bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pedigrees, Sequential Sampling
Authors: Vieland Veronica 
Published: July 2005

Abstract
Families are frequently selected for genetic epidemiologic study based upon (i) initial ascertainment of (affected) individuals, and (ii) subsequent intrafamilial sampling of (subsets of) relatives of those individuals. Such procedures must be taken into consideration when writing genetic likelihoods, estimating genetic parameters, or performing statistical tests based on ascertained pedigree data.
Privacy in Genetic Studies
Authors: Pelias Mary Kay 
Published: July 2005

AbstractThe evolution of the concept of privacy has included protections of property, personal space, personal activities, personal autonomy, and personal information. These ideas expanded over the twentieth century to include the individual's interests in medical treatment and participation in biomedical research. In response to the human genome project, strong moral and legal principles are now in place for protecting the confidentiality of genetic information and the rights of individuals whose genotypes are elucidated in medical practice and in genetics research.
Segregation Ratios
Authors: Elston Robert C. 
Published: July 2005

Abstract
Whereas genetic transition probabilities refer to the distribution of offspring genotype classes that arise from specific mating types, segregation ratios are the expected ratios of offspring phenotype classes. Typical Mendelian segregation ratios are 1 : 2 : 1 and (in the case of dominance) 3 : 1.
Minimum Therapeutically Effective Dose
Authors: Liu Jen‐pei , Chow Shein‐chung 
Published: July 2005

Abstract
The concept and definitions of minimum therapeutic effective dose (MTED) and therapeutic window of a drug are presented. Design and analysis for estimating MTED are overviewed. Design issues such as inclusion of a placebo concurrent control, selection of dose levels, and sample size determination are addressed. Statistical analyses such as the application of analysis of variance and model‐based approach to estimating MTED are summarized. Discussion on internal validity vs. external evidence is also provided.
F
ix–
N
eyman Process
Authors: Chiang Chin Long 
Published: July 2005

Abstract

Introduced in 1951 in their study of survival of cancer patients, Fix–Neyman process is a continuous time stochastic process with two transient states
S
1
and
S
2
and two absorbing states
R
1
and
R
2
, where
S
1
is the wellness state,
S
2
the illness state,
R
1
the death state from cancer and
R
2
the death state from other causes.

Let
p

ij

(0,
t
) be the transition probability from state si at
t
= 0 to state
s

j

at time
t
. Two simultaneous differential equations are established and solutions derived for
p

ij

(0,
t
), for
i
,
j
= 1, 2. Formulas for the death transition probabilities from
s

i

to
R

d

,
Q

id

(0,
t
), are also given.

Another useful concept is the expected duration of stay in each of the four states during (0,
t
).

The model is useful in areas of research where states
S
1
and
S
2
can be defined.
Forward Search
Authors: Atkinson A. C. , Riani Marco 
Published: July 2005

Abstract
This is a powerful robust statistical method for exploring the relationship between data and fitted models. It aids discovery of clusters of observations and previously unidentified important subsets of the data as well as revealing groups of outliers. Examples are given with regard to the use of the forward search for regression and generalized linear models, and for multivariate data. The method is to search through the data, fitting subsets of increasing numbers of observations, with outliers being included towards the end.
Stochastic Processes
Authors: Chiang Chin Long 
Published: July 2005

Abstract
Several articles in the Encyclopedia of Biostatistics describe various topics in stochastic process, while the present paper covers the main elements of the field, such as random walk, gambler's ruin, Markov chains, a general birth process, an equality in stochastic processes, death process and survival distribution, staging process, birth–death processes, and finite Markov processes. These topics together constitute an adequate representation of the field.
The main feature of this article is the explicit solution for every process presented, especially the K‐M epidemic model, the higher order transition probabilities in Markov chains, and the explicit solution for the Kolmogorov differential equations.
These solutions make it easy for the application of stochastic processes in research in biostatistics, epidemiology, health care, and medical sciences.
Rheumatology
Authors: Farewell Vern T. , Gladman D. D. 
Published: July 2005

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.
Linear Regression, Simple
Authors: Matthews David E. 
Published: July 2005

