bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multinomial Distribution: Overview
Authors: Zelterman Daniel 
Published: September 2014

Abstract

The multinomial distribution is the generalization of the binomial to more than two dichotomous categorical outcomes. This multivariate discrete distribution describes the joint behavior of frequencies of each of
k
possible outcomes following
n
independent trials. The
k
individual frequencies follow a binomial distribution and are mutually negatively correlated. The probabilities of each of the
k
outcomes are usually estimated using loglinear models or Bayesian methods. Goodness‐of‐fit and exact tests assess the adequacy of the model to explain the empirical data. These topics are illustrated with an example from genetics.
Ordered Categorical Data
Authors: Becker Mark P. 
Published: September 2014

AbstractMany categorical variables have a natural ordering of the categories. Special methods are available for such data that are more powerful and more parsimonious than methods that ignore the ordering. This article discusses ways of modeling association for ordered categorical data and gives examples of them. These include loglinear models, related association models for two‐way tables that have row and column effects, and logit regression models for an ordinal response variable and a set of explanatory variables. The best‐known logit model, called a proportional odds model, uses the same effect parameters for each set of logits of cumulative probabilities for the ordered categorical response variable.
Poisson Regression: Biostatistical Applications
Authors: H. Seeber Gilg U. 
Published: September 2014

AbstractPoisson regression models are regression models that assume a Poisson distribution for a count response variable. Usually they have loglinear form, modeling the log of the mean in terms of the explanatory variables. Such models are special cases of generalized linear models. A common application is in multiplicative modeling of rates, for which there are connections with Poisson process models and proportional hazards models. This article introduces these types of models, shows how to fit them using maximum likelihood, and discusses checking of model fit.
Power Divergence Methods
Authors: Imrey Peter B. 
Published: September 2014

AbstractPower divergences are measures of discrepancy between two distributions, with roots in information theory. For two multinomials with common support, the power divergence of order λ is based on the weighted sum ∑ipj(pj/qj)λof exponentiated ratios of the probabilitiespj,qjrespectively assigned to thejthpossible outcome. Note that these measures are directional rather than symmetric. The limiting logarithmic forms as λ → 0 or −1 are also considered power divergences. Asymptotically chi‐square goodness‐of‐fit statistics for count data are generally multiples of power divergences, and hence, general theoretical results and computational approaches for the power‐divergence family apply to these statistics and to estimators that minimize them. In particular, comparisons within this framework of tests and estimators based on the Pearson, Neyman, Freeman–Tukey, likelihood ratio, and discrimination information chi‐square statistics have shed considerable light on their absolute and relative performance in small sample settings, sparse contingency tables, and in the presence of outlying or zero counts. The “Cressie–Read” statistic, for which λ = 2/3, has been suggested as a possible replacement for the classical test statistics due to its good performance from several perspectives. This article defines the power divergence class of goodness‐of‐fit statistics and associated estimators, summarizes some basic comparative results, and briefly surveys the increasing use of power divergence in various statistical estimation settings.
Square Contingency Table
Authors: Lipsitz Stuart R. 
Published: September 2014

AbstractThis article discusses the analysis of contingency tables for which the number of rows equals the number of columns. Three types of models for such tables are discussed: marginal models for comparing margins of the table, loglinear models for describing the joint distribution and the association patterns, and conditional models that consider the distribution of one of the categorical variables conditional on the outcome for the other one. Other topics discussed include testing marginal homogeneity and symmetry, the models of quasi‐symmetry and quasi‐independence, and modeling agreement.
S
tat
X
act
Authors: Mehta Cyrus R. , Patel Nitin R. , Senchaudhuri Pralay , Corcoran Christopher D. 
Published: September 2014

Abstract

StatXact is a software package providing exact
P
values and confidence limits for categorical and nonparametric data, including contingency tables. Computations are based on permutation distributions of appropriate statistics. An associated package, LogXact, deals with binary data in the presence of covariates using a logistic regression model.
Computer‐Aided Diagnosis
Authors: Weiner M. G. , Pifer Eric , Williams Sankey V. 
Published: September 2014

AbstractA provider's ability to make correct decisions regarding patient care is predicated on the correct identification of a patient's diagnoses. However, the process of developing diagnostic certainty remains a challenging task despite an increasingly sophisticated array of available diagnostic modalities and techniques. Clinicians need support to integrate a broad range of findings from these tools along with a patient's symptoms and signs. This chapter discusses the evolution and utility of computer‐aided diagnostic decision support systems including recent developments in neural networks, microarray technology, and syndromic surveillance.
Normal Clinical Values, Design of a Study
Authors: Harris Eugene K. 
Published: September 2014

