bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diagnostic Tests, Likelihood Ratio
Authors: Roberts Robin S. 
Published: September 2014

AbstractThis article concerns the use of diagnostic test information in medical diagnosis. Traditionally, the association between a diagnostic test and the true state of disease has been quantified in terms of sensitivity and specificity. The application of Bayes Theorem allows the formal incorporation of the diagnostic test information as a post‐test probability of disease. The positive and negative likelihood ratios (LRs) are alternative expressions of the informational properties of the diagnostic test, which are computed from sensitivity and specificity. LRs offer several advantages including (a) simplifying the calculation of post‐test probability (via odds), (b) providing a more direct indication of the diagnostic value of the test, and (c) avoiding the need to dichotomize a quantitative test. Given enough data, one could estimate a continuous LR curve and thus compute post‐test probability for any test value.
Pharmacoepidemiology, Adverse and Beneficial Effects
Authors: Guess H. A. 
Published: September 2014

AbstractPharmacoepidemiology has been defined as “the study of the distribution and determinants of drug‐related events in populations and the application of this study to efficacious drug treatment.” The termdrugis generally understood to include vaccines and the populations are understood to be human. Pharmacoepidemiology is an essential component of pharmaceuticaloutcomes researchand ofrisk management of pharmaceutical products. Many pharmacoepidemiologic studies are observational, and selection bias and confounding, includingconfounding by indication, are especially critical problems to be addressed in design, analysis, and interpretation. Information bias from misclassification of exposures, outcomes, and covariates is also of particular concern and may impair the ability to control confounding. Another problem is that the hazard function for drug‐related adverse and beneficial effects often varies with duration of therapy. The hazard function may be influenced not only by the biology but also by depletion of susceptibles.
Predictive Modeling of Prognosis
Authors: Marshall G. 
Published: September 2014

AbstractPrognosis is defined in terms of the relative probability of developing one of the several alternative outcomes in the natural history of a disease. This entry discusses predictive models for prognosis using a general Markov model in which the exact transition times are not observed. Data from a longitudinal study of young patients with diabetes are used to determine factors affecting the natural course of diabetic retinopathy. Other examples mentioned include hepatocellular carcinoma and HIV infection.
Prevalence of Disease, Estimation from Screening Data
Authors: Pickles Andrew , Dunn Graham 
Published: September 2014

AbstractSimple estimation of prevalence from sample survey data may be biased because of imperfections in the measurement instrument. An alternative approach is to use two‐phase sampling in which a subsample of screened individuals is investigated more intensively with a gold standard measure to reveal their true disease status. A variant design is to use a calibration or validation sample of individuals who are assessed using both the screen and gold standard methods. The analysis of two‐phase samples is described, including the use of simple conditional probabilities, inverse probability weights, and a more general framework in which the EM algorithm is used. Bayesian approaches are also mentioned briefly. Statistical software for the analysis of multiphase data is mentioned, together with an outline of certain design considerations.
Secondary Attack Rate
Authors: Halloran M. Elizabeth 
Published: September 2014

AbstractThe secondary attack rate is defined as the probability that infection occurs among susceptible persons within a reasonable incubation period following known contact with an infectious person or an infectious source. It is a key epidemiologic parameter in infectious diseases that are transmitted by contact. It can be estimated using a variety of epidemiologic study designs, models, and methods of estimation. Inference needs to take into account the correlation of susceptibles exposed to the same infectious source.
Epidemic Models, Spatial
Authors: Renshaw Eric 
Published: September 2014

AbstractFor a proper understanding of the dispersal and control of infectious diseases, it is often necessary to consider spatial models in which individuals do not mix homogeneously. A birth, death, and migration/infection process for separate colonies, with migration/infection to nearest neighboring colonies, is considered from both deterministic and stochastic viewpoint. Topics covered include Monte Carlo simulation and threshold levels, with particular reference to the transmission of HIV/AIDS and rabies.
Chain Binomial Model
Authors: M. Longini Ira 
Published: September 2014

Abstract
The chain binomial model arises from chains of binomial probability mass functions, where each function is conditioned on previous function. Products of these probability mass functions give the probabilities of particular sequences of binomial realizations. This chain structure allows for detailed analysis of physical phenomena that can be represented by chain binomials. The simple chain structure allows for statistical inference based on likelihood theory.

