bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ion Channel Modeling
Authors: Hawkes Alan G. 
Published: July 2005

AbstractElectrical activity in the nervous system is regulated by the passage of minute electrical currents due to charged ions passing though ion channels in cell membranes. Studying them increases our understanding of normal physiology and the effect of drugs and toxins.Stochastic modeling of ion channel mechanisms is needed to infer the nature of the mechanism from observation of ionic currents, and to estimate the parameters involved. The models are Markov or semi‐Markov processes where, typically, states are aggregated into groups with the same conductance level because we can see what level it is at, not what state it is in.Many distributions can be obtained, such as open times or shut times, and the likelihood can be calculated for a record of sojourns at the various levels that are first extracted from observational noise. Hidden Markov methods have also been applied.
Migration Processes
Authors: Chiang Chin Long , Ball Frank 
Published: July 2005

Abstract

A migration process describes the temporal evolution of a population of individuals who reside in colonies. There is both immigration and emigration, and also migration between colonies. However, there is no reproduction in the population. In the models described in this article, individuals behave independently according to continuous time Markov processes. A single colony model is treated in some detail and the distribution of population size at any given time is derived. Extension to a population comprising
m
> 1 colonies is described. The single colony model is used to motivate a survival model for an individual who is continuously exposed to a low level of toxic material.
Multistage Carcinogenesis Models
Authors: Sherman Claire D. , Portier Christopher J. 
Published: July 2005

Abstract
Biologically‐based mathematical models of carcinogenesis have been used for quantifying cancer risks for approximately fifty years. These models are developed to understand the mechanisms of carcinogenesis as well as to determine reasonable margins of safety for exposure to carcinogens. It is important to capture the biological aspects of carcinogenesis within the framework of the mathematical model to help provide a better understanding of the carcinogenesis process. In addition, it is important to have an accurate model when extrapolating to exposures (doses) observed in the human population since predictions from these models may vary dramatically.
Partner Study
Authors: Shiboski S. C. 
Published: July 2005

AbstractPartner studies are epidemiologic investigations of transmission of infectious diseases in susceptible partners of known infected individuals. Widely used for studying transmission of sexually transmitted diseases (STD) such as Human Immunodeficiency Virus (HIV), these studies are applicable in situations where “partnerships” which link known susceptible and infectious individuals can be identified. Eligibility typically requires that exposure to infection in susceptible individuals is limited to a previously infected partner. This allows potential bias associated with uncertainty about the source of infection to be reduced or eliminated. Partner studies focus primarily on the influence of measured covariates on the probability of transmission, but can also be used to gain knowledge of key parameters such as the infectivity (transmission risk per unit of exposure). Statistical analyses of data from these studies provide a number of unique challenges, most of which arise from the incomplete nature of data describing exposure and infection.
Renewal Processes
Authors: Chiang Chin Long , Ball Frank 
Published: July 2005

AbstractRenewal theory is concerned with a sequence of events in which the waiting times between successive events (known as renewal times) are independent and identically distributed. This article provides a brief introduction to the properties of renewal processes, such as the time until thenth renewal and the number of renewals by timet, treating the cases of discrete and continuous renewal time distributions separately. In the latter case, the limiting distributions asof the backward and forward recurrence times at timet(i.e. the time since the previous renewal and the time until the next renewal) are considered. Delayed, equilibrium and alternating renewal processes are also described briefly, as are renewal reward processes. The theory is illustrated by a few examples.
Epidemic Models, Sensitivity Analysis
Authors: Cairns Andrew J. G. 
Published: July 2005

AbstractIn this article, we consider how one can analyze the sensitivity of epidemic models to changes in the various inputs. We start by discussing the different reasons for conducting a sensitivity analysis. The article then proceeds by discussing the different methods of sensitivity analysis that can be employed.One of our main concerns is to assess which parameters have a significant impact on epidemic dynamics and which do not. However, when we consider relatively complex models, we find that the impact of changes in one parameter can be almost cancelled out by a corresponding change one or more other parameters (e.g. by keeping the reproduction number,R0, fixed). This concept introduces the concept of primary component analysis. The aim here is to identify the small number of components of a model (such asR0) that are the key determinants of epidemic dynamics with the remaining parameters having a relatively small impact.
SIREpidemic Models
Authors: Lefèvre Claude 
Published: July 2005

AbstractThese models describe the spread of infectious diseases in which an individual may move from Susceptible to Infected to Removed. Simple models, including the Reed–Frost chain binomial and the general epidemic, are Markovian with the implication of an exponentially distributed infectious period. The generalized epidemic removes this restriction. More complex models allow for individual or structural heterogeneity in susceptibility, infectivity, and/or mixing behavior.
Tumor Growth
Authors: White R. Allen 
Published: July 2005