AbstractThis entry describes the estimation of normal ranges for clinical variables. A key issue is the identification of the appropriately healthy segment of the general population, and ways in which it can be sampled without bias. Techniques such as convenience sampling often do involve potential bias. Topics such as exclusion and partitioning criteria for the sampling are described. Sample size determination for establishing normal limits is reviewed.
Normal Clinical Values, Reference Intervals for
Authors: Royston Patrick 
Published: September 2014

AbstractThis entry describes the estimation of reference intervals for clinical variables. The appropriateness of various healthy populations to establish such intervals is discussed. The design and sample size of reference interval studies are reviewed. Estimation of the intervals may be done using nonparametric or parametric methods, and may be carried out in subgroups or take covariates into account. Goodness of fit to the data and identification of outliers are touched upon. Estimation of reference intervals is usually iterative, and specialized software to carry out the calculations is identified.
Utility in Health Studies
Authors: Torrance George W. 
Published: September 2014

Abstract
Utility is a quantitative expression of strength of preference. The more something is preferred, the greater is its utility. Formal utility theory for decision making under uncertainty was defined by von Neumann & Morgenstern. Utilities in their theory are measured using the standard gamble. Alternatively, time trade‐off and visual analog scales are used to measure preferences, and these also are sometimes called utilities. Utilities are an integrative measure of health‐related quality of life. Utilities, representing quality of life can be combined with quantity of life to form quality‐adjusted life years. These, in turn, are used in cost–utility analyses. For most clinical studies, the simplest and the preferred way to measure utilities is to use one of the multiattribute health status classification systems that include a utility scoring formula; for example, EuroQol EQ‐5D, Health Utilities Index, or Quality of Well‐Being Scale.
Intention to Treat Analysis: Basic
Authors: Ellenberg Jonas H. 
Published: September 2014

AbstractIn a randomized clinical trial design for the comparison of treatments, subjects are randomly assigned to different treatments. The intention‐to‐treat principle requires that any comparison of the treatments be based upon comparison of all patients in the treatment groups to which they were randomly assigned. This approach maintains the benefits of randomization, one of which is that the characteristics of the group of patients receiving the different treatments should be roughly equivalent at the onset of the trial.Several conditions can influence the actual versus intended protocol under which subjects are studied including: nonadherence to the assigned treatment regimen, or termination of participation prior to outcome measurement. Investigators may attempt to compare groups defined by factors that might be influenced by the treatments under test, for example, the comparison of only those patients in the trial that actually complied with the prescribed treatments. Any observed differences among treatment groups constructed in this manner may be due not to treatment, but to factors associated with compliance.
Drug Approval and Regulation
Authors: Lewis John A. , Jones David R. 
Published: September 2014

Abstract
Before it can be marketed, any new medicinal product generally requires the regulatory approval of the appropriate governmental agency. This article describes the contribution of statistics and statisticians to the regulatory process. It addresses the role of statisticians within regulatory agencies worldwide. It provides an explanation of the various ways in which statistical assessment is carried out within the major regulatory agencies. It concludes by considering the statistical aspects of the regulatory application written by the applicant company to support their claims for their medicinal product.
Number Needed to Treat: Overview
Authors: Bender Ralf 
Published: September 2014

AbstractThe number needed to treat (NNT) has gained much attention in the past years as a useful way of reporting the results of randomized clinical trials. Defined as the reciprocal of the absolute risk reduction (ARR), NNT is the average number of patients needed to be treated to prevent an adverse outcome in one additional patient. This measure has been applied to a number of other research areas involving the development of more sophisticated techniques to calculate and interpret NNTs in medical research. The most important application areas are screening studies, public health research, epidemiology (case–control and cohort studies), crossover studies, studies measuring continuous and survival time data, risk–benefit analyses, and systematic reviews. An overview of the recent developments regarding NNT is given as well as a discussion of the advantages, drawbacks, and usefulness of the NNT statistic as a reporting tool.
Postmarketing Surveillance of New Drugs and Assessment of Risk
Authors: Anello C. , O'Neill R. 
Published: September 2014

AbstractThe goal of this article is to review the role, rationale, objectives, and design of quantitative methods of surveillance strategies developed for drug safety assessment over the past 30 years. Emphasis is placed on the quantitative methods that have been developed in response to regulatory requirements to monitor adverse drug event data for the purpose of assessing potential risk.
Cooperative Cancer Trials
Authors: Rubinstein Lawrence V. , Ungerleider Richard S. 
Published: September 2014

