bluuebunny/crossref_metadata_2025_split huggingface dataset:
Epilepsy
Authors: Johnson Anthony L. 
Published: July 2005

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: July 2005

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 It has to some extent been superseded by generalized linear models, notably logistic and Poisson regression, in epidemiology, and with Cox regression in survival analysis in recent years.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.
Ophthalmology
Authors: Marsh Marta J. , Gange Stephen J. 
Published: July 2005

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.
Correlation
Authors: Armitage Peter 
Published: July 2005

Abstract
In a loose sense, two variables are correlated if changes in one tend to be accompanied by changes in the other. More specifically, correlation implies a linear relationship with superimposed random variation. The product–moment correlation coefficient was introduced by Galton and greatly exploited by Karl Pearson. Its square is the proportion of the variance of one variable “explained” by linear regression on a second variable. Standard tests are described in the article. An important extension is to intraclass correlation for measurements on individuals falling into groups, which measures the association between individuals in the same group.
Measurement Scale
Authors: Hand David J. 
Published: July 2005

Abstract
The measured variables analyzed in statistics are reflections of aspects of the system being studied. As such, the relationships between values of the variables must reflect properties of the attributes being studied. Different types of measurement scale, amongst which the most familiar are nominal, ordinal, interval, and ratio, reflect different properties of the attributes. Incorrect conclusions can be drawn if a statistical analysis fails to take appropriate account of this relationship between the objects of study and their numerical representations.
Model, Choice of
Authors: Matthews John N. S. 
Published: July 2005

Abstract
The choice of which statistical model to use in a given application is a complex matter, which draws not only on the technical expertise of the statistician but also on the nature of the application and the purpose of the analysis. Different purposes may require quite different models to be applied to the same dataset. The role of modeling in clinical trials, epidemiology, and general biostatistics is discussed. The relative contribution of expertise both in the area of application and in the relevant statistical issues is explored. The use of sensitivity analyses to ensure that substantive conclusions are not heavily dependent on model assumptions is stressed. A strong theme throughout the article is that the judgment of the biostatistician plays an inescapable role in an activity, which nonstatisticians too often see as wholly objective.
Nonparametric Regression
Authors: Hazelton Martin L. 
Published: July 2005

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.
Residuals
Authors: Atkinson A. C. 
Published: July 2005

AbstractResiduals are the differences between observed responses and those predicted by a model. Any pattern in the residuals is evidence of an inadequate model or of irregularities in the data, such as outliers, and suggests how the model may be improved. Various graphical methods are described, for both the normal regression and the generalized linear models, and are illustrated for the analysis of various data sets.
Bayesian Methods for Contingency Tables
Authors: Albert James H. 
Published: July 2005

AbstractBayesian alternatives to frequentist methods for fundamental problems in categorical data analysis, such as testing independence in two‐way contingency tables and estimating probabilities of categories containing no observations, have been considered for at least four decades. Since the 1980s, Bayesian methods of model selection have also seen extensive and continuing development. These attempt to address the tendencies of frequentist approaches to arrive at overly complex models, and to disregard the effects of variability in model selection on model‐based estimated effects and predictions. This entry briefly reviews several Bayes and empirical Bayes approaches to estimating probabilities of categories, testing equiprobability in a one‐way table, testing independence in a two‐way contingency table, and selecting among generalized linear models, with particular attention to loglinear models, for tables of higher dimension.
Categorizing Continuous Variables
Authors: Altman Douglas G. 
Published: July 2005

AbstractContinuous variables are often converted to categorical variables by grouping values. For descriptive purposes, this can be useful but problems can arise when this is done as part of a data analysis. This article discusses relevant issues with categorization of explanatory variables in regression. Effects of categorization are discussed, such as loss of information, power, and efficiency. This is considered first for categorizing into two groups and then for categorizing into several groups. Rather than group according to observed values with several variables, one might group according to a weighted sum of the variables–a prognostic index.
Contingency Table
Authors: Altham P. M. E. 
Published: July 2005

