bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multivariate adaptive splines for analysing longitudinal data
Authors: Zhang Heping 
Published: September 2014

AbstractIn a longitudinal study, the outcome variable is measured repeatedly over time, together with covariates that may or may not vary over time. This article describes MASAL — a nonparametric method for analyzing and exploring longitudinal and growth curve data. It is based on a high‐dimensional smoothing technique, accommodates time‐varying covariates, and allows unrestricted interactions among covariates and between time and covariates.
Nonignorable drop‐out in Longitudinal studies
Authors: Fitzmaurice Garrett 
Published: September 2014

Abstract
Longitudinal studies are very common in medical research and many suffer from patients not completing the intended number of visits, that is, some patients drop out of the study prematurely. Such dropouts can be classified into different types depending on how their probability of dropping out relates to observed and missing values of the response. In this article, nonignorable dropouts for which the probability of dropping out depends on unrecorded values of the response are considered.
Spline Function: Introduction
Authors: Rao T. Subba 
Published: September 2014

Abstract

Spline functions are an approach to the smoothing of bivariate data (a spline is a term for a flexible strip of metal or rubber used by a draftsman to draw curves). Splines are polynomials within intervals of the
x
‐variable that are connected across different values of
x
. In this article, the theory behind such functions is described.
Vector Field Plot
Authors: Boker Steven M. , McArdle John J. 
Published: September 2014

Abstract

Longitudinal data are often analyzed using a series of dynamic simultaneous equations. A
slope field
plots the expected change in one variable with respect to another variable for ranges of each of these variables. A
vector field
plots the expected changes in variables with respect to each other and with respect to time. A
statistical slope field
or a
statistical vector field
offers a similar display, but are exploratory graphs generated from raw data and include information about the variations due to sampling.
Summary Measures Analysis of Longitudinal Data
Authors: S. Matthews John N. 
Published: September 2014

AbstractThere are many methods for the analysis of longitudinal data and the method of summary measures is one of the simplest and most effective. The observations on each unit are combined into a summary measure, which measures a relevant aspect of the response over time. In this way, the data from each unit is reduced to a single value and these can, within limits, be analyzed as the response of the unit. For example, two groups of individuals might be compared by performing at‐test on the summary measures. Summary measures can be whatever adequately captures the essential features of the response over time and more than one summary can be used if more than one aspect of the response is of interest. Typical summaries are the area under the curve, the slope of a regression line or the time to the maximum response. Some difficulties arise with missing data and irregular observations but these problems are generally not more severe for this approach than for other methods that analyze longitudinal data.
Time‐By‐Time Analysis of Longitudinal Data
Authors: S. Matthews John N. 
Published: September 2014

Abstract
Groups of subjects measured longitudinally over time are often analyzed by comparing the groups separately at each time the outcome was observed. Graphs of profiles of group means, with attendant error bars (of various kinds) are often displayed. This approach is flawed because it ignores the structure of the data, seldom answers a question of practical interest, and is difficult to interpret.
Communicable Diseases
Authors: Farrington C. P. 
Published: September 2014

AbstractMany of the statistical methods used in communicable diseases are similar to those used in other areas of epidemiology. Clinical trials, risk‐based epidemiological studies, and survival analysis for example account for much of the infectious disease statistician's toolkit. In addition, however, some more specialized techniques are required. This article aims to give a flavor of the range of statistical methods used in the field of infectious disease epidemiology. Only the simpler versions of the techniques are covered, with references to more complex methods. For common childhood infections, survival models are reinterpreted in the context of epidemic transmission models to estimate hazards of infection, and to derive epidemiological parameters such as the basic reproduction number. Basic back‐calculation methods are described for estimating the incidence of infections with long incubation periods such as AIDS. Simple stochastic models such as branching processes and chain binomial models for use with outbreak data are outlined. Methods are described for estimating latency and infectious periods. Cluster detection methods are also reviewed in the context of identifying infectiousness and detecting outbreaks. Statistical methods for evaluating vaccine efficacy and safety are discussed. Finally, some statistical topics relating to diagnostic testing for infectious diseases using laboratory techniques are touched upon.
Epilepsy
Authors: Johnson Anthony L. 
Published: September 2014

Abstract
Epilepsy is a common neurological disorder that affects about one in a hundred people. It is one of the oldest documented diseases that has multiple causes and onset at any age. This article provides a brief overview of epilepsy and its treatment, together with a summary of the types of investigation that have been employed to improve greater understanding of this disease. A few landmark studies in epilepsy are identified.
Oncology
Authors: Duffy Stephen W. 
Published: September 2014