AbstractThe clinical trials cooperative group program was established by the US National Cancer Institute in 1955. The number of groups increased to 18 by 1985 and then decreased to 11. This article describes the work of the groups since the mid‐1980s. It covers Phase I, II, and III studies, in a range of histologic groups, including breast, colorectal, lung, ovary, central nervous system, and prostate. Developments include intergroup studies, formalized interim monitoring systems, and increased emphasis on pediatric cancer and cancer prevention and control. Various methodologic developments are summarized.
Cooperative Heart Disease Trials
Authors: Geller Nancy L. , Friedman Lawrence M. 
Published: September 2014

AbstractThis review of multicenter trials in heart disease sponsored by the United States National Heart, Lung, and Blood Institute illustrates some design and biostatistical advances that selected trials have developed or used effectively to answer important public health questions. Many of the design, organizational characteristics and methods for monitoring modern clinical trials were implemented as a result of the Greenberg Report completed in 1967 and were first used in the Coronary Drug Project during the 1960s. While more recent studies employed more efficient organizations, newer designs, and newer monitoring guidelines, the genesis of multicenter clinical trial practice in heart disease was outlined in the 1960s and that early structure has endured the test of time.
Salk Vaccine
Authors: Meier Paul , Smith Rebecca Pringle 
Published: September 2014

Abstract
The Salk vaccine was a killed‐virus vaccine against poliomyelitis, an uncommon but dangerous disease during the early twentieth century. In 1954, over one million children in the United States took part in a study of the effectiveness of the vaccine. Over 700 000 entered a randomized double‐blind study. Over a million were in a study in which second‐grade children were offered the vaccine and first‐ and third‐ grade children were observed as controls. In both forms of study, the incidence was reduced by about half in the groups that received the vaccine. The problems of design, execution, and interpretation are described in some detail.
UniversityGroupDiabetesProgram (UGDP)
Authors: Knatterud Genell L. 
Published: September 2014

AbstractThe University Group Diabetes Program (UGDP) was one of the first large‐scale cooperative clinical trials designed and implemented in the United States. The three objectives of the program were (1) evaluation of the effects on the development of vascular complications in adult‐onset noninsulin dependent diabetes of the different hypoglycemic treatments accepted in clinical practice in late 1950s and early 1960s (diet, insulin, and oral agents); (2) study of the natural history of vascular disease in adult‐onset diabetics; and (3) development of methods appropriate for the design and conduct of cooperative clinical trials.The study provided little evidence that insulin treatment was any better than diet alone in altering the course of vascular complications in stable adult‐onset diabetes. This was true whether insulin was given in a fixed dose or adjusted to maintain blood glucose within certain levels.Tolbutamide therapy was discontinued prior to the end of follow‐up for patients in the diet and insulin groups, when it became apparent that there was an excess cardiovascular mortality for patients in the tolbutamide treatment group compared to patients assigned to placebo or diet alone. About two years after the decision was made concerning tolbutamide, patients assigned to phenformin treatment were also discontinued when the observed mortality for all causes and from cardiovascular causes was higher than mortality in any of the other treatment groups.Since the last UGDP major report was published in 1992, there has been little evidence provided to indicate benefit of therapy for the prevention of macrovascular diabetic complications in adult‐onset diabetes. The methods and procedures used in the UGDP Coordinating Center provided a framework for standards of operations for future Coordinating Centers of multicenter clinical trials.
Computer‐Assisted Interviewing
Authors: Brinkley Eden , Finlay Elizabeth , Wensing Fred 
Published: September 2014

Abstract
Computer‐assisted interviewing (CAI) refers to the use of a computer to aid the interview process in survey data collection. The computer generally presents the question text on the screen, along with allowable response categories, and the interviewer or respondent records the answers directly into the computer. The computer can also be used to undertake various forms of processing at the time of the interview, and can facilitate electronic data transfer. The advantages and disadvantages of CAI relative to paper and pencil interviewing (PAPI) are discussed briefly.
Database Systems
Authors: Walker N. , Lawrence Catherine 
Published: September 2014

AbstractThe word “database” is used here in two overlapping ways to refer to a collection of related data and its storage method, or to programming system software used to organize and integrate a collection of related data. It is management, access, and control (including efficiency and recoverability), and not necessarily size, which distinguishes a database from a data set. This article concentrates on database software: choosing a database management system (DBMS), designing databases, object‐oriented approaches, and closes with an overview of resources that might be helpful.
Graphical Displays
Authors: Martin Michael A. , Welsh A. H. 
Published: September 2014

