bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cohort Studies
Authors: Pearce Neil , Checkoway Harvey 
Published: September 2008

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.
Attributable Fraction and Probability of Causation
Authors: Greenland Sander 
Published: September 2008

Abstract
This article discusses the concepts and definitions associated with terms such as attributable fraction, attributable risk, and probability of causation. It is emphasized that there are several incompatible definitions of attributable risk. Some of these definitions apply to concepts of excess caseload and excess rate, which are estimable from epidemiologic data; others apply to concepts of etiologic fraction and probability of causation, which are not estimable without strong assumptions about biologic mechanisms of disease.
Estimation of Mortality Rates From Insurance Data
Authors: Melnick Edward L. , Tenenbein Aaron 
Published: September 2008

Abstract
The problem of using insurance intercompany data to determine mortality rates has had a long history of development. The development of these mortality rates involves two steps, namely, the estimation of the rates from the data and the interpolation and smoothing of these rates to conform to known trends in the mortality rates by age, and time since first insured. This paper will discuss the different methods used to determine these rates and discuss the advantages and disadvantages of each approach.
Occupational Cohort Studies
Authors: Rockette Howard E. 
Published: September 2008

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.
Randomized Controlled Trials
Authors: Goldberg Judith D. , Belitskaya‐Levy Ilana 
Published: September 2008

AbstractIn the medical sciences, the ideal experiment is the randomized controlled clinical trial (RCT) in which the subject or patient is randomized to treatment with an experimental agent or control (either placebo or other active treatment), in a double‐blind manner. The RCT is the “gold standard” in medical research to establish the efficacy of a new treatment or intervention. The benefits of such an experiment are that any observed differences between the two groups (or more groups) with respect to the outcome can be attributed to the treatment and are without bias. That is, confounding is controlled and selection bias is removed since neither the subject nor the investigator chooses the intervention.In this entry, we review the issues in the design, conduct, analysis, and interpretation of a randomized clinical trial.
Compliance with Treatment Allocation
Authors: Dunn Graham 
Published: September 2008

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.
Repeated Measures Analyses
Authors: Liu Mengling , Shao Yongzhao 
Published: September 2008

AbstractRepeated 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.
Clinical Dose–Response Assessment
Authors: Burman Carl‐Fredrik 
Published: September 2008

AbstractEssentially all pharmacologically active drugs give adverse effects at high enough doses. On the other hand, the intended effect is generally increasing with dose. A critical question is, therefore, which dose should be chosen, that is, which dose minimizes the total risk associated with both insufficient therapeutic effect and side effects. This article briefly describes the estimation and weighting of different risk components, as well as the design of dose‐finding trials.
Inferiority and Superiority Trials
Authors: Mukhi Vandana , Shao Yongzhao 
Published: September 2008

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.
Blinding
Authors: Belitskaya‐Levy Ilana 
Published: September 2008

Abstract
The objective of blinding is to contribute to the elimination of bias that may be introduced intentionally or unintentionally by the various individuals involved in a research study. Different types of bias (e.g., information bias, detection bias, etc.) can be prevented or reduced by blinding patients, health care providers, data collectors, outcome assessors, and/or data analysts. Even when patients and those administering treatment cannot be blinded (e.g., if the study involves a surgical procedure), blinding of outcome assessment can still be possible and helps reduce detection bias. Owing to ambiguity in blinding methodology (e.g., single‐blinded, double‐blinded, etc.), it is important to carefully report the blinding status of all individuals involved in a research study and the blinding mechanism. This helps the reader judge the validity of the study and ensures the credibility of its results. In order to ensure the success of blinding in preventing or reducing bias, it is important to assess its efficacy before the study begins. However, assessment of the efficacy of blinding at the end of the study might produce ambiguous results as failures in blinding are often confounded with treatment efficacy and adverse events.
Truncation
Authors: Rai Shesh N. , Sun Jianguo 
Published: September 2008

AbstractIn collection and analysis of real‐life data, we always encounter incomplete data. Censoring and truncation are types of incomplete data that arise owing to a type of sampling scheme. We describe the differences and similarities between censoring and truncation. We also describe types of truncation and the role of truncation in the analysis of real‐life data.
Competing Risks
Authors: Ray Rose M. 
Published: September 2008

