bluuebunny/crossref_metadata_2025_split huggingface dataset:
Group Randomized Trials: Introduction
Authors: Murray David M. 
Published: September 2014

AbstractGroup‐randomized trials (GRTs) are comparative studies in which investigators randomize identifiable groups to conditions and observe members of those groups to assess the effects of an intervention. They face special problems of design and analysis that must be considered to ensure adequate power and valid results. The most critical problem is that observations taken on members of identifiable groups tend to be positively correlated, thereby violating the independence of errors assumption underlying the common analytic methods. Mixed‐model regression methods, generalized estimating equations, and randomization‐based methods can provide solutions to this problem if used properly and under the right circumstances. This article provides a brief review of these issues.
Artificial Intelligence
Authors: Hand David J. 
Published: September 2014

Abstract
Artificial intelligence (AI) has been defined as the study of artificial systems that exhibit intelligent behavior. Research in AI is aimed either towards yielding greater understanding of how naturally intelligent systems, like human brains, function (i.e. a subdomain of cognitive psychology) or at building systems like computer programs or robots, say, that behave in apparently intelligent ways (i.e. a subdomain of engineering). In this overview, some important facets of AI research are examined.
Confidentiality and Computers
Authors: Maindonald John H. , Stott Helen P. 
Published: September 2014

AbstractComputer systems that hold data of a private or confidential nature should be designed so that data are “disclosed only to authorized persons, entities and processes at authorized times and in the authorized manner” 24. Rights of privacy and confidentiality may be enshrined in legislation. Such rights are typically not absolute; they may, especially where data is required for research purposes, be weighed against a wider public interest. Demands for privacy and confidentiality impose responsibilities on designers and managers of computer systems that store sensitive data. Advances in computer technology, and especially the use of the internet, have created huge security challenges.
Numerical Analysis
Authors: Smyth Gordon K. 
Published: September 2014

Abstract
Numerical analysis is concerned with the accurate and efficient evaluation of mathematical expressions, especially on computers with floating point arithmetic. This article discusses the fundamental ideas of numerical accuracy including rounding error, approximation error, conditioning, and stability. The problem of computing a sample variance is considered as a simple illustrative example. The article goes on to describe briefly a number of key topics in numerical analysis, which are treated in more depth in separate articles, including linear equations and matrix computations, optimization and nonlinear equations, interpolation and approximation, and numerical integration. Brief pointers are given to available software.
Software Reliability
Authors: Dalal S. R. , Lyu M. R. , Mallows C. L. 
Published: September 2014

AbstractThe demand for complex software systems has increased more rapidly than the ability to design, implement, test, and maintain them, and the reliability of software systems has become a major concern for our modern society. In the last decade of the twentieth century, many reported system outages or machine crashes were traced down to computer software failures. Consequently, recent literature is replete with horror stories regarding software problems. This entry focuses on software reliability models and measurements.
Software for Sample Survey Data
Authors: Carlson Barbara Lepidus 
Published: September 2014

AbstractIn sample surveys, certain methods are used to improve the precision and control the costs of survey data collection. Among these methods are stratification, multistage sampling, clustering, and unequal probabilities of selection. These methods introduce a complexity to the analysis, which must be accounted for in order to produce unbiased estimates and their associated levels of precision. Not accounting for a complex sample design in the variance estimates can result in artificially small standard errors. This article provides a brief introduction to the impact these design complexities have on the sampling variance, and summarizes the characteristics and availability of software to carry out analysis on sample survey data while accounting for the design.
Bacterial Growth, Division, and Mutation
Authors: Gusev Y. , Armitage Peter 
Published: September 2014

AbstractModels of cell growth and division in bacteria were considered more than 60 years ago. The evolution of models of bacterial growth was closely followed by the development of models of the cell cycle in budding and fission yeast, cell cultures, and mammalian cells. In some of these theoretical models, new mechanisms have been hypothesized, which later have been confirmed in experiments on spontaneous and induced mutations, signal transduction pathways, cell cycle regulation, and programmed cell death. Thus, for many years, modeling of the bacterial cell cycle has been a “proving ground” for refinement of theoretical models in cell biology. The major questions addressed in bacterial growth models have included: stochastic and deterministic models of cell population dynamics; different models of the cell cycle in individual cells, such as growth control, random transition, and mitotic clocks; generational dependence models; unequal cell‐division models; spontaneous mutation models and fluctuation tests.
Michaelis–Menten Equation
Authors: Dunn Graham 
Published: September 2014