The chain binomial model forms the statistical underpinnings of the life table. In addition, the chain binomial model has been important in the analysis of infectious disease spread in small groups such as households. For example, the Reed–Frost model is a chain binomial model where the number of new infectives at some time
t
depends on the number of infectives and susceptibles at time
t
− 1. This model has been used to estimate epidemiological parameters for the spread of such diseases as measles, smallpox, and influenza.
Epidemic Models, Deterministic
Authors: P. Heesterbeek J. A. , Roberts M. G. 
Published: September 2014

AbstractIn this article, we briefly review some of the approaches to deterministic modeling of epidemic phenomena. We have chosen to structure the approaches by specific biological examples that exemplify a certain approach and that were, in addition, historically important in shaping the development of the deterministic theory. These examples are measles, malaria, helminth infections, and HIV. For types of models, we briefly discuss ODE models and PDE age‐structured models of compartmental SIR‐type (both for microparasitic agents), ODE models for helminth parasites, formulated in terms of average parasite load, and the more general microparasite models in terms of integro‐differential equations. In the latter, there is flexibility in defining the infectivity of individuals as a function of infection‐age, which does not exist in the compartmental models. We also discuss the way to calculate the basic reproduction ratioR0for each class of models.
Econometric Methods in Health Services
Authors: Maciejewski Matthew L. , Hebert Paul L. , Conrad Douglas A. , Sullivan Sean D. 
Published: September 2014

Abstract
The purpose of this article is to provide a brief summary of prominent econometric applications in modern health economics, by providing a broad overview of problems to which econometric methods have been applied. These methods typically are used to address problems in health, health care delivery, and health care costs from a cross‐section of observations at a given time. The chapter considers methods for analyzing continuous outcomes, such as cost; continuous, nonnormally distributed outcomes; simultaneous equation methods; and methods for count data, such as utilization data.
Health Economics
Authors: Conrad Douglas A. , Sullivan Sean D. 
Published: September 2014

AbstractThis chapter examines the major research questions facing health economics today and describes the larger context for those questions in terms of allocative efficiency and technical efficiency. The theory underlying various decision‐making algorithms is also elucidated–cost–effectiveness, cost–benefit, and cost–utility analyses being the most prominent examples. The Pareto‐efficiency criterion is explicated, and the underlying assumptions of the different algorithms are presented. The chapter concludes with examples of the application of various decision rules and develops the concepts of risk, return, and the time value of resources devoted to health investments.
Health Services Data Sources in Canada
Authors: Taillon S. 
Published: September 2014

Abstract
Canada has a predominantly publicly financed health insurance system that covers medically necessary hospital (inpatient and outpatient) and physician services for all residents. This system, popularly known as “Medicare”, consists of 13 interlocking health plans administered by the 10 provinces and three territories.
In December 1993, the Canadian Institute for Health Information (CIHI) was established as an independent, nongovernmental, not‐for‐profit corporation and the Deputy Ministers of Health confirmed the CIHI board as its principal advisor on health‐information‐related matters. The CIHI was established to serve as the national mechanism to coordinate the development and maintenance of a comprehensive and integrated health information system for Canada. In addition, the CIHI provides and coordinates the provision of accurate and timely information required for the establishment of sound health policy; the effective management of the Canadian health system; and to generate public awareness about factors affecting good health.
This article describes some of the major Canadian health‐related data holdings, which may be accessible to researchers or others.
Health Services Research, Overview
Authors: Lohr Kathleen N. 
Published: September 2014

Abstract
Health services research studies how social factors, financing systems, organizational structures and processes, technology, and personal behaviors affect access to health care, the costs and quality of health care, and the health and well‐being of individuals and populations. HSR, a boundary‐crossing field, engages a very wide set of clinical and academic disciplines, investigates a broad array of questions relating to health care delivery and management, financing, clinical practice, personal health care decision‐making, and health policy, and employs virtually all quantitative and qualitative methods found in the social sciences, economics, outcomes measurement, management and administration, epidemiology and statistics, law, and policy analysis. It has one major professional organization, Academy Health, and is supported in the US in large measure by the federal Agency for Healthcare Research and Quality, certain other federal agencies, and philanthropic organizations; numerous journals exclusively or predominantly publish on HSR‐related topics.
Informatics in the Health Sciences
Authors: Lehmann Harold P. 
Published: September 2014

Abstract
Informatics is the use of information technology, broadly conceived, to advance a domain of work or inquiry. Concerns within informatics divide into system, role, function, workflow, information repository, information tools, standards, and technology. Individual efforts go through four phases: model formulation, system development, system installation, and study of effects. Areas within health sciences informatics include medical informatics, nursing informatics, public health informatics, and statistics informatics.
Admixture Mapping
Authors: Majumder Partha P. 
Published: September 2014

Abstract
Admixture between populations leads to linkage disequilibrium between loci if the allele frequencies are different between populations. One can test whether the linkage disequilibrium is due to linkage or admixture. One can condition the analysis on parental genotype or parental admixture. In the former case, what is scored is whether a haplotype in a parent is transmitted or not to an affected offspring.
Anticipation
Authors: Dawson Deborah V. 
Published: September 2014