Abstract
Contingency tables give counts for cross classifications of categorical variables. This article illustrates two‐dimensional tables (two variables). It then discusses the hypothesis of independence and presents chi‐squared tests. An example of quasi‐independence is presented, and small‐sample tests are mentioned. Extensions to multiway contingency tables and the relevant hypotheses for such tables are also discussed. Loglinear models describe the hypotheses, and there are corresponding logistic regression models when one of the variables is a binary response variable.
Logistic Regression, Conditional
Authors: Hosmer David W. , Lemeshow Stanley 
Published: July 2005

Abstract
This section describes the conditional logistic regression model. This model is appropriate when one wishes to model a binary outcome variable with matched or highly stratified data and when one is not interested in matched sets or stratum effects. The resulting fitted model may be used to estimate odds ratios in the same manner as is done with the usual logistic regression model. Examples of estimation and interpretation are given.
Loglinear Model
Authors: Carriquiry Alicia L. , Fienberg Stephen E. 
Published: July 2005

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.
Marginal Models
Authors: Lindsey J. K. 
Published: July 2005

AbstractRepeated observations on the same subject are likely to be correlated. This situation may arise in longitudinal studies or in other cases where observations are clustered in some way. A multivariate model will refer both to the marginal distributions over the whole data and to the conditional distributions for individual subjects or clusters. A marginal model puts primary emphasis on marginal aspects. Relationships between responses and covariates may differ between marginal and conditional models. The issues are discussed with particular reference to paired binary data.
Quasi‐symmetry
Authors: Hatzinger Reinhold 
Published: July 2005

Abstract
In its simplest form, quasi‐symmetry is a model for square contingency tables that states that certain odds ratios are symmetric around the main diagonal. A special case is complete symmetry, by which probabilities themselves are symmetric around the main diagonal. Another special case is quasi‐independence, by which the odds ratios equal 1. This is equivalent to quasi‐symmetry for 3 × 3 tables. This article discusses its multiplicative formula, gives an example, discusses generalizations to higher‐dimensional tables, discusses tests related to complete symmetry and marginal symmetry, and mentions related models.
Trend Test for Counts and Proportions
Authors: Williams Paige L. 
Published: July 2005

AbstractTrend tests evaluate whether counts or proportions Y1, …, Ykincrease (or decrease) with respect to some ordered covariate, x1, …, xk. This section reviews the general development of trend tests, and then describes specific applications for the case of binomial, Poisson, and multinomial random variables. The null and alternative hypotheses are provided, appropriate link functions are discussed, and both asymptotic and exact tests are described. An illustrative example is included for each type of random variable (binomial, Poisson, and multinomial). Choice of scores for trend tests are discussed, as are various extensions, such as incorporation of historical control data, tests for goodness of fit, isotonic regression, stratified data, multiple outcomes, and clustered data.
Psychometrics, Overview
Authors: Sireci Stephen G. , Wainer Howard , Braun Henry 
Published: July 2005

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.
Reduced Rank Regression
Authors: Jackson J. Edward 
Published: July 2005

AbstractProblems can arise inmultiple regressionwhen the predictor variables are highly correlated. These include inflation of standard errors of the regression coefficients and ill‐conditioned covariance matrices of the predictors. Historically,stepwiseregression andridgeregression have been employed in these situations. With the advent of more powerful computers, it has become a common practice to obtain the principal components of the predictors variables and use them as predictors. However, there is no guarantee that the largest components will be good predictors.Latent root regressionis a possible alternative in which the response variable is included in the principal components solution. Two newer techniques arePartial Least Squares RegressionandMaximum Redundancy, both of which use the relationships between the predictors and the responses in obtaining a solution and both can handle the case of multiple responses.
Multivariate Techniques, Robustness
Authors: Krzanowski W. J. 
Published: July 2005