Abstract
This article describes various approaches to the analysis of quantitative responses assumed to follow a hyperbolic dose–response relationship (the Michaelis–Menten equation) characterized by the values of two parameters: the Michaelis constant (the dose at half‐maximal response) and the asymptotic maximum response. Methods include diagnostic graphical representations, including the double reciprocal or Lineweaver–Burk plot, the Hanes plot and the Eadie–Hofstee plot. Curve‐fitting and parameter estimation is described using either least squares or maximum likelihood. The latter includes using a generalized linear model incorporating a reciprocal link function and the possibility of nonnormally distributed errors. Robust and distribution‐free estimation procedures, such Theil's method (known as the direct linear plot in biochemistry) are also covered.
Bias in Observational Studies
Authors: Hill Holly A. , Kleinbaum David G. 
Published: September 2014

AbstractWe consider three sources of bias affecting observational studies and ways to address them: selection bias, information bias, and confounding. We discuss these sources of bias with respect to cohort and case–control designs. Even if a study is internally valid and not subject to these sources of bias, a judgment must be made whether the results generalize to some other target population.
Case–Control Studies: Details
Authors: Wacholder Sholom , Hartge Patricia 
Published: September 2014

Abstract
We review design options for case–control studies and considerations for determining whether a case–control or cohort study is more appropriate for a given purpose. One issue is exposure data quality, which may be poorer in a case–control study than in a cohort study, and subject to differential error (recall bias). This must be weighed against the cost efficiencies and other advantages of the case–control design. We describe case and control selection, including the use of hospital‐based controls (secondary base studies) in which controls are not sampled from the population base that gave rise to cases. Other design options, including matching and the use of two‐phase studies are described, as well as approaches to analysis. It is pointed out that not only odds ratios, but also absolute risks, are estimable from population‐based case–control studies.
Cohort Study
Authors: Prentice Ross L. 
Published: September 2014

AbstractA cohort study is based on a sample of individuals who are followed to determine disease events over a defined follow‐up period. Disease rates can be calculated from such a study as well as relative risks associated with measured exposures and other covariates. If the cohort identification precedes the follow‐up period, the study is prospective; otherwise, the cohort study is retrospective. Some advantages of the cohort design, compared to the case–control design, include the ability to study a variety of health outcomes and to obtain high quality exposure information before disease develops, thus avoiding recall bias. We review basic elements, including study design and sample size, cohort selection and follow‐up, covariate history ascertainment, study conduct, data analysis, effects of measurement error, and validation substudies. We discuss the cohort study in relation to case–control designs, nested case–control designs, and case–cohort studies, as well as the role of intervention studies versus observational cohort studies.
Latent Period
Authors: Tan Wai‐yuan , Chen Chao W. 
Published: September 2014

AbstractDifferent definitions of latency used in the biological and statistical literature have been reviewed and illustrated by examples. In particular, a distinction has been made between the biological latent period and the epidemiological latent period. This article also discusses the latency of infectious diseases, such as the incubation period of HIV and the time to tumors in cancers. Some stochastic models of the time period to cancer tumors and its applications have been discussed in some detail.
Confidentiality in Epidemiology
Authors: Leufkens Hubert G. 
Published: September 2014

AbstractThis entry describes the need for confidentiality in conducting clinical and epidemiologic research, particularly with respect to informational privacy of individuals. Ethical principles (including beneficence, nonmalfeasance, autonomy, and justice) are reviewed, and mention is made of the legal framework and guidelines to prevent the violation of confidentiality.
Natural History Study of Prognosis
Authors: Cnaan Avital 
Published: September 2014

AbstractPrognosis refers to the probable outcome of an attack of disease. The traditional definition would describe the evolution of disease in the absence of medical intervention, but nowadays we may also include the term “clinical course” to describe the natural history of a disease that has been affected by medical intervention. Various design considerations are reviewed for the study of natural history, including the use of cohort studies, the various biases that may affect such studies, and the definition of time zero as a clearly defined starting point. Other topics include the analysis of dichotomous outcomes, survival analysis and longitudinal data analysis. Combination of data from several studies, and situations involving multiple outcomes are also mentioned.
Screening Benefit, Evaluation of
Authors: Prorok Philip C. 
Published: September 2014

AbstractScreening is the testing of apparently healthy individuals from a population for the purpose of separating them into groups with high and low probabilities of having a given disease such as cancer. This article examines various issues encountered in assessing the benefit of screening. The use of observational studies and possible involvement of selection bias is included, together with a discussion of lead time and length bias. Appropriate study endpoints are discussed. Various possibilities for randomized control trials are reviewed, and the use of observational designs to compare cancer incidence and mortality in a population before and after the introduction of a screening program is mentioned. The entry also lists important information requirements for the evaluation of screening programs.
Epidemic Models, Inference
Authors: Britton Tom 
Published: September 2014