AbstractHistorically, the most influential development in Biostatistics for cancer research is the Mantel–Haenszel estimate. This has contributed massively not only to relative risk estimation in cancer epidemiology, but also to survival analysis in clinical trials of cancer therapies. It has to some extent been superseded by generalized linear models, notably logistic and Poisson regression, in epidemiology, and with Cox regression in survival analysis in recent years. The likely biostatistical developments for the future are likely to be aimed at evaluation of interventions for prevention, early detection, and genetic oncology. Methods for rapid evaluation and modeling of the disease process will be of some relevance. Bayesian estimation techniques such as Markov Chain Monte Carlo are likely to play a major part.
Reproduction
Authors: M. Farley Timothy M. 
Published: September 2014

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.
Rheumatology
Authors: Farewell Vern T. , Gladman D. D. 
Published: September 2014

Abstract
Rheumatology deals with clinical disorders, which involve the musculoskeletal system. A survey of the main rheumatic diseases is given along with a historical overview. Current treatments are discussed. Methodological issues arising in rheumatological research are highlighted, including the choice of outcome measures, multiplicity and longitudinal data analysis.
Transfusion Medicine
Authors: Casbard Angela 
Published: September 2014

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.
Psychometrics, Overview
Authors: Sireci Stephen G. , Wainer Howard , Braun Henry 
Published: September 2014

AbstractPsychometrics includes educational testing, measurement of attitudes and perceptions, personality testing, opinion surveys, health inventories, and other forms of psychological measurement. Psychometric techniques created for measuring the human mind have also proven useful in biometrics and other fields that deal with measuring the intangible. This article is a comprehensive summary of the techniques for psychometric analysis including reliability, generalizability, and validity assessments. The article also presents the evolution of psychometric theory, popular measurement models, and a snapshot of new directions in the field.
Quartimax Rotation
Authors: Jackson J. Edward 
Published: September 2014

Abstract

Quartimax rotation
is a form of
orthogonal rotation
used to transform vectors associated with principal component analysis or factor analysis to
simple structure
. It is a special case of
orthomax rotation
, which maximizes the sums of squares of the coefficients across the resultant vectors for each of the original variables, as opposed to
varimax
, which maximizes the sums of squares of the coefficients within each of the resultant vectors. A numerical example is included to show a case where quartimax might be preferred to the commonly used varimax.
Rotation of Axes
Authors: Jackson J. Edward 
Published: September 2014

Abstract

In both
principal component analysis
and
factor analysis
, it is sometimes desirable to perform a linear transformation of the original components or factors. This operation is performed to arrive at
simple structure
where the coefficients of the vectors related to these new transformed variables are either as large as possible or close to zero. These new coefficients may be used either to aid in the interpretation of the original components or factors, or to cluster the original variables. This linear transformation amounts to a rotation of the axes related to the components or factors. The rotation may be
orthogonal
in which case the resultant new set of vectors are still at right angles to each other or
oblique
in which they are not. Loss of orthogonality may result in better interpretation of the original vector sets or clustering of the original variables. Several numerical examples are given to illustrate these procedures including one where rotation is not appropriate.
Classification, Overview
Authors: Gower John C. 
Published: September 2014

Abstract
Classification is concerned with (i) constructing classes, and (ii) assigning objects to one of several predetermined classes. Classes may be constructed to balance measures of their hopefully large external differences and hopefully small internal differences, or so that subsequent assignment is optimal. Objectives for assigning to classes may be to minimize errors of misclassification and/or to minimize costs. Both (i) and (ii) exist in probabilistic and nonprobabilistic forms. The nature of the things being classified is crucial. Are they single items representative of many identical items, or representative of a population of similar items characterized by a probability distribution? Are they initially totally unstructured, or are they already explicitly, or implicitly, assigned to some previous classification? Class‐construction includes the multivariate mixture problem (probabilistic), partition problems and many methods for hierarchical classification (nonprobabilistic). Assignment problems include discriminant analysis (probabilistic), diagnostic keys and tables (nonprobabilistic), and expert systems for diagnosis (hybrid).
Cluster Analysis, Variables
Authors: B. D'Agostino Ralph , Russell Heidy K. 
Published: September 2014

