry into the scientific basis for any subsequent assertion of legall causation must explore often-uncharted paths through the maze of heterogeneous evidence.
The weight of each item into evidence as part of the proof must be determined through methods that are reproducible and coherent. It is appropriate to assign a higher degree of belief to peer-reviewed evidence than to other evidence. In Usery v. Turner Elkhorn Mining Co. Those are necessary to provide just compensation when uncertain etiological factors are associated with dreaded, fatal and causally complex diseases.
The statute is found at 33 U. IV, Direct legal evidence does not require inference: the facts speak for themselves. This simplicity is appropriate when causation is well established and the statistical problem is the measurement error only. Simplicity can fail in most environmental situations in which exposure is low and there are several causal factors: uncertain and multifactorial causation is the rule. A way to deal with causation is to take the strongg version of the preponderance of the evidence.
This test does not admit statistical correlations, such as regression models, without additional direct and actual knowledge of the causal relationship between the defendant's conduct and the plaintiff's injury. However, the weak version only suggests hypothesis generation, rather than causal association, such as that found in an ecologicall epidemiological study. Uncertainty and multiple and possibly heterogeneous factors41 can become important issues, as the law has long recognized.
The traditional rule in US administrative law makes the proponent of the rule bear the burden of proof; but the evidentiary standards are less demanding than in tort law. However, federal courts can review the record, including the scientific evidence, under the hard look theory44 Determining whether the agency's decision is supported by substantial evidence requires finding that the administrative record contain s respectable scientific authority supporting the agency's factual determinations.
Under the hard look, agency rulemaking will be sustained if: i there is a reasoned explanation of the basis of fact, ii its decision is supported by substantial evidence, iii other alternatives were explored, and given reason for their rejection, and iv the agency responded to public comments. For instance, medical evidence of exposure to lead and resulting anemia, other adverse effects on the red blood cells and neurological effects led the court to find that the US EPA had not acted unreasonably when the uncertainty of the adverse effects was large.
Environmental Protection Agency, F2d D. Moreover, the court held that substantial evidence, including experimental evidence The registrant, not the Agency, had the burden of proof because Congress had made it clear that the public was not to bear the risk of uncertainty concerning the safety of a covered poison. Oregon Natural Resources Council, U. The hard look review indicated that the allegedly important information was in fact less important than suggested by the claimants and that, although some other decision-maker might have reached a different conclusion, the agency did not make a clear error in judgmentt by not conducting more assessments.
Environmental Protection Agency, F. US Environmental Protection Agency. We should remember that there is a significant difference between tort law and environmental law.
Specifically, the courts are traditionally deferential towards rules and regulations issued by public agencies. For instance, in Chevron v. Judges are not expert in the field, and are not part of either political branch of the Government When a challenge to an agency construction of a statutory provision, fairly conceptualized, really centers on the wisdom of the agency's policy, rather than whether it is a reasonable choice within a gap left open by Congress, the challenge must fail.
Deference can result in a judicial unwillingness to deal with uncertain causation. For instance, the US Nuclear Regulatory Commission was held to be free to adopt conservative assumptions by risking error on the side of over-protection rather than under-protection However, for residents nearby a uranium mine with uncontrolled tailings, the radon-related risk had been calculated to be one in two-thousand six- hundred person-lifetimes exposure.
Deference to agency rulemaking remains strong. For instance, in Baltimore Gas and Electric, Co. CPSC, F. OSHA, F. Regulatory Commission had acted arbitrarily and capriciously. However, there must be a rational connection with Congressional intent.
That is, an agency rulemaking is limited by the objectives dictated by the legislation by the substantial evidence test applied to the facts, and by an arbitrary and capricious test that applies to policy judgments and informal rulemaking.
If an agency fails to comply with its own procedures, as in not submitting its findings to peer review, the court may find this error to be harmless, as the DC Circuit concluded in the review of the setting of the ozone standard, under the Clean Air Act. This view was developed in Gulf South Insulation, Inc. Consumer Product Safety Commission. The court struck the ban down because it held that the record as a whole did not support the agency's finding under the substantial evidence standard.