AbstractWe consider stochastic models for the spread of an infectious disease in a population. We first focus on the simplest situation where individuals behave similarly and where there are no social structures. We derive estimates for model parameters including the basic reproduction number, both when data consists of the final size only, and where the outbreak is observed over time. We then look at household models where there is increased transmission within households and discuss other extensions and methodology for such situations, including Markov chain Monte Carlo (MCMC). Copyright © 2015 John Wiley & Sons, Ltd.
Epidemic Thresholds
Authors: Ball Frank 
Published: September 2014

AbstractThis article gives an overview of the threshold behavior of epidemic models, focusing primarily on stochastic closed population models. A key parameter is the reproduction numberR0of an epidemic. For a broad range of models, branching process theory is used to show that an epidemic initiated by a trace of infection in a large population can take off only ifR0is larger than one. These models assume either a homogeneous population or a stratified population in which all the subpopulations are large. Models for the spread of an epidemic among a population partitioned into a large number of small households, or among a spatially configured population, are also considered. In both of these casesR0no longer provides a good indicator as to whether an epidemic can take off. The article closes with a brief discussion of the threshold and long‐term behaviors of open population models, which incorporate demographic effects.
Diagnosis Related Groups (DRGs): Measuring Hospital Case Mix
Authors: Wiley Miriam M. 
Published: September 2014

AbstractThe Diagnosis Related Group (DRG) system constitutes an approach to measuring hospital case mix that entails the separation of hospitalized patients into unique groups based on their diagnoses and procedures. Since originally developed in the United States in the 1980s, a number of versions of DRGs have developed to reflect the evolution in potential applications, in addition to developments in expertise, information technology, and data systems. Major initiatives in the development of DRG‐type systems have taken place in Australia with the production of AR‐DRGs and in a number of European countries. In addition to being applied extensively throughout North America, DRG‐based applications are now widespread internationally, particularly in Europe and Australia.
Health Care Financing
Authors: Lave Judith R. , Peele Pamela B. 
Published: September 2014

Abstract
The methods used to finance personal health care service play a major role in shaping a country's health care system. Personal health care services are those services such as hospital care, physician care, dental services, and drugs that are provided directly to individuals. The financing methods influence the terms under which people access the health care delivery system, the types of health care provided, and the mechanisms used to allocate health care services. They also influence how the costs of health care services are distributed over the population by income and by health status. Two aspects of health care financing are the focus of this article: the sources of funds for health care services and the mechanisms used to pay individuals and institutions who provide health care services.
Health Care Technology Assessment
Authors: Sheldon Trevor A. 
Published: September 2014

Abstract
Health technology assessment is a scientific exercise to provide evidence for health care decision makers who wish to ensure that the introduction of health technologies, such as drugs and devices, is managed in order to control costs and optimize the benefits. Health technology assessment (HTA) is the process of assessing the effectiveness, cost‐effectiveness and, more broadly, the medical, social, ethical, and economic implications of the use of a health technology in practice. Thus, it ranges from the very technical activities of investigating the reliability of a diagnostic test, through the evaluation of the cost‐effectiveness and safety of a drug all the way to policy analysis. The results are used to inform the decisions of health care policymakers, funders and providers of health care, and by the public.
Outcomes Research
Authors: Lipscomb Joseph 
Published: September 2014

AbstractOutcomes research is a multidisciplinary field of study to understand and improve the end results of healthcare. These end results may include survival, health‐related quality of life, satisfaction with care, economic impact, and other outcomes important to a range of decision makers: patients, individuals at risk to disease, providers, public and private payers, regulators, and standards‐setting organizations. Outcomes research is substantially focused on assessing and enhancing the quality of health care. This requires continuing efforts to develop and apply valid, reliable, feasible, and appropriately responsive outcome measures; to evaluate the impact of interventions on these outcomes; and to translate findings into information useful to decision makers.
Quality of Care
Authors: Normand Sharon‐Lise T. 
Published: September 2014

AbstractQuality of care refers to the attributes or characteristics of the delivery and subsequent outcomes of mental and physical health care. Data are collected to assess quality in order to improve care, to provide accountability, and to quantify information for market choices. Analyses of health data, however, are challenging due to their nonexperimental nature and complex structure. These characteristics lead to potential problems with confounding when identifying factors that impact quality, with nonindependence of outcomes associated with individuals treated within health care units, as well as with clustering due to multiple mixed outcomes.
Operations Research, Simulation
Authors: Sharkey Phoebe D. 
Published: September 2014