AbstractCompeting‐risk models are used to examine the relationship between different risk factors to study different types of failures that may occur in the same population. What would happen to the remaining risks if one or more causes of failure could be eliminated is a question that may be addressed with these models under some conditions. Competing risks are generally evaluated by comparing the predicted time to failure or time to event for the risks being considered.In this article, the mathematical formulation of the problem is described and statistical methods for estimating competing risk are discussed. Applications of competing risk in the fields of engineering, medicine, economics, and finance are examined with specific examples.
Toxicity (Adverse Events)
Authors: Levinson Benjamin , Goldberg Judith D. 
Published: September 2008

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 2008

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.
History of Epidemiologic Studies
Authors: Samet Jonathan M. , Apelberg Benjamin J. 
Published: September 2008

AbstractEpidemiology can be defined as the scientific method used to assess health and its determinants in populations. Central to the method are measures of disease occurrence, and study designs for identifying causes of disease and quantifying their strength as causal agents. Because epidemiological studies directly address threats to human health, their findings, when available, have received emphasis in qualitative and quantitative risk assessments. Epidemiological data have the notable strength of being based on the “right” species, “real‐world” exposures, and often the full range of susceptibility. Observational data often have inherent limitations, but strategies are available to handle them. The development of radiation risk models from epidemiological studies and the use of the models in quantitative risk assessment have set a risk assessment paradigm grounded in epidemiological research.
Logistic Regression
Authors: Dash Chiranjeev , Kleinbaum David G. 
Published: September 2008

Abstract

Logistic regression is a popular statistical tool used to analyze epidemiologic data. It is typically used when the outcome or disease/event under study is dichotomous. Logistic regression involves postulating a model to explain the disease process (dependent variable) using exposure and control variables (independent variables), fitting the model to the data obtained from the study using maximum likelihood procedures, and finally, making appropriate statistical inferences from the results of the modeling approach. The measure of effect reported in a logistic regression analysis is known as the
odds ratio
(
OR
). This measure describes the strength of the relationship between the exposure of interest and the outcome. The
OR
obtained can be
adjusted
for other covariates, known as
confounding
or
control variables
, by including these variables in the logistic model. Statistical inference in logistic regression analysis involves hypothesis testing and confidence interval estimation. Wald test and the likelihood ratio test are commonly used methods for testing hypotheses in logistic regression. In this chapter, we provide a brief overview of logistic regression and illustrate the use of this popular procedure using data from an epidemiologic study as an example.
Adverse Selection
Authors: Kunreuther Howard 
Published: September 2008

Abstract

If an insurer sets a premium based on the average probability of a loss in an entire population, those at higher‐than‐average risk for a certain hazard will benefit most from coverage, and hence will be the most likely to purchase insurance for that hazard. In an extreme case, the poor risks will be the only purchasers of coverage, and the insurer can expect to lose money on each policy sold. This situation, referred to as
adverse selection
, occurs when the insurer cannot distinguish between members of good‐ and poor‐risk categories in setting premiums. This article discusses the implications of adverse selection for insurance.
Pharmaceuticals in the Environment
Authors: Schlenker Gerd 
Published: September 2008

Abstract
Up to 95% of pharmaceuticals used in human and veterinary medicine are excreted and land in the aquatic or terrestrial environment. Most of these substances are degraded with difficulty in the environment, resulting in biological effects on bacteria, plants, and animals. Antibiotics and the development of antibiotic‐resistant bacteria are a special problem. At present, environmental damage due to pharmaceuticals is not yet an acute problem for people and animals. Particularly in the European Union, special measures have been taken to reduce the risk of environmental damage due to drugs.
Syndromic Surveillance
Authors: Frickerk Ronald D. 
Published: September 2008

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.
Coding: Statistical Data Masking Techniques
Authors: Slavkovic Aleksandra B. 
Published: September 2008