AbstractIn this article, three broad areas of robustness in the multivariate case are considered: inference, ordination and classification, and latent variable models. Also, the authors give an overview of the robustness of the main techniques encountered under each of them. In summary, across all the different areas the broad concerns are the effects that are departures from normality and/or equal dispersion matrices. Departures from normality are relatively unimportant, but lack of equality of dispersion matrices can have much more serious consequences.
Aalen's Additive Regression Model
Authors: Aalen Odd O. , Scheike Thomas H. 
Published: July 2005

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.
Compliance and Survival Analysis
Authors: Goetghebeur Els 
Published: July 2005

Abstract
Partial compliance with assigned treatment can act as both cause and effect of survival experiences in randomized clinical trials. Structural failure time models have been developed for causal inference on the effect of resulting exposure patterns on survival time. In this setting, we review randomization based estimation methods that protect the standard α level. We also point to alternative methods relying on the assumption of no unmeasured time‐dependent confounders.
Discrete Survival‐Time Models
Authors: Fahrmeir Ludwig 
Published: July 2005

AbstractSurvival times are rarely intrinsically discrete (an important counterexample being time to pregnancy, which is naturally measured in menstrual cycles). If times are recorded in intervals, grouped survival times methods may be relevant, but it is alternatively sometimes preferable to consider time as discrete. This leads to various forms of generalized linear models, notably repeated logistic regressions or discrete proportional hazards (complementary log–log link functions). Smoothing methods are considered.Generalizations to competing risks and to frailty models have also been studied.
Excess Mortality
Authors: Væth Michael 
Published: July 2005

Abstract
The term excess mortality is used to describe the additional hazard rate in a study population relative to that seen in a reference population with the same sex and age distributions. A formal model is introduced and a nonparametric estimate of the integrated excess mortality rate is given. As a by‐product, an estimate of the expected survival function is also obtained. For the special case with a constant excess mortality rate, simple explicit moment estimate is also available. The methodology can be generalized to both parametric and nonparametric regression models.
Explained Variation Measures in Survival Analysis
Authors: Graf Erika 
Published: July 2005

Abstract

In linear regression, the proportion of explained variation,
R
2
, is a familiar measure used to quantify the predictive value of a model including covariates compared to the null model of no covariate effect. Similar measures have been proposed for survival analysis where the situation is typically complicated by the presence of censored observations. In addition, squared error may not be felt to be an adequate loss function in this context. We introduce the concept of explained variation in the survival setting and illustrate it by selected measures.
Frailty
Authors: Hougaard Philip 
Published: July 2005

Abstract
A frailty model is a random effects model for survival data or other time‐to‐event data. Conceptually, frailty models are similar to the normal distribution mixed models, but they are dedicated to the survival data case, where observations may be censored and where updating is natural as the events happen over time. The frailty is a common random term and is assumed to enter multiplicatively in the hazard function. The gamma distribution is the most commonly used distribution for the frailty, but other distributions may also be useful. Over the last 20 years, theory as well as applications have developed so that by now the frailty models have almost become standard models.
Kaplan–Meier Estimator
Authors: Borgan Ørnulf 
Published: July 2005

AbstractIt is described how the Kaplan–Meier estimator can be used to provide a nonparametric estimate of the survival distribution from right‐ censored data. Problems with estimating the survival distribution with left‐truncated data are considered, and it is pointed out that for such data, it may be more useful to estimate conditional survival distributions. Estimation of the median and mean survival times is discussed, and it is described how the Kaplan–Meier estimator may be derived by the redistribute‐to‐the‐right algorithm and how it is the unique self‐consistent estimator for right‐censored survival data. It is shown how the Kaplan–Meier estimator can be given as the product‐integral of the Nelson–Aalen estimator for the cumulative hazard rate, and it is indicated how the product‐integral formulation is helpful in the study the of statistical properties of the estimator. Finally, the construction of confidence bands for the survival distribution is considered.
Smoothing Hazard Rates
Authors: Wang Jane‐Ling 
Published: July 2005

