bluuebunny/crossref_metadata_2025 huggingface dataset:
Probabilistic Risk Assessment
Authors: Modarres Mohammad
Published: September 2014

This article summarizes the formal approach to probabilistic risk assessment (PRA) of complex engineered systems. It is important to stress that the main objective of performing a PRA is not necessarily the estimation of the actual risk values; rather it is the determination of those elements of the system that substantially contribute to the estimated risks, characterization of uncertainties associated with such estimates, and assessment of the effectiveness of the various risk reduction strategies available. Performing PRA for a complex engineered system is a considerably intense and time‐consuming process that requires coordinated cooperation of different individuals and subject matter experts from research, engineering, and operations all the way to decision‐making and regulatory authorities. The main goal of this article, however, is to provide the readers with a high‐level, application‐independent overview of the major elements of a formal PRA. In the first section, critical concepts have been defined and the PRA process has been outlined. In the remainder of the article, a step‐by‐step procedure for conducting a PRA has been discussed, including limitations of each step. In each section, appropriate references for further details about the PRA process have been discussed.
Design of Reliability Tests
Authors: Kececioglu Dimitri B. , Chen Xiaofang
Published: September 2014

Reliability is the probability that a product can perform its required functions for a specified function period without failure under certain environments and stress levels. Reliability information is very important to manufacturers. It enables them to produce better products for their costumers. Reliability tests are necessary in designing and manufacturing a product to guarantee its reliability and quality. This article introduces the basic ideas of how to design reliability tests, their objectives, test types, and test procedures.
Burn‐in Testing: Its Quantification and Applications
Authors: Kececioglu Dimitri B. , Chen Xiaofang
Published: September 2014

Early life failures are usually attributed to problems in the manufacturing, packaging, and assembling phases. After making improvements to the greatest possible extent in these phases, the only way to improve the product's reliability is to eliminate defective units before they reach customers. A common approach used to weed out defective and weak parts before shipping the products to customers is burn‐in testing. Burn‐in testing has become a frequently used procedure in many industries, especially the semiconductor industry. In this article, an overview of burn‐in testing is presented and its applications are explained. Burn‐in methods are related to a variety of failure mechanisms and the conditions under which the tests are run are specified. Finally, the burn‐in time is determined through a reliability bathtub curve model to be the time after which the failure rate remains constant.
Accelerated Life Testing
Authors: Donahoe Daniel , Zhao Ke , Murray Steven , Ray Rose M.
Published: September 2014

Accelerated testing is a set of methods that attempts to replicate and predict the quantitative effect on product life of what are assumed to be the most important degradation mechanisms determining product life in actual service. In all accelerated testing approaches, one or more physical degradation methods are applied to promote failure. The history and limitations of accelerated testing and mathematical tools for modeling product lifetime as a function of acceleration mechanisms are discussed.
Systems Reliability
Authors: Andrews John D.
Published: September 2014

It is necessary to make predictions of the failure frequency or likelihood of systems operating in many industrial sectors. The techniques available to conduct this type of assessment require the component failure and repair characteristics along with information defining the system structure. This article describes the features of some of the techniques that can be used to conduct such system reliability assessments. For each method discussed, an overview of its capability is provided. Consideration is given to the use of systems reliability assessment techniques in the context of risk assessment. Finally, a discussion addresses when each of the methods described should be used.
Reliability of Large Systems
Authors: Kołowrocki Krzysztof
Published: September 2014

This article is concerned with the application of limit reliability functions to the reliability evaluation of large systems. Two‐state large nonrepaired systems composed of independent components are considered. The asymptotic approach to the system reliability investigation and the system limit reliability function are defined. Two‐state homogeneous series and parallel systems are defined and their exact reliability functions are determined. The classes of limit reliability functions of these systems are presented. This article contains an exemplary application of the presented facts to the reliability evaluation of large technical systems. The accuracy of this evaluation is illustrated. Brief review and the latest references on limit reliability functions of two‐state and multistate homogeneous and nonhomogeneous series, parallel, and other systems are presented as well.
Repairable Systems Reliability
Authors: Rigdon Steven E.
Published: September 2014

Nonrepairable systems fail just once, so models for nonrepairable systems must account for random lifetimes. It is often reasonable to assume that different units have random lifetimes that are independent and follow the same distribution, leading to the usual i.i.d. (independent and identically distributed) assumption. By contrast, models for repairable systems must account for successive failures in time. For a given system, these times between failure are often not independent and not identically distributed. Various assumptions about the failure process lead to different models for repairable systems. In this article, we present the nonhomogeneous Poisson process (NHPP), the renewal process, the piecewise exponential model, and the modulated power law process (a compromise between renewal process and NHPP). Inference for a single system as well as for multiple copies of a system is discussed. We also discuss briefly the use of covariates or concomitant variables.
Reliability of Consumer Goods with “Fast Turn Around”
Authors: Ouden Elke Den , Lu Yuan , Brombacher Aarnout
Published: September 2014