AbstractSimple linear regression involves finding the best‐fitting curve of a suitable functional form that relates the value of an explanatory variable,X, and the mean value of a response variable,Y, givenX. The goals of regression modeling are to determine whetherYandXare associated in some systematic way, and to estimate or predict the value ofY, or its mean, corresponding to a known value ofX. We describe estimation, statistical inference, and diagnostic checking of the assumed model and any associated unknown parameters, giving due importance to their statistical and scientific interpretations. An example concerning the relationship between systolic blood pressure and age in men illustrates these concepts concretely.
Generating Functions
Authors: Kemp Adrienne W. 
Published: July 2005

Abstract
A generating function is a way of documenting a sequence of items, such as probabilities or moments, using generators of formulas for the individual terms in a sequence. Common generating functions have the form of power series or of exponential series. For discrete distributions, they are particularly valuable for combining sequences of probabilities in various ways. This article discusses moment generating functions and gives examples. It then discusses the cumulant generating function, which is sometimes simpler to handle, and gives examples. Probability generating functions and factorial moment generating function are useful tools for studying discrete distributions.
Iterative Proportional Fitting
Authors: Speed Terry P. 
Published: July 2005

Abstract
Iterative proportional fitting, also known as iterative proportional scaling, is an algorithm for constructing tables of numbers satisfying certain constraints. In its simplest form, the algorithm enables one to construct two‐way tables with specified row and column margins and prescribed association, measured by a family of cross‐ratios. The algorithm can be used to obtain maximum likelihood estimates of table entries under hierarchical loglinear models, and it can be used to adjust counts in survey sampling, where it is known as raking. Natural extensions exist to three and higher‐way tables, and to more general exponential families.
Negative Binomial Distribution
Authors: Biggeri Annibale 
Published: July 2005

AbstractThe negative binomial distribution is an alternative to the Poisson distribution for modeling counts. It is useful when counts show more variation than the Poisson allows (overdispersion). This article shows two ways of deriving it, one way as a waiting time distribution for a certain number of successes with binary data, and the other way as a gamma mixture of Poisson distributions. It then discusses ways of estimating parameters, the use of the negative binomial distribution in regression models, and tests for overdispersion.
Quasi‐Independence
Authors: Mcdonald John W. 
Published: July 2005

AbstractThe quasi‐independence model is a generalization of the model of independence for a two‐dimensional contingency table. It states that a particular subset of cells satisfies the independence structure whereby the cell expected count is a product of a row effect and a column effect. It is often used for square tables (number of rows equal number of columns), the relevant subset being all cells not falling on the main diagonal. This article also discusses connections with loglinear models, extension to multidimensional tables, inference and testing of model fit, and applications and relationships with other models.
S
tat
X
act
Authors: Mehta Cyrus R. , Patel Nitin R. , Senchaudhuri Pralay , Corcoran Christopher D. 
Published: July 2005

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.
Two‐By‐Two Table
Authors: Haber Michael 
Published: July 2005

Abstract

This article summarizes the most commonly used statistical techniques for analyzing two by two frequency tables. It presents methods for the estimation of measures of association (risk ratios, risk differences, odds ratios) and agreement (kappa). Tests of hypotheses are described for large samples (based on asymptotic theory) and for small samples (exact tests). Both conditional (Fisher's exact test) and unconditional exact tests are discussed, and the controversy regarding the use of conditional versus unconditional tests is summarized. The article advocates the use of the mid
p
‐value exact test for small sample sizes. Methods for the determination of sample sizes for tests on two by two tables are also described.
Delayed Entry
Authors: Keiding Niels 
Published: July 2005

Abstract
Methods for analyzing survival data with delayed entry are surveyed. Particular emphasis is put on the definition of random left truncation, the counting process approach and filtering, delayed entry and covariates, and independent and dependent delayed entry. As specific examples are mentioned the prevalent cohort study, confirmatory analysis of a chance finding at interim analysis, and left censoring, right truncation, time‐reversal, and the retro‐hazard.
Expected Number of Deaths
Authors: Væth Michael 
Published: July 2005

Abstract
In epidemiological studies, the mortality in a study population is often related to that of a reference population by computing the number of deaths that is expected if the mortality rates of the reference population are applied to the follow‐up experience of the study population. This method is based on a multiplicative model for the mortality rate and is closely related to standardization techniques. Two approaches to the calculation of the expected number of deaths, the prospective model, and the person‐years method are described. In the simplest form the latter method is essentially an indirect standardization, but the model is easily generalized to include regression on covariables.
Joint Modeling of Longitudinal and Event Time Data
Authors: Henderson Robin 
Published: July 2005