Abstract
Simulation models have been indispensable tools for decision makers in many different public and private environments. The methodology of developing a simulation model involves the abstraction of a real‐life system into a symbolic model format. To discuss the multiple stages required in a Monte Carlo simulation study, including the (1) Problem Statement; (2) Information Gathering and Data Preparation; (3) Model Conceptualization; (4) Model Implementation; (5) Analysis and Interpretation; (6) Verification and Validation; and (7) Implementation and Documentation. Simulation models are created in order to provide valuable insight to decision makers by replicating real‐life decision scenarios and providing more complete information about the risks and uncertainties in those environments. Once a model has been verified and validated as accurately representing the real‐world environment, many “what if ” questions about the system can be posed. Illustrative examples are presented.
Family‐based association for quantitative traits.
Authors: Horvath Steve , Laird Nan M. 
Published: September 2014

Abstract
Tests of allelic association are designed to demonstrate relationships between genetic and phenotypic information. The class of tests using transmission information from parents to offspring (TDT and its generalizations) are very popular with dichotomous phenotypes because of their lack of sensitivity to population heterogeneity. Here we discuss the generalization of this approach to quantitative phenotypes.
Linkage Analysis, Multipoint
Authors: Speed Terry P. 
Published: September 2014

Abstract
Multipoint linkage analysis is the analysis of linkage data involving three or more linked loci. Such analyses are carried out to order or map a set of loci, to position a new locus in relation to a mapped set of loci, or to exclude a locus from a region containing two or more loci. Multipoint linkage analysis can be viewed as an attempt to make more efficient use of recombination data in furthering the aims of linkage analysis. It is more complicated than two‐point linkage analysis in several ways: the order of loci needs to be specified; a joint distribution for all recombination patterns needs to be specified; and the calculations can become prohibitive rather quickly. Likelihood methods are used in multipoint linkage analysis; there are two principal approaches to calculating likelihoods in the literature: the Elston–Stewart algorithm, which works on a relatively few loci and many individuals in a pedigree, and the Lander–Green algorithm, which is effective for many loci and relatively few individuals in human pedigrees.
Paternity Testing
Authors: Baur Max P. 
Published: September 2014

Abstract
The use of genetic marker systems for the analysis of questionable paternity situations has been introduced in 1920 to prove in a deterministic way, situations of nonpaternity. Statistical evaluation of positive results (nonexclusion) was first performed by Essen–Möller in 1938. The general idea, which holds for any question of possible relationship, is the formulation of two alternative hypotheses regarding the postulated relationships of the persons involved. Subsequently, the likelihoods of the marker‐typing results conditional on these hypotheses are calculated using population frequencies estimated from large population samples and Mendelian proportions for transmission from parents to children. The complete genetic information is contained in the likelihood ratio of these two conditional likelihoods and serves as the basis for the decision between the two alternative hypotheses. Introduction of prior probabilities for the two hypotheses allows the calculation of Bayesian posterior probabilities using these conditional likelihoods.
Dose–Response Models in Risk Analysis
Authors: Gaylor David W. 
Published: September 2014

Abstract
Various biological insults, such as exposure to hazardous chemicals, pose a risk to human health. For various purposes, including attempting to set safe limits of exposure, it is useful to quantify the risk. Direct evidence from human exposure is seldom available and most substances are assessed through bioassays on animals. The form of the dose–response relation obtained from these studies is important, as extrapolation to doses lower than those used in the experiments is necessary to try to estimate risks at the doses encountered in human exposure. Various dose–response models are presented, including multistage and multimutation carcinogenesis models. Models for continuous data are also mentioned. Models for litters are discussed as these are important in reproduction and developmental studies.
Dynamic Allocation Index
Authors: Gittins J. 
Published: September 2014

Abstract
When a process comprises a succession of projects, with each chosen from a pool of competing projects, there may be advantages to undertaking the projects in one order rather than in others. An example of such a process would be testing different candidate compounds when searching for a new pharmaceutical product. Dynamic Allocation Indices are a way of attributing indices to projects so that at each stage, choosing the project with maximum index optimizes the whole process, with respect to some reward function.
Radioimmunoassay
Authors: Redmond Carol K. 
Published: September 2014

AbstractRadioimmunoassays (RIA) use radioactive tracers to model biochemical reactions in which specific antigens bind to antibodies in the immune system. The radiation counts of bound labeled to free‐ligand labeled antigen (or antibody) at various fixed dose levels of antigen are utilized to estimate indirectly the unknown concentration of unlabeled antigen (or antibody) in the system. Statistical methods, analogous to those employed in standard quantal bioassay designs, are available to estimate the parameters of the models and the absolute or relative potency of the concentration of antigen (antibody) in the system and its associated confidence intervals.