Development of consumer goods that meet the buying and using expectations of the consumers is no longer a straightforward job. Technology becomes available at increasing pace, while at the same time the product life cycles ever shortening. Companies are challenged to bring innovative products faster to the market, with inherently higher uncertainties on the consumer expectations of these products. In this context, reliability has a different meaning and the design methods and tools required to manage reliability, as well as test strategies in this context, are different from the classical ones.
Non‐ and Semiparametric Models and Inference for Reliability Systems
Authors: Peña Edsel A. , Taylor Laura L.
Published: September 2014

Precise and reliable knowledge of the performance of deployed systems in the engineering, biomedical, public health, military, economic, and business areas enables an informed assessment of the risk of failure and/or the quality of systems. In the study of the risk and the quality of such systems, a conceptual model is that of reliability systems. This article discusses non and semiparametric modeling and inference of reliability systems. In the first section, a review of some basic notions of reliability systems is presented. The next two sections provide nonparametric models when there are no covariates impacting the performance of the system, and the methods of estimating the survivor function for the system life based on possibly right‐censored data. An illustrative example is given for a randomly generated dataset. The next section then discusses semiparametric models, which incorporates covariates, with primary focus on the Cox's proportional hazards model. Inference procedures for these semiparametric models are discussed. Finally, mention is made of dynamic models which are those that incorporate the changes occurring dynamically in a system, as time progresses.
Repair, Inspection, and Replacement Models
Authors: Coolen‐Schrijner Pauline
Published: September 2014

We discuss some basic maintenance models for stochastically deteriorating systems. Optimization of maintenance models with respect to a certain objective function gives the optimal maintenance policy over a predefined time horizon. We consider models involving repair, inspection, and replacement actions.
Wear
Authors: Bérenguer Christophe , Grall Antoine
Published: September 2014

This article presents an overview of probabilistic wear models based on lifetime distributions or stochastic processes and discusses their interest in wear‐dependent reliability modeling and condition‐based maintenance evaluation.
Degradation and Shock Models
Authors: Finkelstein Maxim
Published: September 2014

The standard assumption in shock models are that the failure of a system is caused either by the cumulative effect of a large number of shocks or by a shock that exceeds a certain critical level. In this paper, we discuss both possibilities and consider some generalizations of existing models. Two generic point processes of shocks are considered: the renewal and the Poisson processes. When the average number of renewals to reach the degradation threshold of accumulated damage is large, the asymptotic renewal theory is used for obtaining the first passage probabilities. For the specific case of the homogeneous Poisson process of shocks, some new models with different criterions of failure are considered. For instance, we consider the setting when the failure occurs only when the successive shocks are “closer” than the predetermined value (deterministic or random).
Reliability Data
Authors: Moss Thomas R.
Published: September 2014

Understanding the reliability characteristics of equipment is important when making decisions concerning the safety and productivity of an industrial plant. Good quality data is vital to this process. This article discusses methods for the analysis of the reliability data required for input to predictive models and the in‐service monitoring of plant and equipment reliability performance.
Lifetime Models and Risk Assessment
Authors: Lee Mei‐Ling Ting
Published: September 2014

In this article we review several methods used in analyzing lifetime data. Lifetime data have found many applications in biomedical research, engineering, as well as in social sciences. Lifetime data can be defined as the times to the occurrence of a certain event. Statistical methods for analyzing lifetime data include the definition of survival time, censored data, prediction of probability of survival, prediction of instantaneous hazards, and evaluation of cure rates. We review different types of lifetime data, methods for estimating hazard and survival functions, methods for risk assessment, as well as multivariate methods for survival data. Lifetime models based on stochastic processes hitting a boundary, such as threshold regression for risk assessment, are also discussed.
Managing Infrastructure Reliability, Safety, and Security
Authors: Zimmerman Rae
Published: September 2014