This standard, the court held, is more restrictive than the general standard of judicial review. A more complete statistical study such as a randomized study and a more thorough investigation of the biological basis associating formaldehyde exposures with cancer might have met the substantial evidence test. Thus, the US federal agency that has control over occupational risks, OSHA, may issue regulations reasonably necessary to reduce a significant riskk of material impairment. These regulations should be technologically and economically feasible.
Costle, 16 ERC D. They found that there was a failure on the part of those agencies involved in regulating this chemical, to follow established federal cancer policy. Moreover, the EPA apparently held meetings with industry representatives and allowed certain appointments of individuals who may be considered to have a conflict of interest in their duty to the agency.
It had calculated that exposure to three parts per million would result in an excess risk off fourr cancers per 1, 0 workers. Grave dangerr had been interpreted to mean that:. The past discussions indicate that precautionary legal principles have a fundamental link to scientific evidence and scientific causation. Thus, the combination of uncertain evidence and causation with legal tests balancing risks, costs and benefits points to the need for a framework for dealing with uncertainty and variability that is also consistent with any reasonable principle of precaution.
One such framework is outlined next. Similarly, probabilistic methods can be applied in dose-response assessment when there is an understanding of the important parameters and their relationships, such as identification of the key determinants of human variation for example, metabolic polymorphisms, hormone levels, and cell replication rates , observation of the distributions of these variables, and valid models for combining these variables.
With appropriate data and expert judgment, formal approaches to probabilistic risk assessment can be applied to provide insight into the overall extent and dominant sources of human variation and uncertainty. In doing this, it is important to note that analyses that omit or underestimate some principal sources of variation or uncertainty could provide a misleadingly narrow description of the true extent of variation and uncertainty and give decision-makers a false sense of confidence in estimates of risk.
Specification of joint probability distributions is appropriate when variables are not independent of each other. In each case, the assessment should carefully consider the questions of uncertainty and human variation and discuss the extent to which there are data to address them. Probabilistic risk assessment has been used in dose-response assessment to determine and distinguish the degree of uncertainty and variability in toxicokinetic and toxicodynamic modeling.
Although this. Ginners Assoc. Marshall, F. The current policy and associated guiding principles are not intended to apply to dose-response evaluations for human health risk assessment until this application of probabilistic analysis has been studied further. Thus, the likelihood of each risk is quantitatively characterized in the resulting estimates.
In addition, probabilistic analysis may avoid some potential problems of apparent overestimation of risk estimates from multiplying UFs uncertainty factors in a deterministic risk assessment. It will not necessarily result in different outcomes or decisions, nor are its implementation and use simple processes.
The accuracy of probabilistic analysis will still depend upon the quality of the data used for the analysis. Moreover, in some situations, a probabilistic analysis may not be appropriate.
For example, at cleanup sites such as Superfund, Resource Conservation and Recovery Act, or Brownfields sites , the resident populations may not have the wide-ranging susceptibilities that would warrant conducting a probabilistic risk assessment.
Dealing with risk is a task that requires representations of uncertainty and variability that are often represented by probabilities and distributions. Although there are other measures that can be used to describe and account for uncertainty, we take probability measures and their calculus as a suitable means to accomplish this science-policy task.
To begin with, there are different views of probabilities. They have been understood as either subjective measures of belief or objective as the result of the long-term replication of the same physical experiment. It is knowledge. Probabilities, pr, are dimensionless numbers between 0 and 1, including these two limits. Frequencies, fr, are also dimensionless numbers between 0 and 1, and include these two limits. Both of these numbers are ordinal and use the same calculus.
Ordinal means that it cannot be said that the probability of 1 is twice the probability. Press, Cambridge When such comparisons are needed, probability numbers can be transformed to yield a cardinal number that permits comparisons such as twice as large. The transformation, for exponential failure rates, is a hazard rate:.
At low probability numbers, the transformation is unnecessary because r pr is approximately equal to the probability number pr. As Cox discusses, r pr is the value of the continuous-time hazard rate which, if constant over the period of time of interest to the assessment, yields the instantaneous probability of adverse response. For activity A the constant hazard rate is: [-ln 1- 0.
