Predicting Cell Capture from Dilute Samples for Microfluidic Biosensors
Nathan Mosier (Purdue) and Bruce Craig (Purdue)

Detecting low numbers of organisms in large volumes of liquids is a challenge for the food industries, especially where food safety is concerned. The detection of microbial contamination or the presence of pathogens requires that the food be sampled, the sample be processed and the cells concentrated, and the final concentrate be assayed to detect living cells. The prediction of the minimal sample volume required to enable detection of a specified microorganism must be carefully carried out, so that the probability of detection meets pre-determined criteria.

 

Risk as Analysis and Risk as Feelings: Some Thoughts about Affect, Reason, Risk, and Rationality
Paul Slovic (University of Oregon)

Modern theories in cognitive psychology and neuroscience indicate that there are two fundamental ways in which human beings comprehend risk. The “analytic system” uses algorithms and normative rules, such as the probability calculus, formal logic, and risk assessment. It is relatively slow, effortful, and requires conscious control. The “experiential system” is intuitive, fast, mostly automatic, and not very accessible to conscious awareness. The experiential system enabled human beings to survive during their long period of evolution and remains today the most natural and most common way to respond to risk. It relies on images and associations, linked by experience to emotion and affect (a feeling that something is good or bad). This system represents risk as a feeling that tells us whether it’s safe to walk down this dark street or drink this strange-smelling water. Proponents of formal risk analysis tend to view affective responses to risk as irrational. Current wisdom disputes this view. The rational and the experiential systems operate in parallel and each seems to depend on the other for guidance. Studies have demonstrated that analytic reasoning cannot be effective unless it is guided by emotion and affect. Rational decision making requires proper integration of both modes of thought. Both systems have their advantages, biases, and limitations. Now that we are beginning to understand the complex interplay between emotion and reason that is essential to rational behavior, the challenge before us is to think creatively about what this means for managing risk. On the one hand, how do we apply reason to temper the strong emotions engendered by some risk events? On the other hand, how do we infuse needed “doses of feeling” into circumstances where lack of experience may otherwise leave us too “coldly rational.” My talk addresses these important questions.

 

Energy System Risk Assessment
James D. McCalley, Professor of Electrical and Computer Engineering, Iowa State University

The economic health of a nation is heavily dependent on the price and reliability of electric energy. These twin attributes, price and reliability, are determined by a continuous series of interrelated > decisions that span multiple functions, organizations and time frames. At the heart of these decisions are the determination of risk, risk tolerance, and appropriate tradeoffs between immediate costs and risk exposure. In this talk, the different decision problems will be identified, including

1. Transmission control center economy/security system maneuvering

2. Responding to low probability, high consequence events with blackout potential

3. Maintenance: maximizing cumulative risk reduction with limited resources

4. Investing in capital-intensive infrastructure under uncertainty

5. Reliability and economy of the national bulk energy transportation system: electric, gas, and coal.

Critical to each of the above problems is the ability to assess scenario likelihood. Several contingency probability estimation methods will be described.

James D. McCalley is professor and associate chair of Electrical and Computer Engineering at Iowa State University, where he has been employed since 1992. Dr. McCalley was employed with Pacific Gas and Electric Company from 1985-1990 as a transmission planning engineer. He graduated with his BSEE, MSEE, and PhD degrees from Georgia Tech in 1982, 1986, and 1992, respectively. He holds a professional engineering license and is a fellow of the Institute of Electrical and Electronics Engineers (IEEE). He currently serves as chair of the IEEE Power Engineering Society (PES) Risk, Reliability, and Probability Subcommittee, and he is Editor-in-Chief of IEEE PES Letters.

 

From an "Essentially Useless Exercise" to Ground Zero in the Sound Science Wars: The Changing Law of Risk Analysis
Christopher Schroeder, (Duke Law School)

When the major US environmental statutes were enacted in the early 1970s, none of them required that risk assessments be performed prior to establishing an important emissions or exposure standard. In 1976, a House Committee issued a report highly skeptical of the value of risk assessments, declaring risk analysis “an essentially useless exercise” when dealing with matters of environmental health and safety. A succession of Presidents, one influential Supreme Court decision and powerful interest groups disagreed. Risk assessments subsequently became absolute preconditions for major environmental health regulations. Initially, industry saw risk assessments, together with the risk-benefit analyses that they facilitated, as ways to prevent onerous regulations. This attitude changed during the 1980s when risk assessments played significant roles in justifying some tougher standards. Industry then modified its strategy, and started attacking agency approved risk assessments for being poorly done. This has made risk assessments a central battlefield in the sound science wars. Efforts to write specific risk assessment techniques or decisions into statutory law were a point of contention in the regulatory reform efforts of the 1994-95 Congress. Unable to achieve legislative success then, industry and its supporters have now found a powerful ally in the Office of Management and Budget, which has been using a two-paragraph rider to an appropriations bill, known as the Information Quality Act, as justification for policing the risk assessment process. OMB's efforts as well as other legal developments are placing ever greater numbers of legal constraints on government agency use of risk assessments.

 

Some Perspectives On Evaluating The Risks Of Medical Products
Susan Ellenberg (University of Pennsylvania, School of Medicine)

The safety of commonly used drugs and other medical products is a topic of legitimate public concern. Recent attention has been given to cardiovascular problems with the COX-2 inhibitors and suicidality with SSRI antidepressants, but there have been many other issues in the past (Halcion and neurological disturbance; fen-phen and heart valve damage; alosetron and intestinal damage; accutane and suicidality; and, of course, vaccines and a wide range of problems (of which autism is currently at the top of the list) that have been similarly publicized. Some of the problems that have gained public attention have been generally accepted as real, but many others have not. Evaluating product safety is a complicated process; the level of attention given by quantitative scientists to post-marketing safety surveillance, however, has paled in comparison to that given to the design, conduct and analysis of clinical trials. In this presentation I will discuss the complexities of monitoring product risk, provide an overview of some of the statistical procedures that have been proposed for use with spontaneous reporting systems for post-market surveillance, and address proposals for improvements in safety monitoring systems.

 

Overarching Issues in Pharmaceutical Risk
Robert Obenchain (Eli Lilly and Company)

The three primary issues I will concentrate upon concern head-to-head comparisons of a pair of competing pharmaceuticals (treatments.) I will start by describing the need to continue recent trends in development of statistical methods for robust analysis of data from non-randomized human studies. Instead of being based upon traditional model equations (covariate adjustment), methods based upon patient clustering and matching in X-covariate space are ideal for identifying important patient subgroups precisely because they proceed by forming, comparing and re-combining subgroups. The second issue is the need for modern terminology for even-handed testing of composite hypotheses, and the third issue is the need to postpone decisions when cost-effectiveness data are insufficient. All three issues share the common theme that statistical methods need to become more-and-more realistic (rather than less-and-less) as diverse data accumulate towards a "dense information" limit.