Infrastructure reliability, in its most general sense, refers to whether a system performs as expected. Reliability is relative, depending on the perspective of the infrastructure user. Safety and security are key components for the concept of reliability, since they pertain to basic survival, and are prerequisites for others. Safety and security are defined, and their emergence as core concepts in infrastructure reliability is described. Infrastructure security now relates to a set of critical infrastructures. Factors that potentially compromise infrastructure security include how infrastructure is spatially distributed and interrelated or interdependent with other infrastructures. Risk‐based analytical approaches, and alternatives to these approaches, have arisen to address the likelihood and form of attacks as well as the consequences of attacks on infrastructure. Selected examples of these methodologies are presented. Resource allocation at the federal level emphasizes a risk‐based approach, though the nature of risks faced by critical infrastructures, how these risks should be quantified, what analytical tools should be used to produce such estimates, at what geographic scale, and how they are to be incorporated into prioritization schemes are still a subject of considerable debate.
Product Risk Management: Testing and Warranties
Authors: Chan Wai
Published: September 2014

Manufacturers employ various testing strategies to identify and rectify defects before products are released to the markets. A carefully planned test strategy can improve product quality and customer satisfaction and reduce life cycle cost. Testing can also help reveal potential hazards associated with a product, evaluate associated risks, and discover factors that can be controlled to reduce risks. This article presents some of the tests that are performed during the prototype and production phases. These include the design verification test (DVT) and the highly accelerated life test (HALT) during the prototype phase; and the reliability qualification test (RQT) and the ongoing reliability test (ORT) in the production phase. We also describe how the results of a warranty performance analysis can be used as feedback to improve the test coverage for the tests run during production.
Optimal Stopping and Dynamic Programming
Authors: Bingham Nick H. , Peskir Goran
Published: September 2014

We present a brief review of optimal stopping and dynamic programming using minimal technical tools and focusing on the essentials.
Axiomatic Models of Perceived Risk
Authors: Jia Jianmin , Dyer James S. , Butler John C.
Published: September 2014

This article presents two classes of measures for perceived risk based on decomposition of a lottery into its mean and its dispersion about the mean, or standard risk. One of the classes of risk measures presumes that there is no risk when there is no uncertainty involved, and the other allows different degenerate lotteries to be evaluated with different values of “risk.” The former has more prescriptive appeal in risky decision making, but the latter may have more descriptive power for subjective risk judgments. These risk measures can also incorporate the asymmetric effects of losses and gains on perceived risk when an appropriate form of the standard measure of risk is applied. These perceived‐risk models unify a large body of empirical evidence regarding risk judgments, and provide sufficient flexibility to capture better perceptions of risk than previously developed risk models. Further, they provide clear ways to accommodate financial measures of risk and psychological measures of risk, and they can be incorporated into preference models based on mean‐risk trade‐offs.
Axiomatic Measures of Risk and Risk‐Value Models
Authors: Jia Jianmin , Dyer James S. , Butler John C.
Published: September 2014

This article provides a review of risk and risk‐value models that lead to decision making by explicitly trading off between risk and value. We begin with a discussion of a preference‐dependent measure of risk that is compatible with traditional expected utility theory, and therefore lends itself to an examination of how individuals perceive risky gambles as well as how they choose among them. Further, this risk measure can be the basis for generalizing models of decision making based on the intuitively appealing notion of trading off between risk and value. Based on the two‐attribute expected utility axioms, this risk‐value framework has much more flexibility in modeling preferences than the traditional single‐attribute expected utility theory. Specifically, we describe three useful classes of decision models: moments risk‐value models, exponential risk‐value models, and generalized disappointment models. These decision models unify two streams of research: one in developing preference models and the other in modeling risk judgments. They can also provide prescriptive guidance for those people who are willing to deviate from the traditional expected utility preference models and make their decisions based on risk‐value trade‐offs, as in financial decision making.
Understanding Large‐Scale Structure in Massive Data Sets
Authors: Braverman Amy J.
Published: September 2014

This article discusses massive data sets and some statistical methods for analyzing them. This is not a comprehensive review of all aspects of massive data set analysis, but rather a review and discussion of the issues in the context of statistics. We start with what a “massive” data set is, and then review several analysis methods that have been proposed in the statistics literature. These have certain common threads, most notably their relationships to the concept of data reduction. Then we examine how they can be applied with the specific aim of understanding local structure on a global scale, thus exploiting one important characteristic of massive data sets. We conclude with a brief discussion of the challenges and potential benefits for risk analysis, focusing on an example from climate studies.
Repeated Measures Analyses
Authors: Liu Mengling , Shao Yongzhao
Published: September 2014