AbstractThe measurement of tumor growth rates has applications in such areas as tumor detection, prognosis, and control. The proliferation of tumors can be measured at different levels including gross measures of size, population progression through the cell cycle, and at the molecular level with biomarkers. Each of these methods has specific requirements and problems, particularly, including the heterogeneity both within and between tumors. Since tumors typically consist of a very heterogeneous mixture of normal and tumor cells, problems of interpretation of results must be made with care. Moreover, since tumors typically are measured only at detection with variable unknown histories and, after detection, at only a single or, at most, a few time points, problems in analysis require special methods.
Viral Population Growth Models
Authors: Tuckwell Henry C. 
Published: July 2005

AbstractThe steps involved in viral reproduction are described briefly. Mathematical models for the growth of viral populations without random effects take the form of systems of differential equations and these are reviewed, including the standard HIV equations, with correction terms. The validity of the “mass action” assumption is questioned for low virion levels. Stochastic differential equation models for HIV are described, including those with both discontinuous and diffusion processes. Simulation results are given for early HIV population growth, including the distribution of the time to reach a detection level, for which an analytical theory is also available. The long‐term model of Tan and Wu is discussed briefly as well as mutation and its effect on drug treatments. Despite some useful analysis of patient data with respect to simple differential equations, it is concluded that modeling, either deterministic or stochastic, of viral populations requires much further development to give an accurate and useful representation of the underlying biological processes.
Allometry
Authors: Sprent P. 
Published: July 2005

Abstract
Allometry is a study of shape changes associated with size. Most living organisms change shape as they grow in order to overcome biological or physical constraints. Simply, allometry occurs when two size measurements exhibit an approximately linear relationship between their logarithms. Suggestions made for multivariate extensions range from applications of principal component analysis to studies of size variables and shape vectors. The limitations imposed by Euclidean geometry and other disadvantages with these approaches have been removed by considering curvature and tangential directions at a series of points called landmarks and by coordinate transformations.
General Practice
Authors: Peters T. J. 
Published: July 2005

Abstract
Research in general practice is principally focused on common, chronic medical conditions, covers organizational as well as clinical issues and is a multidisciplinary endeavor. At the same time, conditions seen frequently in particular specialities are relatively uncommon, and this has implications for the statistical performance of diagnostic and screening tests. Generally, biostatistical methods in general practice are characterized by sources of variation operating at various levels, including the primary care center, practitioner, and individual. Both study design and data analysis must make reasonable allowance for this, in particular, in experimental studies where the unit of randomization is a group of individuals. Practical, ethical, and financial constraints mean that a variety of study designs will remain valuable in general practice research, but the central role of the pragmatic randomized trial will continue.
Midwifery, Obstetrics, and Neonatology
Authors: Macfarlane Alison , Elbourne Diana 
Published: July 2005

AbstractMidwifery is the profession responsible for care in normal pregnancy and labor, obstetrics is the profession that deals with pregnancies with clinical complications, and neonatology is concerned with care for sick newborn babies. Members of these professions and others concerned with evaluating the care they give in the context of clinical, environmental, and genetic factors have measured the outcome in terms of maternal and infant mortality and morbidity and used a range of descriptive, analytical, and experimental approaches to do so. From the mid‐nineteenth century onwards, routine vital statistics have been used to describe trends and variations. In latter half of the twentieth century, these were analyzed by correlation, regression, and later multivariate analysis, with techniques being developed for record linkage. Clinicians in these profession and associated researchers were among the first to use randomized trials to evaluate care and meta‐analyses to synthesis the results of trials.
Psychiatry
Authors: Everitt Brian S. 
Published: July 2005

AbstractPsychiatry is the medical speciality that deals with mental disorders. In early medieval times, beating, incarceration, and restraint were the “treatments” endured by the majority of the mentally ill, and a more enlightened perspective has evolved only slowly over the intervening 600 years. In the twentieth century, psychiatry struggled to attain scientific respectability by making quantitative observations on mentally ill patients, and subjecting these observations to a variety of statistical methodology ranging from simple descriptive techniques to complex multivariate analyses.
Radiation
Authors: Darby Sarah C. , Gilbert Ethel S. 
Published: July 2005