Suppose that activity 1 has a 0. The hazard rate for activity 1 is: [-ln Twice 1. In the next sub-section we will discuss how probability measures can have different interpretations. These are briefly discussed in the next sub-section, which is provided as an introduction.
The important matter to keep in mind is that degrees of personal belief can measure uncertainty about specific events or propositions, without having to use the probabilistic interpretations via frequencies, but including them.
It is important to understand some of those views because, in many circumstances, risk assessors and managers must deal with different justifications of uncertainty or variability that are represented by probabilities.
Among the views, the following three stand out. View 1. According to von Mises, a probability measure is defined as an analytical proposition, which is associated with some physicall meaning. That is, one speaks of probability of…, not of probability. Moreover, in this view, there no probability that.
For completeness, consider a random variable that has a Poisson distribution. Thus, a probability has a long-run characteristic: it is the limit of a relative frequency. This limit tends to infinity and the relative frequency tends to a constant value. The critical element in this view is the uniformity of the ensemble the set of elements over which the probability is calculated and the independence of the limit from the placement of the elements within the ensemble.
This is what classical statistical analysis deals with, as studied by Fisher, Kolmogorov, Neyman, Pearson and others. View 2. Ramsey and de Finetti de Finetti, developed the subjective view of probabilities in the s. Subjective probabilities are qualitative because they are formed through human experiences but are measured by numbers.
These qualitative probabilities require coherence, which is the condition that no one would opt for a bet that results in a sure loss, given the choice of a lottery with at least one positive outcome. If this rational requirement is met, de Finetti demonstrated that the numbers associated with the lottery are consistent with the axioms of finitely additive probability measures.
The interpretation of subjective probabilities is that 1 is full belief, 0 is its contradiction; numbers in between these are statements of a degree of uncertainty these numbers are partiall beliefs. For Ramsey, a probability as a degree of belief is a causal property, measured by a willingness and ability to act on a choice plus specific, but possibly hypothetical, circumstances. The odds of an outcome measure the intensity of belief associated with that outcome, which can be prospective or yet unrealized.
The fact that a hypothetical situation can be considered is consistent with the nature of choices. That is, a choice does have to be made. An issue with such personal but informed basis for belief is that bets must be comprehensible and must be backed by sufficient wealth to pay-up in the event of a loss. This view of probability has lead to Bayesian analysis, discussed later in this chapter. View 3. For example, there is no indeterminacy in classical physics.
In other words, the laws of physics determine exactly future outcome, given the initial conditions and laws of motion. Although an outcome as the sudden collapse of a walk-way that kills tens of people can be predicted using calculations involving strength of materials, dynamics and so on, reversing that failure is extremely improbable.
This is the domain of statistical mechanics, developed by Maxwell, Boltzman and Gibbs, in which the probability of events is based on generalizations and irreversibility, but can also account for reversibility if a lower level of generalization is adopted Guttman, On the other hand, indeterminacy can governs, as we know from the indeterminacy or uncertainty principle of Heisenberg.
For the moment, we can discuss uncertainty as the combination of:. Scientific conjectures. These arise when causal models are unknown a conjecture is a theory that has an equal chance of being right or wrong. System model misspecification. It consists of the exclusion of fundamental components in a multi-component model. Specification refers to the choice of mathematical form linear, polynomial, and so on and the variables excluded: relevant explanatory variables that cannot be accounted for by random error.
For example, a choice of dose should account for biochemical changes that occur after exposure as the chemical moves through physiological and biochemical pathways to reach the target organ.
Statistical uncertainty. This is a familiar form of uncertainty that generally refers to the natural variability of data e. Probabilistic reasoning helps to resolve these difficulties. A form of such reasoning is known as Bayesian reasoning. Its central aspect is reliance on the combination of expert judgment expressed as prior probabilities or distributions and experimental data the likelihood to for a posterior probability or distribution. Specifically, four concepts that characterize Bayesian analysis are:.
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