Repeated measures data arise when multiple measurements of response variables and/or covariates are obtained in sequence from each experimental unit and occur frequently in many disciplines. The repeated measures design provides opportunities for investigators to examine the pattern of response variable under different measurement occasions, to understand the relationship between repeated outcomes and covariates, and to discern the dependence pattern among the repeated measurements. A special feature of repeated measures data is that measurements from the same unit are usually positively correlated; thus, valid and effective analysis of repeated measures must deal with this within‐subject correlation. In this article, we review some of the analysis methods for repeated measures, which include univariate summary methods, multivariate analysis of variance, parametric random‐effects methods, marginal generalized estimation equation methods, and modern semiparametric and nonparametric approaches. Some simple methods for data exploration and graphical display are demonstrated using real data examples. We also briefly discuss related computational tools and statistical packages.
Change Point Analysis
Authors: West Ronnie Webster
Published: September 2014

This entry provides a brief introduction to the statistical methodology called
change point analysis
, which is oft used in risk assessment. The goal of change point analysis is to model changes in the characteristics of a response variable over a range of a predictor variable. The discussion below is framed by two simple examples from the risk assessment literature where the response characteristic of interest is the mean response. The examples illustrate two simple change point methods for detecting the location of the change point in terms of the predictor variable and the nature of the change in the mean response before and after the change point. More complicated change point methods are also discussed briefly.
Intent‐to‐Treat Principle
Authors: Goldberg Judith D. , Belitskaya‐Levy Ilana
Published: September 2014

This article provides an overview of the concepts involved in intent‐to‐treat analyses of randomized controlled clinical trials. Implications for the interpretation of the results of randomized trials that arise when noncompliance, missing data, and losses to follow‐up occur are described. Alternative approaches to analysis are described.
Compliance with Treatment Allocation
Authors: Dunn Graham
Published: September 2014

Estimation of the effects of treatments in a randomized clinical trial is usually based on the intention‐to‐treat (ITT) principle. This procedure leads to an estimate of the average causal effect of treatment allocation based on everyone in the trial. When there are departures from randomized allocation (noncompliance), the ITT effect does not measure the impact of the receipt of treatment. The ITT estimate is an attenuated estimate of the effect of the receipt of treatment. We obtain a valid estimate of the latter by estimating the average effect of random allocation (i.e., ITT effect) on outcomes in the subgroup of participants who receive treatment if and only if they are randomized to its receipt (i.e., the compliers)—this is known as thecomplier‐average causal effect(CACE) of treatment. Noncompliance is often associated with loss to follow‐up. We illustrate simple methods to calculate CACE estimates both with and without the complication of missing outcme data.
Comparative Efficacy Trials (Phase
III
Studies)
Authors: Goldberg Judith D. , Shao Yongzhao
Published: September 2014

A comparative efficacy trial (a phase III study) is a clinical trial that is designed to compare the efficacy of an experimental treatment with that of a control treatment (active or a placebo). This article provides a brief introduction to the design and analysis of phase III trials. The issues discussed include randomization and blinding, selection of populations for study, selection of efficacy endpoints, the risk of type I and II errors, the determination of sample size, multicenter trials, interim analysis, and the assessment and comparison of treatment effects between/among different treatment groups.
Inferiority and Superiority Trials
Authors: Mukhi Vandana , Shao Yongzhao
Published: September 2014

Superiority clinical trials are often conducted to establish superior treatment effect of an experimental treatment compared to the control treatment. Noninferiority clinical trials are frequently designed to establish that the treatment effect of an experimental treatment is not much worse than that of an active control treatment, while the experimental treatment may offer other advantages such as reduced toxicity and/or cost. This article provides a brief introduction to the design and analysis of superiority and noninferiority clinical trials. The main issues discussed include, randomization and blinding during treatment allocation, prespecification of the noninferiority margin, the requirement of effective active control in noninferiority trials, risk of type I and type II errors, sample size and power analysis, and assessment and comparison of treatment effects between different treatment groups.
Toxicity (Adverse Events)
Authors: Levinson Benjamin , Goldberg Judith D.
Published: September 2014

In the setting of a clinical trial, an adverse event (AE) is any unfavorable change in the structure, function, or chemistry in a patient or subject that is temporally associated with the participation in a clinical trial, regardless of whether or not this change is considered related to the therapeutic intervention. An AE, which is treatment emergent, is classified by its relationship to the therapeutic intervention. In a randomized controlled trial, only increases in AEs in the intervention group compared with the control group are attributable to the intervention. A serious adverse event (SAE) is an AE that is at least possibly related to the therapeutic intervention that results in death, life‐threatening experience, inpatient hospitalization or prolongation of existing hospitalization, persistent and significant disability or incapacity, jeopardy to patient and requires intervention, or a congenital anomaly or birth defect. An unexpected AE is as any unanticipated AE that is possibly related to the therapeutic intervention. AEs are graded and classified using standardized coding dictionaries into broad categories based on anatomy and/or pathophysiology, and by specific terms within each category. In clinical trials, we perform routine laboratory tests prior to the start of the study, to screen patients for eligibility into the trials, and during the trials to ensure patient safety. AEs and SAEs are monitored during a study and are summarized to provide assessments of the safety of the treatment or intervention.
Safety
Authors: Goldberg Judith D. , Levinson Benjamin
Published: September 2014

