bluuebunny/crossref_metadata_2025_split huggingface dataset:
Parallel‐Line Assay
Authors: Redmond Carol K. 
Published: September 2014

Abstract
Parallel‐line assays are the most common form of indirect analytical assays. The aim is to estimate the potency of substance under test relative to the potency of a standard. The test and standard preparations are administered at several dose levels. The key assumption is that the expected response for a given dose of the test preparation is the same as that which would be obtained from the standard preparation administered at a dose that is ρ times the given dose: ρ is known as the relative potency. It is further assumed that the expected response is linearly related to the logarithm of the dose. If this obtains, the graphs of expected response against log dose for the test and standard preparations will give parallel lines. The response can be continuous, in which case estimation is through a linear model, or quantal, in which case analysis usually assumes a logistic model. The estimator of ρ is defined in terms of ratios of regression coefficients and interval estimates are often obtained by use of Fieller's theorem.
Stochastic approximation with biomedical applications
Authors: Gross Alan J. , C. McLean David 
Published: September 2014

AbstractStochastic approximation is a method for approximating the root of the equationM(θ) = m0, when the functionM(.) is not directly observable but can be cast as the expectation of a suitably defined regression function. The method was introduced by Robbins and Munro in 1951 and has since been extensively refined and extended. It has found application in many areas of biostatistics and in the application of neural networks.
Analysis of Variance for Longitudinal Data
Authors: Dunn Graham 
Published: September 2014

Abstract
This entry describes how traditional analysis of variance (ANOVA) techniques might be adapted for use in the analysis of serial measurements. Typically, one might recognize data coming from a longitudinal study as coming from a nested or split‐plot design with serial or repeated measurements (subplots) being nested within each of the subjects (plots). The difference between the results of a typical split‐plot experiment and that involving repeated measurements arises from the fact that the latter are likely to be serially correlated. Methods of adapting ANOVA methods to allow for serial correlation (i.e. the Greenhouse–Geisser correction) are described, together with suggestions for alternative data analytic approaches.
Generalized Linear Models for Longitudinal Data
Authors: Zeger Scott L. , Diggle Peter J. , Huang W. 
Published: September 2014

AbstractThe analysis of longitudinal data needs to take into account the correlations between the repeated measures of the response variable to draw valid and efficient inferences about parameters of scientific interest. There are a variety of approaches that can be taken depending on what is the target of the estimation process. This article outlines marginal models, random effects models and transition models.
Linear Mixed Effects Models for Longitudinal Data
Authors: Pinheiro José C. 
Published: September 2014

AbstractLinear mixed‐effects (LME) models have become a popular tool for analyzing longitudinal data that arise in areas as diverse as clinical trials, epidemiology, agriculture, economics, and geophysics. The increasing popularity of these models is explained by the flexibility they offer in modeling the within‐subject correlation often present in longitudinal data, by the handling of both balanced and unbalanced data, and by the availability of reliable and efficient software for fitting them. This paper describes a general LME model for longitudinal Gaussian data, presenting the model definition and its underlying assumptions. Maximum likelihood and restricted maximum likelihood estimation methods are discussed, as well as asymptotic inference methods. Available software for fitting and analyzing LME models are briefly reviewed. The model and methods are illustrated through an example from a longitudinal growth study.
Nonparametric Regression Analysis of Longitudinal Data
Authors: Wang J. ‐L. 
Published: September 2014

AbstractNonparametric approaches have recently emerged as a flexible way to model longitudinal data. This entry reviews some of the common nonparametric approaches to incorporate time and other covariate effects for longitudinally observed response data. Smoothing procedures are invoked to estimate the associated nonparametric functions, but the choice of smoothers can vary and is often subjective. Both fixed and random effects may be included for vector or longitudinal covariates. A closely related type of data is functional data, where the prevailing approaches to model random effects are through functional principal components analysis and B‐splines. Related semiparametric regression models also play an increasingly important role.
General Practice
Authors: Peters T. J. 
Published: September 2014

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: September 2014

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.
Ophthalmology
Authors: Marsh Marta J. , Gange Stephen J. 
Published: September 2014

Abstract
The field of ophthalmology and vision research provides a rich source for both the application and the development of statistical theory and methodology, particularly with correlated data, due to the paired nature of much ocular data. This article begins with a brief history of ophthalmology, followed by a section describing the variety of types of data available in the field of ophthalmology and optometry. Finally, a section describing the multitude of common statistical issues that anyone analyzing ophthalmologic data must consider concludes this article, with emphasis on methods for the appropriate analysis of paired (correlated) data.
Otorhinolaryngology
Authors: Zielhuis Gerhard A. , Straatman Huub , M. Schilder Anne G. 
Published: September 2014