Abstract
Coding broadly refers to altering the data in some way. In computer science literature, it may refer to encryption, compression, or error correction. These are relevant for cryptography and cryptanalysis, and for data privacy and homeland security. In statistical confidentiality literature, coding encompasses various data‐masking techniques. These techniques generally introduce bias and variance to data. The question is how much and what kind of bias and variance is introduced and how this influences the utility of data‐mining algorithms and precision of identification, that is the false positive error rate. This article gives a brief overview of statistical disclosure limitation techniques for data masking, pointing out the likelihood of their use and potential impacts for data privacy.
Digital Governance, Hotspot Detection, and Homeland Security
Authors: Patil Ganapathi P. , Acharya Raj , Phoha Shashi 
Published: September 2008

Abstract
Effort is in progress for spatial and spatiotemporal hotspot detection, early warning, and security. A hotspot can mean an unusual phenomenon, anomaly, aberration, outbreak, elevated cluster, critical area, or object‐recognition and tracking. The declared need may be for monitoring, etiology, early warning, or management. The responsible factors may be natural, accidental, or intentional.
This article attempts to address concepts, methods, tools, and a variety of case studies and applications of interest to agencies, academia, and the private sector involving societal issues for homeland security, such as public health, infectious disease, invasive species, crop pathogens, biosecurity, hospital networks and syndromic surveillance, drinking water network systems, crisis management, and others.
R&DPlanning and Risk Management
Authors: Cox Louis A. 
Published: September 2008

AbstractThis article discusses quantitative concepts and methods for making risk‐management decisions about R&D projects. Such decisions include what projects to fund, the order in which to attempt different activities within an R&D project, when to abandon projects that are not working out as hoped, and how to structure the incentives of R&D project managers and investors to take best advantage of the opportunities represented by risky projects.
NoFaultFound
Authors: Jones Jeffrey A. 
Published: September 2008

AbstractThis article describes the No Fault Found (NFF) phenomenon that occurs when a system is tested after a failure is reported but no failure is found during the test. This basic phenomenon is known by a large number of terms, which are often abbreviated to three letter acronyms, and are described in this article. The level of occurrence of such false positive faults in various technical fields is described and a number of possible reasons for the incidence are discussed. Finally a discussion of the management of such faults within the context of quantitative risk assessment is included.
Environmental Risk Assessment of Water Pollution
Authors: Al‐Khalidi Abdul Sattar 
Published: September 2008

AbstractEnvironmental risk due to water pollution is an ongoing every day problem. Municipal wastewater systems are one of the largest pollution sources to surface waters. Other major sources include residential and industrial discharges, and agricultural runoff, dumped in to rivers directly or by seepage from contaminated groundwater. Threats from chemicals may come from sources such as pesticide use and waste disposal. Air pollution and land pollution also affect water quality.Statistical methodologies and probabilistic models play major roles in assessing environmental water pollution risk, since risk is a “probabilistic measure for degree of harm associated with pollutant levels”. The analyses presented in this article are designed to assess the risk of surface water pollution from bacterial and chemical sources. The probabilistic approach in the analysis is designed to test the significance of pollution measuring parameters in the probability distribution model. This article gives illustrations of two statistical techniques that can assess the risk of surface water pollution from bacterial and chemical sources, as applied to a sample from Tigris river water after it had passed through the Baghdad metropolitan area. The first is an adaptation of existing methods to lognormal data, while the second is an application of intervention analysis with time series data.
Causal Diagrams
Authors: Greenland Sander , Pearl Judea 
Published: September 2008

AbstractFrom their inception, causal systems models (more commonly known asstructural‐equations models) have been accompanied by graphical representations or path diagrams that provide compact summaries of qualitative assumptions made by the models. These diagrams can be reinterpreted as probability models, enabling use of graph theory in probabilistic inference, and allowing easy deduction of independence conditions implied by the assumptions. They can also be used as a formal tool for causal inference, such as predicting the effects of external interventions. Given that the diagram is correct, one can see whether the causal effects of interest (target effects, or causal estimands) can be estimated from available data, or what additional observations are needed to validly estimate those effects. One can also see how to represent the effects as familiar standardized effect measures. The present article gives an overview of the following: (a) components of causal graph theory; (b) probability interpretations of graphical models; and (c) methodological implications of the causal and probability structures encoded in the graph, such as sources of bias and the data needed for their control.