Patient safety is of primary importance; safety must be ensured throughout the clinical trials that are conducted during the process of developing a new drug (intervention) and maintained after the drug is approved for use. Once a drug is approved for marketing, the safety data that are available provide patients and physicians with the ability to assess safety risks relative to efficacy benefits. Approaches for safety monitoring, during clinical trials, at the patient level, at the clinical trial level, and across clinical trials are described. Issues specific to the assessment of patient safety after marketing approval, particularly, active and passive postmarketing surveillance, are discussed.
Cancer Risk Evaluation from Animal Studies
Authors: Melnick Ronald L. , Kissling Grace E.
Published: September 2014

Data from properly designed and evaluated studies in experimental animals have been and continue to be reliable sources of information for the identification of potential human health hazards and the estimation of risks in exposed human populations. Properly designed studies must include (a) animal models that are sensitive to the endpoints under investigation, (b) adequately challenging doses, (c) adequate durations of exposure (at least 2 years for carcinogenicity studies in rats and mice), (d) sufficient numbers of animals per dose group to have adequate statistical power to detect a true effect, (e) multiple dose groups to allow characterization of dose–response relationships, (f) complete and peer‐reviewed histopathological diagnoses, and (g) pair‐wise comparisons with the control group and analyses of trends based on survival‐adjusted tumor rates. Mechanistic information and pharmacokinetic models that have been adequately tested may have an impact on the characterization of dose–response relationships and the estimation of cancer risk at human exposure levels. Setting occupational or environmental exposure standards based on animal carcinogenicity data provides a prudent public health approach to reduce the burden of cancer in exposed populations. More work is necessary to understand the basis for interindividual differences in susceptibility and to properly establish health‐based exposure standards that protect the most vulnerable segments of the general population.
Bioequivalence
Authors: Endrenyi Laszlo
Published: September 2014

Bioequivalence is defined as the comparable bioavailability of alternative drug formulations. Kinetic measures of bioavailability are reviewed, leading to a discussion of the statistical assessment of bioequivalence. Parametric and nonparametric methods of testing are described. Current research areas, including the estimation of individual bioequivalence and alternative measures for the evaluation of bioequivalence, are reviewed.
Disease Mapping
Authors: Lawson Andrew B.
Published: September 2014

This entry describes the range of subjects covered by the term
disease mapping
. The analysis of georeferenced health data has become very important. The areas of ecological analysis, clustering, health hazard assessment, and surveillance are all covered. The focus of relative risk modeling is also addressed.
Dose–Response Analysis
Authors: Edler Lutz
Published: September 2014

Dose‐response analysis is a substantial part of risk assessment aiming both at estimation of risk at a particular dose and at estimation of a minimum dose for a particular risk. The proof of the existence of a dose‐response relationship is a basic element of the proof of causality in experimental sciences. Dose‐response analysis requires the establishment of a dose‐response model and the availability of dose‐response data, both addressed in this chapter. Dose‐response data describe the extent of exposure of the individual subject by amounts, or concentrations of the agent. Dose‐response models are mathematical formula relating doseDto effectY(D) and riskR(D), which itself is a function of the effect. The most common models are provided in explicit form. Finally, the procedure of a dose‐response analysis is described in generic terms and the methods of statistical inference are addressed.
Threshold Models in Medicine
Authors: Kopp‐Schneider Annette
Published: September 2014

Dose–response models link the dose of an agent to the effect observed in the organism it is administered to. They constitute the central ingredient for risk assessment and are used for estimation of dose corresponding to a specified risk of an adverse effect. Threshold models assume that doses below a threshold,
d
*, lead to the same response as background. Alternatively, a less stringent definition of threshold model requires that the first derivative of response with respect to dose, i.e., the rate of change in response is zero at dose 0. If threshold doses exist and can be estimated, safe exposure levels can be derived, leading to tolerable small exposures. Hence, it is essential for quantitative risk assessment for a specific agent to resolve the question as to whether it exhibits threshold behavior.