Abstract
The relationship between the development over time of a quantitative response variable and a parallel process recording events in time can be investigated through a joint model for the two outcome types. Interest in this topic arose from two separate backgrounds: the possibility and consequences of informative dropout in longitudinal trials, and measurement error problems for survival studies with time‐varying covariates. Modeling strategies include combining a marginal distribution for one response type with a conditional distribution for the other, or adopting a correlated random effects model with otherwise conditional independence. Naive methods can lead to significant bias, yet more sophisticated modeling approaches are often sensitive to assumptions, some of which cannot be checked from observable data. When simple summaries such as treatment effect estimators are of interest, sensitivity analyses can bring some reassurance as to whether important conclusions are maintained as assumptions are allowed to change. Overall, the topic is relatively new and research is continuing in many directions.
N
elson–
A
alen Estimator
Authors: Borgan Ørnulf 
Published: July 2005

Abstract
It is described how the Nelson–Aalen estimator can be used to provide a nonparametric estimate of the cumulative hazard rate function based on right censored and/or left truncated survival data, and how the estimator can be used to estimate the integrated transition intensities in Markovian multistate models. It is also pointed out how the Nelson–Aalen estimator can be adopted to estimate other quantities, like the integrated relative mortality in a multiplicative model with external rates and the cumulative infection rate in a simple epidemic model. It is shown that counting processes provide a framework that allows for a unified treatment of all these diverse situations, and the most important small and large sample properties of the estimator are summarized. Finally, the construction of confidence bands is considered.
Quality of Life and Survival Analysis
Authors: Olschewski Manfred 
Published: July 2005

Abstract

In clinical research, where time to a specific endpoint like, for example, death is of interest, methods of survival analysis are usually applied. In recent years, such research is more and more accompanied by the inclusion of the patients subjective assessment of treatment circumscribed by the term
health‐related quality of life
(HQoL). In the analysis of HQoL, methods of longitudinal analysis of variance are adequate. In this chapter, we put emphasis on statistical analysis methods that allow a joint analysis of both quantity and quality of survival. The major approach here is defined by quality‐adjusted survival (QAS) times. Conceptually, this means that the survival time of a patient is weighted according to his/her subjective assessment of that time. Advantages and limitations of such a joint analysis are discussed in relation to the separate analyses of the components.
ARMAandARIMAModels
Authors: Helfenstein Ulrich 
Published: July 2005

AbstractNotifications of diseases, entries in a hospital, and so on are frequently collected in regular intervals. Consecutive observations of such “time series data” are likely to be dependent. In environmental medicine, where series such as daily concentrations of pollutants are analyzed, it is evident that dependence of consecutive measurements may be important. A flexible class of models allowing representing of the stochastic dependence of consecutive data are ARIMA models (autoregressive integrated moving average models). They may be particularly useful for forecasting. Forecasts of epidemiological time series are needed since it is of interest to know what frequencies of diseases might be expected in the future, in order to better plan the distribution of resources. It may also be of interest to assess relations between two ARIMA time series, a “response” series such as “daily number of patients coming to a clinic” and “explanatory” series such as daily concentrations of a pollutant, daily mean temperature, and so on. Such situations may be adequately represented by the so‐called “transfer function model”. Analogous questions arise when studying time series data recorded in an individual subject. This article reviews basic concepts of the topic. The methods are illustrated by medical applications.
D
iggle–
K
enward Model for Dropouts
Authors: Diggle Peter J. 
Published: July 2005

Abstract
Dropouts in longitudinal data analysis occur when some subjects withdraw prematurely from the study. When the processes that govern dropout are related to the measurement process, which is the primary focus of the study, interpretation of the data can be difficult. The Diggle–Kenward model gives a description of the association between the dropout and measurement processes in which completely random, random, or informative dropout models can be obtained by imposing simple and explicit restrictions on the model parameters. Typically, informative dropout parameters are poorly identified, and the associated inferences are therefore sensitive to distributional assumptions.
Distribution‐Free Methods for Longitudinal Data
Authors: Davis Charles S. 
Published: July 2005

