bluuebunny/crossref_metadata_2025_split huggingface dataset:
Candidate Gene
Authors: Elston Robert C. 
Published: July 2005

Abstract
The “candidate gene” approach presupposes that we have reason to believe that a particular metabolic pathway underlies a disease. We then try to confirm or refute this belief by seeking a disease–marker association or linkage, where the marker is the causative gene, in linkage disequilibrium with the causative gene, or tightly linked but in equilibrium with it.
Eugenics
Authors: Pelias Mary Z. 
Published: July 2005

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.
Genetic Risk Ratios
Authors: Kopciuk K. A. , Bull Shelley B. 
Published: July 2005

Abstract
Genetic risk ratios measure the association between known or putative genetic factors and the occurrence of a particular disease. These risk quantifiers are expressed as relative risks (RRs) or odds ratios (ORs). Risk ratios for relatives, such as measures of familial aggregation or recurrence risk in relatives, use family disease history to assess the evidence for an inherited genetic basis for the disease. Allelic, genotype, and haplotype risk ratios evaluate the association of a disease with a measured candidate gene or with one or more genetic markers. Risk ratios estimated under a case–control design may be population‐based or family‐based depending on the type of control group.
Genotype
Authors: Elston Robert C. 
Published: July 2005

Abstract
The genotype of an organism is its genetic make‐up, as opposed to its phenotype, or outward appearance. If the two alleles at a locus are the same, the genotype is homozygous, otherwise it is heterozygous. If a heterozygote has the same phenotypic distribution as one of the corresponding homozygotes, the allele in that heterozygote is dominant and the other allele is recessive with respect to that phenotype. A multilocus genotype is made up of two haplotypes. In the case of a quantitative trait, dominance corresponds to intralocus interaction; epistasis to interlocus interaction.
Calibration
Authors: Lee J. Jack , Moon Hojin 
Published: July 2005

AbstractStatistical calibration is often used in biomedicine as a means to estimate the quantity of a substance that is difficult or expensive to measure by another measurement, which is easier or cheaper to obtain. Classical or inverse estimators can be computed from parametric or nonparametric regression models. Multivariate calibration, Bayesian modeling, and optimal designs are reviewed. Selected topics such as interval estimation, measurement error, robustness, heteroscedasticity, instrumental calibration, and survey sampling are discussed as well.
Dynamic Allocation Index
Authors: Gittins J. 
Published: July 2005

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.
Stimulus–Response Studies
Authors: Morgan Byron J. T. 
Published: July 2005

AbstractSimple stimulus–response experiments result in estimates of the probability of a hit (getting it right) and probability of a false alarm (getting it wrong), which produce a point on a receiver operating characteristic (ROC) curve. Signal‐detection models convert this information into a single measure of performance. There are several elaborations of the simple experiment. ROC curve methodology has been applied in areas such as ecology, medicine, engineering, and psychology.
Conception, Models for
Authors: Weinberg Clarice R. 
Published: July 2005

AbstractTime to conception offers a stochastic surrogate for fertility, in that subfertile couples not using contraception tend to take longer to conceive than do fertile couples. Couples can be studied retrospectively, by asking about how long it took them to achieve an index pregnancy, or prospectively, by recruiting couples during or prior to an attempt to conceive. The resulting data play out in discrete time, as measured by the number of menstrual cycles required. Because of heterogeneity among couples in their fecundability (per cycle probability of conception), the data are overdispersed compared to the geometric. This overdispersion can be modeled parametrically (e.g. with a beta‐geometric) or nonparametrically (based on the moments of the dispersed but unknown underlying fecundability distribution). If more detailed data are available, on days with intercourse and time of ovulation, then the models for conception can additionally incorporate the strong effects of timing.
Epidemic Thresholds
Authors: Ball Frank 
Published: July 2005

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.
Hidden
M
arkov Models
Authors: Churchill Gary A. 
Published: July 2005

Abstract
Hidden Markov models are a generalization of the finite mixture model that allow for dependence in a sequence of observations via Markovian dependence of an unobserved (hidden) sequence of states. Inference problems associated with hidden Markov models include parameter estimation and restoration of the hidden state sequence. The recursive algorithms employed to solve these problems are related to the Kalman filter and have spawned a wide variety of applications. Areas of application range from speech recognition and signal processing through analysis of molecular sequence data.
Michaelis–Menten Equation
Authors: Dunn Graham 
Published: July 2005