Apparent thresholds may be observed in the statistical evaluation if organisms receiving a positive dose show no statistically significant difference in response compared to background. Theoretical arguments have been collected to support the existence of thresholds. Possible threshold mechanisms include metabolic inactivation of the compound, DNA repair, cell cycle arrest, apoptosis of damaged cells, and control of the immune system. The discussion about the existence of thresholds has been closely related to the discussed distinction between genotoxic and nongenotoxic carcinogenic compounds.
Cross‐Species Extrapolation
Authors: Benignus Vernon A. , Boyes William K. , Bushnell Philip J.
Published: September 2014

The practice of cross‐species extrapolation (CSE) in toxicology is a special case of comparative physiology in which experimental data collected in nonhumans are used to estimate or predict effects of toxicants in humans. Conceptually, data from nonhumans are adjusted to account for physiological differences between species and applied to humans. The components of the process are as follows: (a) adjust for differences in internal doses that result from differences in physiology due to body size and pharmacokinetic differences, (b) account for differences in the sensitivity of species due to physiological processes, and (c) make the adjustments in numerical terms with confidence limits. Steps (a) and (b) are usually done by applications of mathematical/mechanistic physiological models to the extent possible. Step (c) is sometimes done using mechanistic models and sometimes empirically. The state of practice and limitations are discussed. The practice is in early stages of development.
Syndromic Surveillance
Authors: Frickerk Ronald D.
Published: September 2014

Syndromic surveillance is the regular collection, analysis, and interpretation of real‐time and near‐real‐time indicators of diseases and other outbreaks by public health organizations. Motivated by the threat of bioterrorism, syndromic surveillance systems are being developed and implemented around the world. In a 2004 systematic review of publicly available information, 115 surveillance systems were identified, of which 29 were found that were designed specifically for detecting bioterrorism. In spite of their development, it is unknown how effective these systems will be at quickly detecting a bioterrorism attack. However, under the rubric of electronic biosurveillance, the goal of some of these systems has recently been expanded to include both early event detection and situational awareness, so that the focus is not simply on detection, but also on response and consequence management. Regardless of their utility for detecting bioterrorism, there seems to be a consensus that these biosurveillance systems are likely to be useful for detecting and responding to natural disease outbreaks such as seasonal and pandemic flu, and thus they have the potential to significantly advance and modernize the practice of public health surveillance.
Microarray Analysis
Authors: Kafadar Karen , Bjork Kathe E.
Published: September 2014

Microarray‐based experiments querying tens of thousands of genes, peptides, and other cellular components of varying organisms have evolved over the past several years to become a cornerstone screening method for numerous diseases and conditions, especially those with known genetic etiologies. They are also rapidly becoming a standard tool among researchers investigating reactions within and between hosts and agents relative to shifting environments and environmental conditions, and for quantitatively assessing risk to the health of humans, animals, and plants. The most commonly used microarray technologies for investigating gene expression are described herein, as well as examples of current and future research areas in public health.
Cohort Studies
Authors: Pearce Neil , Checkoway Harvey
Published: September 2014

Cohort studies provide the most comprehensive approach for evaluating overall patterns of health and disease. Every epidemiologic study should be based on a particular population's experience over time. The cohort design has the advantage, relative to other study designs, that ideally it includes all of the relevant person‐time experience of the population under study, whereas other study designs, such as case–control and cross‐sectional studies, involve sampling from that experience. However, cohort studies, particularly prospective studies, may be relatively expensive in terms of time and resources, and typically involve the efforts of epidemiologists, environmental scientists, occupational hygienists, biostatisticians, computer programmers, and clerical staff. The specific features of cohort studies that we present in this chapter include cohort enumeration, follow‐up procedures, the choice of comparison populations, person‐time analysis, stratified analysis, subcohort analyses, analyses of cumulative exposure, and latency and induction time.
Occupational Cohort Studies
Authors: Rockette Howard E.
Published: September 2014

Retrospective occupational mortality studies have been one of the most effective mechanisms for investigating the relationship of exposure to chronic disease. Occupational cohorts are well suited to investigate such exposure‐disease relationships because exposure is usually better characterized in the workplace than in most other settings. In this chapter, we address issues related to the design, conduct, analysis, and interpretation of retrospective occupational mortality studies. We summarize aspects of cohort definition and verification, determination of vital status, coding of death certificates, and incorporation of exposure measurements. Selected mortality summary indices are reviewed, followed by a discussion of regression models appropriate for the analysis. Finally, we briefly describe some of the designs that combine the advantages of case–control and cohort studies.
Causality/Causation
Authors: Phillips Carl V. , Goodman Karen J.
Published: September 2014

