bluuebunny/crossref_metadata_2025_split huggingface dataset:
Compliance with Treatment Allocation
Authors: Dunn Graham 
Published: September 2014

AbstractEstimation 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

AbstractData 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

Abstract
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.
Dose–Response Analysis
Authors: Edler Lutz 
Published: September 2014

AbstractDose‐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

Abstract

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

AbstractThe 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

AbstractSyndromic 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

Abstract
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

Abstract
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

AbstractRetrospective 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

Abstract
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

AbstractSystematic 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

Abstract
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.
Integration of Risk Types
Authors: Overbeck Ludger 
Published: September 2014

Abstract

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

Abstract

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

Abstract
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

Abstract
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

AbstractFinancial 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.