AbstractMany of the available methods for analyzing repeated measurements require distributional assumptions on either the joint distribution of a subject's repeated measurements or on the marginal distributions at each occasion. Although such methods are often adequate and appropriate, nonparametric approaches to the analysis of repeated measurements may also be indicated in certain situations. One approach is to use nonparametric summary statistics in order to reduce the vector of responses from each subject to a single measurement. Multivariate nonparametric approaches include generalizations of univariate distribution‐free methods such as the Wilcoxon–Mann–Whitney and Kruskal–Wallis tests, randomization model methods based on the use of Cochran–‐Mantel–Haenszel statistics, asymptotically distribution‐free analogs of parametric procedures for normally distributed outcomes, distribution‐free methods for incomplete repeated measurements based onUstatistics, the rank transform method, and nonparametric regression methods such as kernel estimation, weighted local least squares estimation, and smoothing splines.
Moving Average
Authors: Janacek G. J. 
Published: July 2005

Abstract
Given a series of observations made over time, a time series, it is often useful to examine the raw data sequence. One common approach is to apply a moving average. This is, as the name suggests, a series of averages (possibly weighted) of the data sequence. While there are many kinds of moving average, the most common are used to eliminate seasonal or other cyclic effects in time series. While the method is very simple, it can be very effective.
The name, often abbreviated to MA, is used to describe both the output and the computational process. The term Moving Average is also used to describe a time series model in the ARIMA family.
Multivariate Adaptive Splines for Analyzing Longitudinal Data
Authors: Zhang Heping 
Published: July 2005

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.
Spline Function
Authors: Subba Rao T. 
Published: July 2005

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: July 2005

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.
Time‐By‐Time Analysis of Longitudinal Data
Authors: Matthews John N. S. 
Published: July 2005

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.
W
igner–
V
ille Distribution
Authors: Castiglioni Paolo 
Published: July 2005

Abstract
The Wigner–Ville distribution is a time–frequency distribution developed for the analysis of time‐varying spectra. The interpretation of classic Fourier analysis may become problematic when the frequency content changes over time. The power spectrum cannot indicate when specific spectral components occur or how they change in intensity and frequency. Thus, when the signal is characterized by a time‐varying spectrum, it is preferable to decompose the signal power by a joint function of time and frequency. The Wigner–Ville distribution quantifies the fraction of the power in a certain frequency band during a certain time range.
Battery Reduction
Authors: Massaro Joseph M. 
Published: July 2005

Abstract

Battery reduction is used to select a subset of
m
variables from an original set of
n
variables (
m
<
n
) that reproduce a large proportion of the variance in the original set of
n
variables. There are a number of procedures for performing battery reduction analysis. A popular method involves first performing a principal components analysis to select
m
components, which account for the salient variance in the original data. Gram–Schmidt orthogonal rotations are then performed to determine the
m
variables that account for the largest proportion of variance. The procedure is illustrated and reference made to a SAS macro for performing the analysis.
Centroid Method
Authors: D'agostino, Sr Ralph B. , Russell Heidy K. 
Published: July 2005

Abstract
The centroid method is a factoring procedure based on the idea that if the original variables are represented as a set of vectors, then the common factor can be thought of as the vector that passes through the centroid of the terminal points for this set of vectors. The centroid method was originally derived as an approximation to the more complex principal‐axes procedure. An outline of the procedure is provided along with references for further details.
Correspondence Analysis
Authors: Weller Susan C. 
Published: July 2005

Abstract
Correspondence analysis is a procedure for exploring the relationships among two or more sets of variables. A key feature of the analysis is the joint scaling of both row and column variables to provide information on the interrelationships among variables within a set (either row or column variables) and in interrelationships between row and column variables. Correspondence analysis can be used on qualitative or quantitative data. A final step in the analysis involves rescaling of characteristic vectors into optimal scores. Normalizing these optimal scores allows for assessment of relative importance of factors. Correspondence analysis can also be used to find the optimal ordering of variables for a given set of characteristics. If the technique is extended to three or more sets of variables, it is called multiple correspondence analysis.
Cosine of Angle between Two Vectors
Authors: Searle Shayle R. 
Published: July 2005

Abstract

A vector in 2‐space is easily represented by two coordinates. The angle between two such vectors can, by using right angle triangle trigonometry, be expressed as an algebraic function of the two pairs of coordinates, one pair on one vector, the other on the second vector. And this can be extended generally to two vectors in
n
‐space. The resulting expression has useful invariance properties, and can also be thought of as a product–moment correlation.