Causation is a concept that is universally intuitive, but it is difficult to define and even more difficult to create clear guidelines for inferring it from data. Although much of science is devoted to inferring causation, it is generally accepted that causation cannot be directly observed because doing so would require observing mutually contradictory states of the world. In epidemiology and other social sciences, causal inference can be particularly difficult, and there is widespread misunderstanding of how to interpret evidence for causation. Several conceptualizations and graphical models, including causal response types, causal pie models, and causal pathway diagrams, have been developed to aid in this process. These models can be used to better understand quantitative effect measures and the concepts of confounding and probability. Clearly defining and modeling causation leads to a recognition of some myths about causal inference (e.g., that randomized trials are a “gold standard” or that cause‐effect relations can be identified using “causal criteria”), and reveals how research can be designed to be most useful in inferring causation.
Meta‐Analysis in Nonclinical Risk Assessment
Authors: Jones David R. , Sutton Alex , Peters Jaime
Published: September 2014

Systematic review and meta‐analysis methods seek to combine all relevant evidence in transparent, reproducible, updatable, and quantifiable ways. They are well established in evidence‐based medicine and some other fields but remain relatively uncommon in risk assessment. Systematic review and meta‐analysis methods should be adopted both for assessing evidence from human epidemiological studies alone or from animal studies alone and in combining animal and human data in risk assessment processes.
Association Analysis
Authors: Neale Benjamin M. , Medland Sarah E. , Neale Michael C.
Published: September 2014

Association analysis aims to determine whether a disorder of interest correlates with genetic variation in the population. Such a correlation indicates that the surveyed genetic variation either predisposes to disease or acts as a proxy for the causal variant. This article provides a brief introduction to the genetic and statistical concepts involved in association analysis. The two primary research designs for association mapping of disease loci are case–control and family‐based studies. The article concludes with recent developments in association, such as genome‐wide association, that hold great promise for mapping of genetic variation related to common disease.
Nonparametric Calibration of Derivatives Models
Authors: Wu Lixin
Published: September 2014

Model calibration is a procedure used to incorporate observed information in a derivatives market into a derivatives pricing model. We explain the motivations for model calibration, and introduce representative calibration technologies for equity and interest‐rate derivatives markets.
Integration of Risk Types
Authors: Overbeck Ludger
Published: September 2014

To obtain an accurate estimation of the total risk, it is necessary that the different risk categories are integrated in a consistent way. Usually, the three most important risks are market (
see
Risk Measures and Economic Capital for (Re)insurers; Default Correlation; Statistical Arbitrage), credit (
see
Options and Guarantees in Life Insurance; Mathematical Models of Credit Risk), and operational risk (
see
Solvency; Simulation in Risk Management; Extreme Value Theory in Finance; Recurrent Event Data). Since all risk measures are functions of a random variable describing the potential loss, or more accurately the potential change in value of positions and transactions the financial institution holds, the integration should take place at the level of random variables. We assume that the same fixed horizon, usually 1 year, is used in the risk measurement process for all risk types. On the basis of the conceptual similarity in the model approach for risk types, they can be integrated at the level of their risk factors. At this level, integration means that the joint distribution of all risk factors should be specified. The integrated risk can then be calculated either on single transaction level, which is very challenging, or on the loss distribution in each risk type. The latter calculation can easily be achieved if the loss distribution in each risk type is a function of the underlying factors. If the financial institutions cannot identify the risk factors or their multivariate distribution, an even more robust method for integration that is based on the copula (
see
Copulas and Other Measures of Dependency) approach is applied to the loss distribution for each risk type.
Risk‐Neutral Pricing: Importance and Relevance
Authors: Basak Gopal K. , Mukherjee Diganta
Published: September 2014

This paper discusses the concept of
risk neutrality
in a general setting. Starting from the discrete‐time binomial model, it outlines the transition to a continuous‐time stochastic differential equation that epitomizes the continuous‐time asset pricing theories. Under
no‐arbitrage principle
and using Itô's lemma it gives a risk‐neutral derivation of the famous Black–Scholes–Merton equation. It then introduces the
equivalent martingale measure
that results from
risk neutrality
and finally, briefly discusses the
stochastic volatility
models.
Simulation in Risk Management
Authors: Mcleish Don L. , Metzler Adam
Published: September 2014