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.
Psychiatry
Authors: Everitt Brian S. 
Published: September 2014

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: September 2014

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.
Axes in Multivariate Analysis
Authors: Krzanowski W.J. 
Published: September 2014

Abstract

Visualization of data is valuable in statistical analysis to reveal features that are difficult to detect in numerical summaries. When
p
variables are measured on
n
subjects it can be difficult to graphically depict patterns due to limits of dimensionality. Multivariate graphical analysis identified directions that can be projected into subspaces to highlight sample features. Projecting the original data into a subspace generated from several orthogonal linear combinations allows for low‐dimensional approximation to the full data representation.
Independence of a Set of Variables, Tests of
Authors: Krzanowski W. J. 
Published: September 2014

Abstract

If
p
variables have been divided a priori into distinct sets, then within each set, individual values for each variable may be highly correlated; however, variables in different sets could be treated as independent. Testing independence of such sets of variables is considered through development of a likelihood ratio test. Derivation from this likelihood ratio test to canonical correlation and a union–intersection test is also shown.
L
ikert Scale
Authors: Dukes Kimberly A. 
Published: September 2014

Abstract
A Likert scale is computed by summing responses over several items hypothesized to measure the same latent variable or construct. Likert scales are often measured on Likert response formats, which often have three, four, or five response options and represent degrees of endorsement of items. When creating a Likert sale, usually each item is equally weighted, and assumed to have a linear relationship to the overall scale score.
Multiple Endpoints, Multivariate Global Tests
Authors: B. D'agostino Ralph , Russell Heidy K. 
Published: September 2014

AbstractIn clinical trials, multiple endpoints are often used to investigate a treatment effect. The statistical procedures available to analyze multiple endpoints fall into two categories: multivariate global methods and endpoint‐specific orPlevel methods. Multivariate global methods are used to make an omnibus assessment of the efficacy of the treatments, providing an overallPvalue to reflect whether there are any differences among the treatments on several correlated endpoints. Within multivariate global methods there are two types of tests. The first type involves combining multiple endpoints into a single test statistic, which provides an overall statement to declare whether there are any significant differences among the treatments. The second method arises from Bonferroni‐type adjustments. They adjust the univariate observedPvalues of the individual tests to maintain the familywise type I error rate at a prescribed level, and they provide an overall probability for the trial.
Multivariate Distributions, Overview
Authors: Jensen D. R. 
Published: September 2014

AbstractMultivariate distributions arise throughout statistics and applied probability and they are defined on finite‐dimensional spaces. They serve as probabilistic models for dependent outcomes of random experiments. Biometric data typically comprises observations on multiple characteristics for each experimental subject, and joint distributions are central to the modeling and analyses of such data. From multivariate distributions, the distributions of various sample statistics of note in statistical inference can be derived. Multivariate distributions also characterize the behavior of stochastic processes through properties of their finite‐dimensional projections.
Multivariate t Distribution: Introduction
Authors: Jensen D. R. 
Published: September 2014

AbstractMultivariate versions of the Student'stdistribution derive through Studentization from multivariate normal models. Multivariatetdistributions are useful to model errors of a random experiment offering greater flexibility and heavier tails than multivariate normal models. There are two types of multivariatetdistributions. Type I distributions are derived by scaling each component of a normal vector by a single random scalar. Type II distributions involve separate scalings by elements of a further random vector, itself having a joint multivariate distribution.
O
ptres Rotation
Authors: Jackson J. Edward 
Published: September 2014

Abstract

Optres Rotation
is a three‐stage procedure for obtaining
oblique rotations
used to transform vectors associated with
principal component analysis
or
factor analysis
to
simple structure
. The first stage will be an
orthogonal
rotation such as
Varimax
. The second will be an oblique rotation such as
Promax
. The third rotation requires, for each vector, a specification of which coefficients are to be maximized and which are to be minimized. A numerical example is included.
Trees, Probabilistic Functional
Authors: Gama Joäo 
Published: September 2014

AbstractDecision Trees are one of most used models for prediction tasks in machine learning, data mining, and statistics. The representation language of a standard decision tree uses splitting tests based on a single input variable and constants at tree leaves. The most recent advances in learning decision trees from data involve the use of splitting tests based on combinations of the input variables and functions at tree leaves. These types of models are denominatedfunctional trees. In this article, we present the issues and actual trends in learning functional trees from data.
Simplex Models
Authors: Browne Michael W. 
Published: September 2014