Abstract
Anticipation denotes a phenomenon characterized by increasing severity and/or earlier onset of a disease in successive generations. It could be induced by ascertainment bias and this cast serious doubts among many geneticists about its reality as a biological, phenomenon. However, it is now recognised that it may occur as result of trinucleotide repeat expansion. The number of repeats has been found to correlate with severity and onset age, and this is often associated with a parent‐of‐origin effect. Anticipation has also been reported for a growing number of disorders not found to be associated with trinucleotide repeat expansion.
Ascertainment
Authors: Hodge S. E. 
Published: September 2014

Abstract
For most disorders of genetic interest, families are not selected for study at random but rather because they are segregating for the disorder. This biases the estimate of the segregation ratio if not properly allowed for in the analysis. The classical ascertainment model assumes families are ascertained if they contain at least one proband, an affected person who at any time was detected independently of the other members of the family. Ascertainment can be complete (“truncate selection”), multiple, or single; in the last situation, the probability of that a sibship will be ascertained is proportional to the number of affected sibs it contains. In practice, this independent “proband” model may not be appropriate and other models have been proposed. However, some situations still remain difficult or intractable.
Assortative Mating
Authors: Wilson Susan R. 
Published: September 2014

Abstract
Assortative mating occurs when the phenotypes of biological parents are more (or less) similar than would occur under random mating. In contrast to inbreeding, mechanisms for assortative mating are poorly defined. The classical model for assortative mating makes strong assumptions, such as independent genetic and environmental contributions to a trait and no interaction between genotype and environment. More complex models have been developed, especially for human behavioral traits. Assortative mating affects the estimations and interpretation of heritability and lowers the power of linkage studies.
Cladistic Analysis
Authors: Zhu Xiaofeng , Cooper Richard S. , Elston Robert C. 
Published: September 2014

AbstractHaplotype based association analysis is playing an important role in dissecting the genetic factors underlying complex diseases. Cladistic analysis, which incorporates the evolutionary information among haplotypes, can improve the power to detect genetic factors by limiting the number of hypothesis tests that need to be performed. In this article, we describe the cladistic analysis method and its application to mapping quantitative trait loci.
Comparative Genomic Hybridization
Authors: H. Moore Dan 
Published: September 2014

Abstract
Comparative genomic hybridization is a method for directly identifying regions of gains and losses of genomic material in chromosomes. The results are measured as a series of test‐to‐reference signals from fluorescent dyes for a large number of distinct chromosomal regions and statistical methods are used to identify regions where the signal ratios are significantly different from 1.0. It is applied to microarrays to define abnormality in tumours.
Complex Diseases
Authors: Palmer Lyle J. 
Published: September 2014

Abstract
The principal distinction between “simple” monogenic diseases and “complex” diseases is that the latter do not exhibit classic patterns of Mendelian inheritance. Complex diseases tend to involve greater difficulties in phenotype definition and present challenges for genetic epidemiology. There is, typically, incomplete penetrance and genetic heterogeneity and they are often closely associated with “intermediate” phenotypes that are themselves highly heritable. The use of isolated populations, inbred animal models, and linkage and association analysis with haplotypes are often used in the study of complex diseases.
E
lston–
S
tewart Algorithm
Authors: Elston Robert C. 
Published: September 2014

Abstract

The Elston–Stewart algorithm is a recursive algorithm that allows one to calculate the pedigree likelihood of an oligogenic model as a function of founder genotypic probabilities, penetrance functions, and genetic transition probabilities. The amount of time required for the computation increases linearly with the size of the pedigree, but exponentially with the number of loci in the genotype. In contrast, the Lander & Green algorithm has computational time that increases exponentially with the size of the pedigree, but linearly with the number of loci. An analogous algorithm allows the likelihood to be calculated for a pedigree of
n
individuals under polygenic inheritance without needing the inversion of an
n
×
n
symmetric matrix.
Eugenics
Authors: Pelias Mary Z. 
Published: September 2014

AbstractThe Eugenics Movement was born in England in the late nineteenth century and rapidly found ardent followers in England, the United States, and Germany. The goal of eugenics was to describe and improve the genetic endowment of man. Investigations in eugenics included studies of physical traits and criminality, social traits and pathology, mental traits, and racial and ethnic traits. Many of these studies were later discounted as pseudoscience, strongly influenced by ethnocentric biases of the investigators. The pursuit of eugenic goals eventually led to legal sterilizations, restrictive immigration laws, and to obscene violations of human rights and barbarous excesses of racism and genocide in Nazi Germany. Today, eugenic principles are no longer imposed by law, but they remain alive in the choices that humans make about transmitting their genes to their offspring.
Linkage Disequilibrium
Authors: Chakravarti Aravinda 
Published: September 2014