Abstract
Cluster analysis involves grouping objects, subjects or variables, with similar characteristics into groups. Similarity or dissimilarity of objects is measured by a particular index of association. The focus here is on clustering of variables instead of subjects. Types of methods that cluster variables based on correlation structure of variables or factorial structure of variables are considered, where factorial structure refers to a structure obtained from principal component analysis or factor analysis. The approaches described for cluster analysis uses various indices of association such as factor loadings, correlation matrix, and cosine matrix. Examples of these are shown and the advantages and disadvantages of each approach are discussed.
G
ram–
S
chmidt Process
Authors: Dukes Kimberly A. 
Published: September 2014

Abstract

The Gram–Schmidt process is a technique for constructing an orthogonal basis from a basis spanning the same subspace. The technique is used in a number of multivariate applications including battery reduction analyses and multiple regression applications. In the former, the goal is to reduce the number of variables at the same time explaining as much variance in the original variables as possible. In the latter, the goal is to reduce the
n
‐dimensional space (observations) to a
p
‐dimensional space (independent variables).
Multiple Endpoints,PLevel Procedures
Authors: B. D'Agostino Ralph , Russell Heidy K. 
Published: September 2014

AbstractIn clinical trials, multiple endpoints are often used to investigate a treatment effect. There has been substantial debate in determining the appropriate approach to analyze these multiple endpoints. Global procedures provide a single statistic and are used to compare treatments in an overall sense but do not look at how the treatments affect each endpoint individually.Endpoint‐specific procedures are intended to be used to identify the significance of the individual endpoints. These procedures are also calledPlevel procedures because they usually involve making statistical decisions on the basis of thePlevel, resulting from examining the endpoints separately.Plevel procedures can be used in the primary analyses or as a second‐step analyses in conjunction with multivariate global tests. There are three categories of endpoint specific methods, Bonferroni‐type tests, normal‐based tests and resampling procedures.
Multivariate Classification Rules: Calibration and Discrimination
Authors: Schmid Christopher H. , Griffith John L. 
Published: September 2014

AbstractThis article presents a number of measures of discrimination and calibration, along with graphical representations of calibration and discrimination assessment. It emphasizes multivariate classification rules for models, where the classification is into one of two possible states, and also discusses extensions to multistate classifications. The c‐index and the Hosmer–Lemeshow χ2statistic are the most widely used measures of discrimination and calibration.
Multivariate Graphics
Authors: Carr Daniel B. 
Published: September 2014

Abstract
This extensive tour of multivariate graphics leads into methods that can be applied to biostatistics. Basic design issues and techniques, such as smoothing and density estimation, are considered. Issues of choice of color, depth, and structure are discussed. Multivariate representations basically fall into a few classes: glyph plots, linked plots, nested plots, conditioned plots, geometric section plots, series plots, and composite plots. Each of these classes is described in detail. Once the graphics are created, important considerations of viewing the graphics in terms of dimensionality, cognostics, and projection pursuit arise. Ways of handling these issues by starting from low‐dimensional plots and seeing what happens through cognostics or projection pursuit, and sectioning are pursued. The ongoing challenges of producing graphics from massive datasets is also considered.
Multivariate Normal Distribution: Overview
Authors: Jensen D. R. 
Published: September 2014

Abstract
Biometric data typically entail observations on multiple characteristics for each experimental subject. Multivariate normal distributions are often central to the modeling and analysis of such data. Empirical evidence often points towards the normality of multivariate data. Biometric measurements, especially, may emerge as the result of many small increments due to heredity and environment, so that the approximate multivariate normality of such data rests on multidimensional central limit theory. In addition, many normal‐theory procedures, both univariate and multivariate, remain exact for many nonnormal multivariate distributions exhibiting suitable symmetries.
Multivariate Weibull Distribution: Theory
Authors: Jensen D. R. 
Published: September 2014

Abstract
The univariate Weibull distribution is frequently used in accelerated life testing and failure time models. It reduces to the exponential distribution when the shape parameter is equal to one. Multivariate Weibull distributions encompass distributions exhibiting Weibull characteristics and relate to multivariate exponential distributions of various types. Applications include reliability analysis, survival analysis, and repeated failure time analysis.
Oblique Rotation
Authors: Jackson J. Edward 
Published: September 2014