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.
Ornstein–Uhlenbeck Process
Authors: Blackwell Paul G. 
Published: July 2005

AbstractThe Ornstein–Uhlenbeck process (OUP) is a unique Gaussian, stationary diffusion process. Originating as a model for the Brownian motion of a particle, it has a wide range of applications in biology and elsewhere. These include the modeling of membrane potentials of nerve cells, modeling the location or velocity of anything from whole organisms to individual cells and the use of the OUP as a limiting approximation to population models in epidemiology and genetics. Another area of application is the large‐sample behavior of likelihood ratios for multiple genetic markers. As well as including discussion of these (and other) applications, this article describes likelihood‐based inference and mathematical properties such as first passage times.
Queuing Processes
Authors: Chiang Chin Long , Ball Frank 
Published: July 2005

AbstractThis article is primarily concerned with two queuing models,M/M/sandM/G/1, both of which assume Poisson (M) arrivals. In theM/M/squeue, there aresservers and the service time is exponentially (M) distributed. In theM/G/1 queue, there is a single server and the service time follows an arbitrary but specified distribution (G). For theM/M/smodel, the equilibrium distribution of the number of customers in the system is derived, as are other equilibrium properties of the system, such as the mean waiting time of a typical customer and the probability that a given server is busy. Two approaches to analyzingM/G/1 queues are outlined,embeddingand themethod of stages. Formulae are given for the equilibrium mean number of customers in the system and the equilibrium mean total time spent in the system by a typical customer, and properties of a typical busy period of the server are outlined. TheM/M/∞ model, in which there are infinitely many servers, is described briefly.
Serial‐Sacrifice Experiments
Authors: Dewanji A. 
Published: July 2005

AbstractSimple survival experiments provide little information to study the progress of different disease(s) and possible interaction between them. In serial sacrifice experiments, individual animals are randomly selected and killed (sacrificed) at fixed or adaptively selected time points, allowing the examination of presence or absence of the different diseases. Therefore, a serial sacrifice experiment, with interim observation on some individuals before natural death occurs, allows one to probe into the disease processes and the complex interrelationships between different diseases. Data from the sacrificed animals provides an estimate for prevalencep𝒜(t), the probability being that an alive animal has a particular disease combination 𝒜 at timet. On the other hand, death data provides an estimate of probability of death with the particular disease combination 𝒜, given by μ𝒜(t)p𝒜(t), where μ𝒜(t) denotes the mortality rate from disease combination 𝒜. In animal carcinogenicity experiments with serial sacrifice, in particular, the tumor occurrence time distribution is estimable. Design issues regarding the optimal sacrifice schedule are of some interest, but not much work has been done on this. There are few examples of serial sacrifice experiments, the ED01 study with 2‐acetylamino‐fluorene (2‐AAF) conducted by the US national center for toxicological research (NCTR) being probably the largest.
Shape Analysis
Authors: Dryden Ian L. 
Published: July 2005

AbstractShape is defined to be all the geometrical information that is invariant under a set of registration transformations. For example, the registration transformations might be translation, rotation, and scaling. We discuss Kendall's shape space for two‐dimensional sets of landmarks, which is the complex projective space. Probability distributions for planar shapes are reviewed and some approximate distributional results for higher‐dimensional cases are given. Practical shape inference using Procrustes analysis is discussed, and particular emphasis on multivariate analysis in tangent spaces is given. We discuss principal component shape analysis and hypothesis tests, and provide some examples from biology and medicine. Finally, we discuss some applications of shape analysis in image analysis.
Tumor Incidence Experiments
Authors: Dinse Gregg E. 
Published: July 2005