AbstractThe nonparametric approach to estimate hazard rates for lifetime data is flexible, model‐free, and data‐driven. No shape assumption is imposed other than that the hazard function is a smooth function. Such an approach typically involves smoothing of an initial hazard estimate, with arbitrary choice of smoother. We describe methods for grouped lifetime data observed at certain time intervals and for continuously observed lifetime data. There are some intrinsic differences between the smoothing approaches for these two types of data. More specifically, smoothing an initial hazard estimate based on the life table is adopted for grouped lifetime data; while for continuous data, smoothing is employed to increments of the Nelson–Aalen cumulative hazard estimate aiming at the derivative of the cumulative hazard function. A few nonparametric hazard regression methods are also discussed.
Staggered Entry
Authors: Slud E. V. 
Published: July 2005

AbstractClinical trials and epidemiologic studies, which measure duration until some endpoint of interest, often follow subjects who may arrive at random or “staggered” times and may remain under observation until occurrence either of the endpoint or censoring/withdrawal. An important theme of statistical methodological development over the past three decades has been to understand how to handle such data. Building on earlier ideas (Lexis Diagrams, life tables), the treatment of staggered data has required sophisticated mathematical treatment (counting processes, martingales, repeated significance tests), but has practical impact in the interim analysis and early stopping of survival experiments.
Baseline Adjustment in Longitudinal Studies
Authors: Senn Stephen 
Published: July 2005

Abstract
A baseline variable may be a demographic characteristic such as sex, a pretreatment observation on the variable to be used for measuring outcome, or a pretreatment observation on a variable correlated with the outcome. Adjustment by the analysis of covariance is preferable to the analysis of changes. Topics discussed include testing for baseline balance, adjustment for baselines taken after start of treatment, cut‐off designs, cohort studies, and uncontrolled studies.
Correspondence Analysis of Longitudinal Data
Authors: Van Der Heijden Peter G. M. 
Published: July 2005

Abstract
Correspondence analysis is an exploratory tool for the analysis of associations between categorical variables, the results of which may be displayed graphically. For longitudinal data with two time points, an analysis of the transition matrix (showing the relative frequencies for pairs of categories) provides insight into the structure of departures from independence in the transitions. Transitions between more than two time points can be studied simultaneously. Examples are provided.
Generalized Estimating Equations
Authors: Pickles Andrew 
Published: July 2005

AbstractLongitudinal data occurs frequently in medical studies, particularly in clinical trials. Often, the response variable is nonnormal, for example, it may be a binary variable, that is, “improved” or “not improved”. The approach to the analysis of such data discussed in this entry is the use of generalized estimating equations, which, while making weaker distributional assumptions than those required for a fully likelihood‐based model, maintains the properties of consistency and asymptotic normality of parameter estimates.
Longitudinal Data Analysis, Overview
Authors: Dunn Graham , Pickles Andrew 
Published: July 2005

AbstractThe article reviews several approaches to the analysis of data containing repeated or serial measurements, including data arising from both observational studies and experiments. Examples include fitting growth curves and other forms of trend analysis. Methods for the analysis of quantitative responses include adaptations of analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), and analysis of covariance (ANCOVA) models allowing for autocorrelation. Structural equation models involving latent variables are also explained briefly, as are random effects models. Methods for counts and categorical responses include loglinear models, latent class models, random effects models, and marginal models involving generalized estimating equations.
Non‐Fourier Waveforms
Authors: McCoy E. J. 
Published: July 2005

Abstract
The discrete Fourier transform (DFT), which gives us information about the “frequency” domain characteristics of data, is probably the most ubiquitous orthogonal data transform, and uses basis functions made up of sines and cosines at different frequencies. Often, we have to analyze signals whose frequency behavior changes over time. In such cases, we can utilize transforms with non‐Fourier basis functions, which can better represent nonstationary behavior.