AbstractThe nonrandom association between alleles at different loci is termed linkage disequilibrium. This note discusses the origin of this concept, its theoretical genetic basis, its estimation and tests of significance, and applications. Linkage disequilibrium studies are critical in human disease gene mapping and is now a major area of research with the availability of variation of the human genome sequence.
Linkage Information Content
Authors: Guo Xiuqing 
Published: September 2014

Abstract
The probability of knowing IBD sharing status of relative pairs at polymorphic genetic markers is an important factor for a model‐free linkage study. Here, Linkage Information Content (LIC) value is introduced, which measures the informativeness of a marker for identity‐by‐descent (IBD) sharing status of relative pairs. LIC value is independent of the mode of inheritance of the disease, but is specific to the type of relative pairs used.
Privacy in Genetic Studies
Authors: Pelias Mary Kay 
Published: September 2014

AbstractThe evolution of the concept of privacy has included protections of property, personal space, personal activities, personal autonomy, and personal information. These ideas expanded over the twentieth century to include the individual's interests in medical treatment and participation in biomedical research. In response to the human genome project, strong moral and legal principles are now in place for protecting the confidentiality of genetic information and the rights of individuals whose genotypes are elucidated in medical practice and in genetics research.
Bioinformatics in Functional Genomics
Authors: Kohane Isaac S. , Butte Atul 
Published: September 2014

AbstractDatabases of DNA sequences, physical maps, genetic maps, gene polymorphisms, protein structures, and gene expression have produced a need for systematic quantitative analysis that goes by the name of bioinformatics. To manage these large datasets in the domain of functional genomics requires algorithmic implementation on computers. Functional genomics will continue to be hypothesis‐driven and hypothesis‐generating biological research. Because the datasets are of high dimensionality, involving relatively small numbers of cases, a large number of solutions can explain the data; the computational techniques to unravel these involve both supervised and unsupervised learning.
Animal Screening Systems
Authors: Haseman Joseph K. 
Published: September 2014

AbstractAnimal screening systems are experimental protocols designed primarily to screen substances in laboratory animals for possible adverse effects in humans. These experiments include long‐term rodent toxicity/carcinogenicity bioassays and shorter duration studies that focus on reproductive and developmental toxicology endpoints. These experiments are the first step of a broader risk assessment process to determine the magnitude of human risk associated with exposure to a test substance. Statistical issues that arise in these studies include (i) selection of the number, magnitude, and spacing of doses; (ii) use of statistical methodology that adjusts for potentially confounding factors such as survival, body weight, and litter effects; (iii) multiple comparison adjustments for the large number of variables evaluated; (iv) use of historical control data; and (v) development of appropriate statistical methodology to permit extrapolation of experimental results from high to low doses and from species to species.
Extrapolation, Low Dose
Authors: Gaylor David W. 
Published: September 2014

AbstractWhile many substances are toxic to humans at high doses, naturally there is concern about the safety of long‐term exposure to much lower doses of certain materials. Such effects are difficult to observe directly and the usual approach is to expose animals to high doses of the substance, establish a dose–response relation, and use this to extrapolate much lower doses. The use of maximum likelihood to estimate extreme quantiles of the tolerance distribution is explained. Methods for measuring carcinogenic potency, such as the dose causing a tumor in 50% of animals, TD50, and the maximum tolerable dose (MTD) are introduced. For noncancer outcomes, the concepts of acceptable daily intake (ADI) and no observed adverse effect level (NOAEL) are also mentioned.
Infectivity Titration
Authors: Armitage Peter 
Published: September 2014

AbstractThe virulence of a suspension of particles, typically viruses or bacteria, can be assayed by administering several dilutions of the suspension into host organisms and observing the number of hosts that become infected at each dilution. The usual analysis uses likelihood methods to estimate a parameter that depends on both the density of particles in the suspension and on the chance that each particle will cause an infection. In the simplest model, each particle acts independently of the others and each gives rise to an infection with the same probabilityp. In these circumstances, the probability that a host becomes infected can be found using the Poisson distribution. More elaborate models allowpto vary from host to host according to some distribution. Methods for detecting suchhost variabilityare available. Some discussion is given to the effects of failure of the assumption of independence.
Mutagenicity Study
Authors: Piegorsch Walter W. 
Published: September 2014

AbstractBiometric methods are discussed for analyzing data from bioassays of mutagenic response. Emphasis is placed on the classic microbial system in environmental mutagenesis: the Ames/Salmonellamicrosome assay. The assay produces count data to quantify the mutational response; statistical issues of interest when analyzing such data include testing for extra‐Poisson variability, trend testing for a dose response, and trend testing under a nonmonotone dose response.