AbstractBiostatistical applications in the area of ionizing radiation are primarily directed towards obtaining quantitative risk estimates for various cancers following radiation exposure. The relevant data arise mostly from observational studies on humans. The major populations studied include the survivors of the atomic bombings of Hiroshima and Nagasaki, patients given radiotherapy for a variety of malignant and other conditions, workers in the nuclear industry, persons exposed in countries of the former Soviet Union, uranium miners, and populations living in areas where residential radon concentrations are high. Experimental animal data are also available. The large quantity of information and the availability of quantitative dose estimates in many studies have enabled the development of a variety of complex risk models. Recent work has included bringing together data from several different studies, usually with the objective of producing more precise estimates of risk, and the development and application of methods that account for uncertainties in the estimates of radiation dose.
Reproduction
Authors: Farley Timothy M. M. 
Published: July 2005

AbstractAssessing objectively the efficacy and effectiveness of contraceptive methods is challenging because of the large number of different factors affecting the intrinsic fertility of a couple, the timing of intercourse, and the way the method is used. While effectiveness, or the typical use pregnancy rate, is easier to observe, it is difficult to generalize to other populations since it includes behavioral factors, such as how or whether the method was used. By contrast, efficacy, or the perfect use pregnancy rate, can be indirectly estimated from patterns of intercourse and method use, and is more easily generalized to other women or couples. For methods that are not coitus‐dependent, typical and perfect use pregnancy rates are similar, but can be very different for methods requiring a high degree of user compliance.
Transfusion Medicine
Authors: Casbard Angela 
Published: July 2005

Abstract
Transfusion medicine is the specialty of medicine devoted to all aspects of the use of blood products and components, from donor selection and care, through testing and component preparation, to storage, distribution, and use. It also includes pregnancy testing for blood group incompatibilities. This article provides a brief overview, including a history of blood transfusion, current practice, usage of statistics, and recent studies.
Dummy Variables
Authors: Darlington G. A. 
Published: July 2005

Abstract

Categorical explanatory variables can be included in models via the use of dummy variables. In a regression model with an intercept,
K
− 1 linearly independent dummy variables must be defined to represent a
K
level categorical variable. Indicator variables are commonly used to define a set of dummy variables. In a regression model with an intercept, indicators are derived for
K
− 1 of the
K
possible levels and the remaining level is the referent or baseline category. Overall tests regarding the categorical variable will not depend on the choice of dummy variable definitions. However, the interpretation of the coefficient for each dummy variable will depend on the coding used.
Errors in the Measurement of Covariates
Authors: Yi Grace Y. , Cook Richard J. 
Published: July 2005

AbstractMeasurement or misclassification errors in covariates lead to biased estimates of regression parameters. Corrections typically, require addition date such as replication of errors observations, validation through direct observation of correct values, or the introduction of further “instrument” variables. Methods described include regression calibration, simulation extrapolation, and the use of estimating equations. An example is based on a case–control study of cervical cancer.
Goodness of Fit
Authors: Atkinson A. C. 
Published: July 2005

AbstractPearson's classical chi‐square test compares observed and expected frequencies in various classes. It tests for unspecified departures from the model, is based on aggregate rather than partial data, and uses an asymptotic distribution. Goodness of fit in multiple regression may be explored by including further explanatory variables in the model, or by comparing residual variation against external predictions. Tests based on residuals are described, as are extensions to generalized linear models.
Multilevel Models
Authors: Goldstein Harvey 
Published: July 2005

AbstractBiostatistical data often have a hierarchical structure. Typically, these structures are naturally occurring ones: animal populations are characterized by individuals nested within parents, themselves often nested within groups or herds, which may also be nested within spatial entities. In other cases, the structure may result from research designs, as in multicenter clinical trials. Other examples are repeated measure designs, where measurements are “nested” within individual subjects and multivariate response data, where measurements are “nested” within individuals. In addition to nesting relationships among data units, we may also have cross‐classifications. For example, an individual cow may be nested within a herd of cattle, but may also be the offspring of parent stock, where any parent may contribute to several herds: individual cows are thus cross‐classified by parents as well as nested within their herds. A further complexity is also often present whereby individual units at one level of a data hierarchy may be nested within more than one higher‐level unit as in spatial data.The article describes models for all these kinds of data structures, increasing in complexity as they move from simple hierarchies with continuously distributed responses, to cross‐classifications, multiple unit membership and multivariate data, and to discrete responses. Various extensions and special cases will also be considered. The emphasis is on model specification with a brief section on estimation.
Multiple Linear Regression
Authors: Matthews David E. 
Published: July 2005