Abstract

Any rotation of vectors associated with
principal components analysis
or
factor analysis
designed to produce new set approximating
simple structure
is said to be
oblique
if the resultant set of vectors are not at right angles to each other. Oblique rotations may obtain a better approximation to simple structure but with the loss of orthogonality. Some of these rotations are obtained by a general procedure called
Oblimin rotation
but some of the more widely used rotations such as
Orthoblique, Promax
and
Optres
do not use this form but instead are two‐ and three‐stage procedures, usually using orthogonal rotations as the first stage.
Orthogonal Rotation
Authors: Jackson J. Edward 
Published: September 2014

Abstract

Any rotation of vectors associated with principal components analysis or factor analysis designed to produce a new set of approximating
simple structures
is said to be
orthogonal
if the resultant set of vectors are still at right angles to each other. Most such rotations are obtained by a procedure called
Orthomax rotation
whose general expression is given. Part of the expression is multiplied by arbitrary constant changes which will produce new vectors with different properties. The two most common orthogonal rotations are
Varimax
and
Quartimax
. An alternate procedure,
Minimum Entropy
, is also presented.
Primary Factors
Authors: B. D'Agostino Ralph , Russell Heidy K. 
Published: September 2014

Abstract
One of the aims of factor analysis is to display the configuration of variables in as simple a manner as possible. There are several techniques for achieving this. Primary axes are the factor axes that are rotated to a good fit to the configurations of the variables. They are the unit vectors that lie in the intersection of the primary factors, which represent the ideal variables, each of which measures only one common factor with no unique factors. Reference vectors are defined as the axes that are perpendicular to the primary factors. Various techniques are illustrated for obtaining projections of variable vectors onto the different sets of axes. These are extremely important for interpretation of results.
P
romax Rotation
Authors: Jackson J. Edward 
Published: September 2014

Abstract

Orthoblique rotation
is a two‐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 usually be an orthogonal rotation such as
Varimax
. The nature of the second stage will depend to some extent on the degree of obliqueness desired. A numerical example is included with two choices for the second stage.
Structural Equation Models including Historical Origins
Authors: Bollen Kenneth A. 
Published: September 2014

Abstract
Structural equation models refer to general statistical procedures for multiequation systems that include continuous latent variables, multiple indicators of concepts, errors of measurement, errors in equations, and observed variables. An analysis that uses structural equation models has several components. These include (a) model specification, (b) the implied moment matrix, (c) identification, (d) estimation, (e) model fit, and (f) respecification. Historical origins of structural equation models are also described.
Adaptive Sampling
Authors: F. Seber George A. , Salehi M. Mohammad 
Published: September 2014

AbstractAnimal populations are often highly clustered. For example, fish can form large, widely scattered schools with few fish in between. Even rare species of animals may form small groups that are hard to find. Applying standard sampling methods such as simple random sampling of plots to such a population could yield little information, with most of the plots being empty. Adaptive sampling is based on the simple idea that when some animals are located on a sample plot, the neighboring plots (and possibly their neighbors as well) are added to the sample; in this way, the whole cluster can be sampled. The scheme lends itself to a variety of modifications, for example, sampling with and without replacement, sequential and inverse sampling, stratified sampling, two‐stage sampling, and so‐called adaptive allocation, as well as extensions to multivariate data and incomplete detectability.
Composite Estimators
Authors: Schaible Wes 
Published: September 2014

AbstractA composite estimator is a weighted combination of two (or more) component estimators that, with appropriate weights, will have a mean square error smaller than that of either component estimator. Early applications of composite estimators occurred in conjunction with continuing surveys with rotational designs. These designs allow the production of two distinct component estimators for the current time period—one using the overlap data and the estimate for the previous time period, and the second using only the data from the current time period. A second type of application occurs when the sample size in a particular domain is too small to produce a standard, direct estimate of sufficient reliability. In such cases, a second indirect estimate can be made and a weighted combination of the direct and indirect estimates used to make a composite estimate.
Interpenetrating Samples
Authors: Ghosh Subir 
Published: September 2014

AbstractThis article presents an overview of interpenetrating sampling (IPS), also known as interpenetrating subsampling and replicated sampling. IPS was introduced in the pioneering contribution of P.C. Mahalanobis. It was originally proposed in assessing the nonsampling errors as the so‐called “interviewer errors”. IPS provides a quick, simple, and effective way of estimating the variance of an estimator even in a complex survey. In fact, IPS is the foundation of modern resampling methods like Jackknife, bootstrap, and replication methods. In IPS, three basic principles of experimental designs, namely, randomization, replication, and local control, are used. IPS is used extensively not only in agriculture, but also in social sciences, demography, epidemiology, public health, and many other fields.