AbstractThis article describes a variety of methods for analyzing data from animal tumorigenesis experiments. The primary endpoint of interest is the tumor incidence rate. Both the estimation of tumor incidence rates and the comparison of these rates across treatment groups are discussed. Analyses are complicated by the fact that all onset times are censored when tumors are occult, censoring is informative unless tumors are completely nonlethal, and biases arise if proper adjustment is not made for survival differences across groups. Existing methods address such concerns by assuming a particular level of tumor lethality, assigning individual causes of death, requiring some animals to be sacrificed, specifying parametric models, or imposing functional restrictions. This article describes each of these approaches and summarizes the associated advantages and disadvantages.
Two‐Mutation Carcinogenesis Model
Authors: Moolgavkar Suresh H. 
Published: July 2005

AbstractThe two‐mutation carcinogenesis model, better known as the two‐stage clonal expansion (TSCE) model, is a type of multistage carcinogenesis model that explicitly considers stochastic growth of intermediate cells on the pathway to malignancy. Because of this fact, this model can be used not only for analysis of time‐to‐tumor data, which requires the hazard function, but also for analysis of intermediate lesions, such as colon polyps, which requires expressions for the number and the size distribution of such lesions. The model has been extensively used for analyses of both types of data. Extensions of the model to incorporate more than two mutations have also been proposed.
Chronomedicine
Authors: CornÉlissen Germaine , Halberg Franz 
Published: July 2005

AbstractChronobiology (fromchronos, time;bios, life; andlogos, science) investigates the mechanisms underlying variability in the otherwise unassessed physiological range, including rhythms found in us, resonating with cycles around us. Broad time structures (chronomes) consisting of deterministic chaos and trends organized by rhythms are found in organisms and in their environments. They are mapped by chronomics as the reference values for both an applied chronomedicine and a basic chronobiology. Chronomics quantify health, identifying new disease risks, diagnosing predisease and overt illness, enabling timely and timed treatment (Rx), and validating the short‐ and long‐term efficacy of a givenRxon an inferential statistical individualized (as well as population) basis.
Otorhinolaryngology
Authors: Zielhuis Gerhard A. , Straatman Huub , Schilder Anne G. M. 
Published: July 2005

AbstractBiostatisticians entering the field of ORL are confronted with large numbers of patients and a diversity of diseases and treatment options. Most of the statistical procedures used are similar to those used in other branches of medicine. Special features are the potential of each patient having two ears, the fluctuating course of many of the common ORL diseases, and the diversity of management options.
Critical Care
Authors: Corwin Howard L. 
Published: July 2005

AbstractThe randomized controlled trial (RCT) has become the “gold” standard for clinical research. However, the complex nature of critical illness has made the design and conduct of RCTs difficult. Research in critically ill patients presents unique challenges. The disease processes are often not well defined. There are also multiple therapeutic interventions being administered and the disease processes themselves often have a variable clinical course. Finally, outcome selection is a challenge, mortality (short or long) versus nonmortality outcomes. The last few years have seen the publication of the results of five RCTs that have had a major impact on the practice of critical care.
Bayesian Measures of Goodness of Fit
Authors: Rice Kenneth 
Published: July 2005

AbstractWe consider methods for critical assessment of the appropriateness of fitted Bayesian models. This is done for situations where no particular alternative model is specified, and where the discrepancy between model and data is measurable in a user‐defined way. We review methods for testing goodness of fit in nonhierarchical models before discussing hierarchical models.
Model Checking
Authors: Chandler R. E. 
Published: July 2005

AbstractMost statistical analyses are, implicitly or explicitly, based upon an underlying model for the data at hand. The conclusions from any analysis can therefore be justified only if the model is adequate. This article gives an overview of general principles for checking the adequacy of a statistical model. The focus is primarily upon models for relationships between variables. Specific techniques are summarized; for the reader who requires more detail, pointers to the relevant literature are given.
Separate Families of Hypotheses
Authors: De Braganca Pereira Basilio 
Published: July 2005

AbstractIn the last forty years since statistical procedures were first developed to test separate (nonnested) families of hypotheses, a huge amount of literature has developed for testing a null model against one or more separate alternatives. In the first two decades, mainly statisticians were interested in this problem. During the eighties the econometricians took a great interest and in the nineties, Bayesian statisticians developed some new results on the subject. The purpose of this paper is to given an introductory overview of the subject and an updated bibliography.
Variance Components
Authors: Solomon P. J. 
Published: July 2005