AbstractMultiple linear regression involves finding the best‐fitting surface of a suitable functional form that relates the values of explanatory variables,X1, …,Xk, and the mean value of a response variable,Y, given values ofX1, …,Xk. The objectives of regression modeling are to determine whetherYand one or more of the explanatory variables are associated in some systematic way, and to estimate or predict the value ofY, or its mean, corresponding to known values of a selected subset ofX1, …,Xk. We describe methods of estimation, variable selection, statistical inference, and diagnostic checking of the assumed model and any associated unknown parameters, giving due importance to their statistical and scientific interpretations. An example concerning the relationship between oxygen uptake and age, weight, sex, the time required to run 1.5 miles, and various pulse rates for participants in a physical fitness workshop illustrates these concepts concretely. Finally, we outline the close connection between ordinary and weighted linear regression.
Nonlinear Regression
Authors: Kolkiewicz Adam W. 
Published: July 2005

AbstractThe defining feature of nonlinear regression models is that the expectation function depends on some parameters in a nonlinear way. In such cases, the classical results for linear models, including explicit estimation, normality of estimators, exact tests, and analysis of variance, break down. In nonlinear regression, estimates are usually found by iteration and for the distribution of estimators and test statistics, we have to rely on approximations, which are often based on linear models. Several measures of nonlinearity have been developed to provide information on the adequacy of such approximations and to indicate situations where, by changing the parameters, a nonlinear model behaves more like a linear one. In the article, we provide several examples of nonlinear models used in practice. We also give a brief description of some measures of nonlinearity and outline basic nonlinear optimization techniques used in estimation procedures.
Capture–Recapture
Authors: Chao Anne 
Published: July 2005

AbstractCapture–recapture methods are used to estimate population sizes and related parameters such as survival rates, birth rates, and migration rates. A typical capture–recapture experiment places traps in the study area and samples the population several times. At each trapping sample, one records and attaches a unique tag to every unmarked animal, records the capture of any animal that has been previously tagged, and returns all animals to the population. This article describes the history of the method, discusses applications to epidemiology, discusses potential models, and discusses the challenges inherent in estimating the population size well.
Categorical Data Analysis
Authors: Imrey Peter B. , Koch Gary G. 
Published: July 2005

AbstractCategorical data consist of observations counted and subclassified into one among a set of disjoint, exhaustive groups. The groups may be defined on the basis of a single or a combination of qualitative characteristics and/or quantitative measurements. When such categorizations are to be modeled as probabilistic “responses” or “outcomes”, statistical analysis must be based upon discrete distributions and requires extensive generalization of the classical notions of regression and analysis of variance commonly used for continuous observations. This overview article reviews statistical inference for categorical data, from the basic issues in analyses of 2 × 2 contingency tables through generalized linear mixed models, with emphasis on approaches that have increasingly unified our concepts of categorical and continuous data modeling.
Chi‐Square Tests
Authors: Koehler Kenneth 
Published: July 2005

AbstractA chi‐square test investigates whether observed data are consistent with a proposed probability model. Best‐known chi‐square statistics are the Pearson statistic and the likelihood‐ratio statistic. This article also mentions various other statistics that have limiting chi‐square distributions, all of which are special cases of the power divergence statistic. The article begins by introducing simple goodness‐of‐fit tests, then discusses a variety of chi‐square statistics. Chi‐square tests are then presented that apply with fixed intervals or with random intervals. Applications are then discussed for categorical data, such as testing independence. Extensions are discussed for small samples and sparse tables, and for correlated responses and complex surveys.
Chi‐Square, Partition of
Authors: Gilula Z. , Haberman Shelby J. 
Published: July 2005

AbstractA chi‐square statistic suitable for testing a primary hypothesis can be partitioned into components such that each component gives a test for a corresponding secondary hypothesis. Some partitionings are exact and some are approximate. The theory is based on the Fisher–Cochran theorem about decomposing quadratic functions of normal variables. The history of this technique is surveyed. Applications are described for contingency tables, with the main focus on the two‐way table and likelihood‐ratio statistics. Brief mention is also made of partitioning into non‐chi‐square components, such as a decomposition that forms the basis of correspondence analysis.
Contagious Distributions
Authors: Kemp Adrienne W. , Kemp C. D. 
Published: July 2005

Abstract
Polya proposed a model for the spread of an infection, whereby the probability of catching a disease depended on the existing number of cases. This leads to a distribution that has a limiting negative binomial form. Neyman proposed an alternative approach that arises from a Poisson distribution of clumps, with the number of cells for each clump being Poisson. This article surveys probability distributions and corresponding generating functions that result from such constructions.