AbstractGraphical methods are fundamental tools of modern data analysis. Graphics are useful for communicating complex information quickly and easily, and are thus excellent tools for describing data and for providing direction in data analysis. They are essential tools for building statistical models from data, and for assessing model adequacy. This article surveys the use of statistical graphics in describing data and for statistical modeling, including models for relating variables, for spatiotemporal dependence, and for survival data. Graphics are also critical tools for visualizing and analyzing high‐dimensional data. The article illustrates the use of graphics in statistical modeling through numerous examples drawn from the life sciences.
Numerical Integration
Authors: Smyth Gordon K. 
Published: September 2014

AbstractThis article focuses on the process of approximating a definite integral from values of the integrand when exact mathematical integration is not available. This problem arises in statistics when marginal density functions or expected values of random variables are required. The article describes classical univariate quadrature methods, including the trapezoidal rule, Simpson's rule, Newton–Cotes formulas, Clenshaw–Curtis integration, and Gaussian quadrature. Refinements including adaptive methods, treatment of singularities, and progressive rules of the Gaussian type are also mentioned. A survey is given of the possibilities and limitations of multiple integration methods, including product rules, globally adaptive methods, rules of polynomial degree, lattice methods, and Monte Carlo integration. Detailed pointers are given to available software.
Spline Smoothing
Authors: Turlach Berwin A. 
Published: September 2014

AbstractOver the past few decades, several methods for fitting smooth curves to data have been proposed and intensively studied. Among these methods are kernel smoothers, orthogonal series estimators, wavelet smoothers, and spline smoothers. This entry provides a brief description of the commonly used spline smoothers – regression splines, smoothing splines, and penalized splines.
S
‐
PLUS
and
S
Authors: Venables W. N. 
Published: September 2014

Abstract
S‐PLUS is a commercial software environment for data analysis and graphics based on the programming language S, designed and initially implemented by John M. Chambers at Bell Laboratories. In addition to a large suite of software for standard tasks, the system aims to provide users with the means to implement new techniques or combine standard methodology in possibly new ways. The language is interpreted rather than compiled, and has some object‐oriented features. Versions of S‐PLUS are current for Unix, Linux, and Windows. The system has a command‐line interface only, but the Windows implementation offers a graphical user interface as well.
Spreadsheet
Authors: Hunt N. 
Published: September 2014

Abstract

The term
spreadsheet
is derived from the sheet of paper employed by an accountant to set out financial calculations, often so large that it had to be spread out on a table. The first electronic spreadsheet, VisiCalc, was developed in 1979 by Bob Frankston and Dan Bricklin at Harvard. Originally designed as a business problem‐solving tool, the remarkable versatility of the spreadsheet package has made it a popular working environment among computer users in many professions. The most commonly used spreadsheet packages include Excel, Lotus 1‐2‐3, Quattro Pro, and SuperCalc.
Support Vector Machines
Authors: Schölkopf Bernhard , Smola Alex 
Published: September 2014

AbstractWe describe the central ideas of Support Vector (SV) learning in a nutshell. This includes the notion of learning from data via risk minimization and the notion of kernels as an efficient means of encoding prior information. We give an overview over the optimization problems arising from SV classification and discuss various extensions such as kernel PCA and automatic margin adaptation.
Resampling Procedures for Sample Surveys
Authors: Frankel Martin R. 
Published: September 2014

AbstractThis entry discusses the two basic resampling techniques that have found widespread application in survey sampling: Jackknife Repeated Replication (JRR) and Balanced Repeated Replication (BRR). To date, resampling methods based on the bootstrap have found only limited application in samples, which involve complex (stratified and clustered) sample design.
Back‐Calculation
Authors: Bacchetti Peter 
Published: September 2014

AbstractBack‐calculation estimates past infection rates of an epidemic disease by working backward from observed incidence. This requires: external knowledge about the distribution of incubation times between infection and disease, possibly including how it is changing over time; incidence data that have been adjusted for reporting delay, underreporting, and possibly seasonal effects; and a parametric model or smoothness assumption that imposes some structure on past infection rates. In the case of human immunodeficiency virus, sensitivity analyses have found that uncertainty in back‐calculation is mainly due to uncertainty in these inputs. Projected incidence is obtained by applying the assumed incubation distribution to the estimated (and extrapolated) infection rates, but direct extrapolation of observed incidence may do as well or better in some circumstances.
Conception, Models for
Authors: Weinberg Clarice R. 
Published: September 2014

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