Risk refers to the possibility and the fear of things going wrong (i.e., some combination of events that have negative impact) and the magnitude of the losses resulting from these events. The concept of risk varies depending on the perception of different individuals and in some cases the “perceived” or “risk‐neutral” probabilities of events are more important than the real‐world probabilities, because they drive publicly traded asset prices in the immediate term. The Basel committee provides a framework for regulating minimum capital requirements for banks to cover losses incurred under five different types of risk: credit risk, market risk, operational risk, liquidity risk, and legal risk, and many of these categories carry over to different types of industry. Complex structures or organizations are exposed to many different risk factors or types of risk. The probability of one or more risk events is often very small and difficult to assess for lack of historical experience. There is a relationship among risk factors that may increase the probability of them occurring in combination. Because of the complexity, simulation methodology is quickly becoming the method of choice for evaluating and providing safeguards against the potential losses resulting from risk exposure. In this article, we discuss the use of Monte Carlo simulation as a cost‐effective method to quantify the financial risks of a corporation.
Risk in Credit Granting and Lending Decisions: Credit Scoring
Authors: Soopramanien Didier G.R. , Finlay Steve
Published: September 2014

The discipline of credit scoring is concerned with the development and application of predictive models of the likelihood that credit obligations will be met. The likelihood that credit obligations will be met represents the risk attached to granting credit. In this paper, the authors present an overview of the context of credit scoring and how it fits in the wider research area of risk analysis and prediction. The interested reader who wishes to pursue further research in this area will develop a pedagogical appreciation of this growing area of research in predictive risk analysis.
Credit Value at Risk
Authors: Wong Samuel Po‐Shing
Published: September 2014

Financial crises cause losses in many portfolios. The measures of value at risk quantify the severity of the consequential negative impacts and are therefore used as the capital buffers prepared for financial risks. Basel II Accord classifies financial risks into the following three categories:

Market risk: the loss caused by price fluctuations.

Credit risk: the loss triggered by default events.

Operational risk: the loss resulted from factors other than those in 1 and 2.

The credit value at risk plays a pivotal role in the reserve capital assignment because credit risk is the most contributing component among the three factors listed. In this survey, we describe the definition and computation of the credit value at risk and give some guidelines for stress testing. Some implementation details of credit value at risk are also presented.
Mathematical Models of Credit Risk
Authors: Bielecki Tomasz R. , Jeanblanc Monique
Published: September 2014

This article presents some elementary notions underlying the mathematical modeling of credit risk. We focus on the reduced approach, providing a conceptual outline of it, which is illustrated by some key formulae.
Operational Risk Development
Authors: Davies Brandon J.
Published: September 2014

This paper looks at the application of operational risk ‘models’ under Pillar 1 of the Basel II regulatory framework. It looks both at the conceptual problems underlying the definition of operational risk and the practical issues of data collection associated with modeling operational risk events. It also looks at operational value at risk (Op. VaR) as a measure of operational risk and the issues associated with implementing such a measure; it contrasts Op. VaR with an expected shortfall approach as used in the insurance industry. When managing operational risk, it is proposed that a process control approach operational risk management has many advantages especially when implemented through conformance to ISO and similar standards.
Use of Decision Support Techniques for Information System Risk Management
Authors: Buckshaw Donald L.
Published: September 2014

Identifying and understanding security risks for information system design or evaluation is very challenging. Making decisions about accepting or mitigating these risks through a rational, traceable, and understandable process is even harder. Quantitative risk analysis techniques can help stakeholders understand and communicate risk‐informed design decisions even when the stakeholders have conflicting objectives. Quantitative risk analysis also helps stakeholders understand security, cost, and functionality trade‐off decisions in repeatable process. This paper discusses the roles of stakeholders in risk analysis, the advantages and disadvantages of quantitative risk analysis, basic cyber risk decision support concepts and processes, and the mathematical definitions of risk for information systems. This paper recommends the use of properly constructed quantitative risk analysis models and discourages the use of semiquantitative risk analysis models.
Game Theoretic Methods
Authors: Bier Vicki M.
Published: September 2014

Protecting homeland security against intentional attacks on homeland security is fundamentally different from protecting against accidents or acts of nature. In particular, an intelligent and adaptable adversary may adopt a different offensive strategy in response to our protective security measures, in order to circumvent or disable them. Game theory provides a way of taking this into account analytically. Thus, security can benefit from game‐theoretic methods. This article discusses approaches for applying game theory to the problem of defending systems against attack. The results yield insights into the nature of optimal defensive investments.
Privacy Protection in an Era of Data Mining and Record Keeping
Authors: Karr Alan F.
Published: September 2014

The computerization of the world is changing the nature and degree of privacy protection, and even the very definition of privacy. The topic is rife with compromises, inconsistencies and paradoxes. Concern about privacy engenders “classical” trade‐offs between individual risk and social benefit, which are understood in only some contexts. Corporations and educational institutions hold immense amounts of information about individuals, most of which was provided willingly, but is subject to misuse. People fear identity theft, but employ their usernames as computer passwords. This article lays out a way to think about the issues, and summarizes history, current concerns, and possible futures.