Abstract
Simplex models are used for the analysis of relationships among variables that can be arranged according to a logical ordering. Most methods for fitting the simplex require a prior knowledge of the ordering of the variables, although there are two‐stage procedures where the ordering is estimated in the first stage and procedures that do not require a prior specification. There are two types, a perfect simplex where no allowance is made for error of measurement and a quasi‐simplex where measurement error is taken into account.
Software Packages for Data Mining
Authors: Haughton Dominique 
Published: September 2014

Abstract

The term
data mining
refers to the identification—within a typically large database—of new, valid, and interesting patterns. While data mining has become most popular in the context of, for example, database marketing, most of the methods under the data mining umbrella have been widely applied in biostatistics. We describe which main applications of data mining have arisen in biostatistics, and introduce the reader to some of the available data mining software packages with a reference to biostatistical needs.
Double Sampling
Authors: R. S. Rao Poduri S. 
Published: September 2014

AbstractDouble sampling is of practical importance for the estimation of population characteristics from sample surveys and for other purposes. This approach for the estimation of stratum weights and the mean of the auxiliary variable for ratio and regression estimators is presented. Its applications for the ratio estimation with subsampling the nonrespondents in a survey, for determining sample sizes in analytical surveys, and for multistage and successive sampling are also described.
Establishment Surveys With Population Survey–Generated Sampling Frames
Authors: Sirken Monroe , Shimizu Iris 
Published: September 2014

AbstractWhen stand‐alone sampling frames list all establishments and size measures, the Hansen–Hurwitz (HH) pps estimator is generally used to estimate the volume of transactions between establishments and households. The network sampling (NS) version of the HH estimator depends on a population‐survey‐generated establishment frame, which lists establishments that have transactions with households in a population sample survey and the number of transactions that establishments have with survey households. The NS estimator is a competitor of the HH estimator whenever stand‐alone frames of good quality are unavailable and, as this article indicates, the NS estimator may be competitive with the HH estimator when stand‐alone and population‐survey‐generated frames are flawless.
Sample Size Adequacy in Surveys
Authors: Penne Michael A. , Levy Paul S. 
Published: September 2014

Abstract
This entry presents concepts involved in determination of sample size adequacy in sample surveys. There is a broad discussion of issues such as stratification, sampling without replacement, finite population corrections, sampling of clusters, sampling in more than one stage, and unequal weighting of observations that have a profound effect on the distribution of estimates and on the sample sizes necessary to construct reliable estimates. We include formulas for determining sample size adequacy for some of the most widely used sample designs and some practical guidelines for statistical consulting on this issue. A set of references to texts and specific articles is included.
Errors in the Measurement of Covariates
Authors: Yi Grace Y. , Cook Richard J. 
Published: September 2014

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.
Multilevel Models
Authors: Goldstein Harvey 
Published: September 2014

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.
Nonparametric Regression ‐ An Introduction
Authors: Hazelton Martin L. 
Published: September 2014

Abstract
Nonparametric methods of regression seek to describe the relationship between an explanatory and response variable while making only the most general type of assumptions about the functional form of this relationship. Techniques for computing nonparametric regression functions include kernel estimation, local polynomial regression, and spline smoothing. In each case, there is a balance to be reached between the smoothness of the regression function and its goodness of fit to the data. Simple nonparametric regression models (with one explanatory variable) can be extended to deal with multiple covariates by employing so‐called generalized additive models.
Loglinear Model
Authors: Carriquiry Alicia L. , Fienberg Stephen E. 
Published: September 2014

AbstractThe general structure of loglinear models emerged in the 1960s as a response to the need for methods to assess departures from independence (and conditional independence) for multiple categorical variables. This entry describes both frequentist and Bayesian statistical methods for the analysis of multiway contingency tables using loglinear models. Special attention is given to the subclass of graphical models, defined in terms of the simultaneous occurrence of one or more conditional independence relationships. The entry also includes a brief guide to computer programs for categorical data analysis.
Proportional Odds Model: Theoretical Background
Authors: McCullagh Peter 
Published: September 2014

AbstractThe proportional odds model is one member of the family of cumulative logistic regression models, designed for studying the effect of covariates on an ordinal responses variable. Relationships with other members of this family are described, emphasizing the effect of response aggregation. The connection with latent variable models, dispersion models, continuation‐ratio models, and log‐linear models is also discussed.
Aalen's Additive Regression Model
Authors: Aalen Odd O. , Scheike Thomas H. 
Published: September 2014

Abstract
In survival analysis, the well‐known Cox regression is based on modeling the hazard rate. This method assumes a proportional hazard structure. Aalen's additive model, however, assumes a linear parametric structure on the hazard rate. This approach may be justified within counting process theory, and produces martingale type estimates. These are used for plotting purposes and shows time‐varying effects of covariates on survival. The plots are easy to compute and have a well‐developed statistical theory. There exist several software packages for carrying out the analysis.