AbstractRandom variation may have a complex structure, with several identifiable sources and corresponding components of variance. In a hierarchical or multilevel model, parameters may change, for example, between individuals, their variation being measured by variance components. This article reviews the history of the study of variance components and describes methods of estimation. Topics include synthesis of variance, empirical Bayes methods, nonnormal models, and model assessment and prediction.
Binary Data
Authors: Collett D. 
Published: July 2005

AbstractWhen a response variable can take one of two possible forms, such as alive or dead, the resulting data are said to be binary. The focus of this article is on models for binary response variables, and the use of logistic regression models is emphasized. The models can be fitted using most statistical software packages. Alternative models can be compared using the deviance, and for grouped binary data, this statistic may be used as a measure of goodness offit. Techniques for assessing model adequacy are outlined and the use of these methods is illustrated with an example.
Binomial Distribution
Authors: Wypij David 
Published: July 2005

Abstract

The binomial distribution refers to the number of successes in
n
independent trials with a common success probability
p
in each. With large
n
, the distribution is approximated by the normal, and with large
n
and small
p
, it is approximated by the Poisson. This article reviews its properties and discusses point estimation, confidence interval estimation, and hypothesis testing for the parameter
p
. Extensions are also briefly considered.
Bradley–Terry Model
Authors: Imrey Peter B. 
Published: July 2005

AbstractBradley–Terry models parsimoniously summarize preferences expressed in paired comparisons of objects through a set of latent “merit” or “preference” parameters linked to the individual objects rather than to the pairs. When comparisons of all pairs ofkobjects are observed, the resultingk(k‐ 1)/2 linearly independent proportions are reduced tok‐ 1 latent parameters, or potentially fewer if these are further modeled using characteristics of the objects. This entry introduces the basic model, discusses its relationship with the probit, logit, and loglinear models developed in other contexts, and describes some extensions that allow for data with ties, varying degrees of preference, or simultaneous expressions of preferences along multiple dimensions of assessment.
Correlated Binary Data
Authors: Pendergast Jane F. , Gange Stephen J. , Lindstrom Mary J. 
Published: July 2005

AbstractCorrelated binary data may arise in longitudinal or spatial studies or through some other form of clustering of sampling units. The simplest setting, with clusters of size two and no covariates, leads to familiar methods of analysis. Models with covariates include response feature models, conditionally specified models, transitional models, marginal models, and cluster‐specific models. These may lead to different interpretations of the regression parameters.
Exact Inference for Categorical Data
Authors: Corcoran Christopher D. , Senchaudhuri Pralay , Mehta Cyrus R. , Patel Nitin R. 
Published: July 2005

AbstractCommonly used methods for analyzing categorical data rely on asymptotic theory and may be misleading for small data sets. ExactPvalues and confidence limits are available based on the permutation distributions of appropriate test statistics. Details are given here for a wide range of settings with contingency tables, which may be stratified or involve clustering, including also logistic regression. Various software packages are available.
Fisher's Exact Test
Authors: Routledge Rick 
Published: July 2005

AbstractFisher's exact test can be used to assess the significance of a difference between the proportions in the two groups. In particular, it provides a widely applicable way to assess the results of simple, completely randomized experiments leading to two‐by‐two contingency tables with small frequency counts, However, it has low power, especially when the standardP‐value is used. The power can be increased considerably through either using the mid‐Pvalue, or carefully constructing a test based, at least in part, on a binomial model.
Generalized Linear Mixed Models
Authors: McCulloch Charles E. , Neuhaus John M. 
Published: July 2005

Abstract
Generalized linear mixed models (GLMMs) are an extension of the class of generalized linear models in which random effects are added to the linear predictor. This allows the modeling of correlated, possibly nonnormally distributed data with flexible accommodation of covariates. We describe this class of models and the inferences possible and contrast them with marginal modeling techniques such as generalized estimating equations.
Interaction Model
Authors: Kreiner Svend 
Published: July 2005

AbstractLoglinear models that describe association patterns among categorical variables are sometimes calledinteraction models. Graphical interaction models are useful for portraying conditional independence and indicating when an association remains the same after collapsing over a variable. This article discusses a “Separation theorem” that describes how conditional independence patterns are maintained by conditioning on a certain subset of variables as indicated by an interaction graph. Collapsibility issues are also discussed.