bluuebunny/crossref_metadata_2025 huggingface dataset:
Health Care Utilization and Behavior, Models of
Authors: Aday Lu Ann , Andersen Ronald M.
Published: September 2014

Models of health care utilization provide guidance for defining variables, specifying the relationships between them, and evaluating programs and policies concerned with access to and utilization of health care services. This chapter reviews four major types of utilization models: (a) models of patient decision making, grounded in sociological theory and research; (b) the health belief model, based in psychological theory; (c) economic models of the demand for medical care; and (d) the behavioral model of health services utilization that has guided much health services research on access to and utilization of health care services.
Health Care Utilization Data
Authors: Ash Arlene S.
Published: September 2014

Many important questions about the fairness, efficacy, or efficiency of health care delivery systems require utilization data. Ideally, we would like to have information on the nature, timing, cost, and setting of each person's utilization, and the ability to link that information with personal demographics, health‐status characteristics, and health outcomes, including declines in functional status and death. We discuss the major sources of health care utilization data in the United States and the characteristics that make them more or less useful for health services research.
Health Care Utilization Data, Analysis
Authors: Ash Arlene S.
Published: September 2014

Data on health care utilization are used to address important questions about what is done for whom, and can shed light on why and with what outcome, in the large segment of economic life that “delivers” health care services to populations. We describe the purposes of and major issues associated with common study types (e.g. small area variations, disparities, provider profiling, cost effectiveness). Methodologic issues include: defining the study scope in accordance with its purpose (data source, population, timeframe, units of analysis and units of inference); choosing appropriate models (dealing with clustered observations, skewness, heteroscedasticity, and “point masses” at zero); and addressing the “censoring” effect of death on cost.
Health Status Instruments, Measurement Properties of
Authors: SchÜnemann Holger , Juniper Elizabeth , Guyatt Gordon
Published: September 2014

In this article, we present the key measurement properties necessary for a useful health status instrument. These instruments can measure health status at a moment in time to differentiate between individuals (discriminative instruments) or they can measure change over time (evaluative instruments). We describe how investigators establish validity, responsiveness, and interpretability of a health status measure. Furthermore, we describe advantages and disadvantages of various administration methods for health status measures.
Instrumental Variables in Health Services Research
Authors: Newhouse Joseph P.
Published: September 2014

Instrumental variable (IV) estimators provide consistent estimates of regression coefficients if an explanatory variable or variables are correlated with the error term. They require a variable or variables that do not explain variation in the dependent variable directly but are correlated with those explanatory variables that are correlated with the error term. Although developed for applications in economics, they have recently become popular in health services research.
League Tables
Authors: Spiegelhalter David J.
Published: September 2014

When comparing organizations, it is tempting to place them in rank order according to some summary statistic, and hence implicitly estimate their “true rank”. This can be a very unreliable exercise, which can be emphasized by making formal inference on the true rank. Mortality rates of New York surgeons are used for illustration. A comparative plot that does not involve explicit ranking is recommended.
Medicare Data
Authors: Ash Arlene S. , Virnig Beth
Published: September 2014

Medicare is a federal health insurance program for nearly all Americans aged 65 and over, for younger persons entitled to Social Security disability payments for at least 2 years, and for people with end‐stage renal disease (ESRD). It is administered by the Center for Medicare and Medicaid Services (CMS, formerly the Health Care Financing Administration, HCFA) in the US Department of Health and Human Services. In 2003, persons with entitlement due to these mandates numbered approximately 34.7 million, 5.8 million, and 300 000 respectively. Medicare data, especially the program's extensive repository of enrollment and administrative claims information, is a unique resource for health service research, due to the program's size, national representativeness, continuity of enrollment, presence of race and date‐of‐death data, well‐documented history of use in research, and the help available for researchers wishing to use the data for studies of potential benefit to Medicare beneficiaries.
Quality of Life and Health Status
Authors: Patrick Donald L.
Published: September 2014

Clinical studies, epidemiological investigations, population surveys, and clinical practices increasingly incorporate self‐reported measures of health status and quality of life. These help to determine whether treatments are doing more good than harm, whether health and quality are improving or worsening, and to see if health status differs between groups. Self‐reported outcomes are often compared to clinical or performance‐based measurements that remain the primary endpoints for most clinical trials and are important markers of disease, injury, and their trajectories. Self‐reported measures of health and quality of life, however, often have more meaning to the persons affected by disease or treatment. Because perceptions of health and illness influence what people do about their health (e.g. visit doctors, go to hospital, or ignore signs and symptoms), policy makers are also increasingly interested in self‐reported outcomes.
Small Area Variation Analysis
Authors: Diehr Paula
Published: September 2014

Small area variation analysis examines the variation in the rates of performance of a medical procedure, such as an appendectomy, across geographic areas. The approach is used to identify procedures or DRGs in which there is too much variation, with the goal of improving guidelines or identifying outlying providers. Unless the number of procedures per area is very large, variations analyses present a variety of statistical challenges.
Admixture in Human Populations
Authors: Majumder Partha P.
Published: September 2014

When parental populations give rise to a hybrid population, it is possible to estimate the proportions of admixture from each parental population on the basis of known marker allele frequencies if there has been no genetic drift. Methods include generalized least squares and maximum likelihood. It is also possible to use a measure of genetic similarity. All these methods assume that the admixture is a static, one‐time phenomenon, whereas in reality there is usually a continued, long‐term exchange of genes among populations.
Age‐of‐Onset Estimation
Authors: Siegmund K.
Published: September 2014

Age of onset estimates are crucial to clinical management and designing disease prevention studies. For many diseases, age is the primary risk factor. Age of onset is censored if a person is unaffected. Two different modeling philosophies are possible: to assume a mixture of susceptible and nonsusceptible individuals in the population, or to assume that all individuals are susceptible and would become affected if only they lived long enough. Variability of age of onset as a function of exposures can be modeled through the hazard. Designs possible to estimate age of onset parameters are: case—control family studies, kin—cohort studies, and the study of high‐risk families. Age‐specific penetrances can be estimated using a likelihood, pseudolikelihood, marginal likelihood, weighted score, or the method of moments.
Genetic Markers
Authors: Elston Robert C.
Published: September 2014

Genetic markers are genetic polymorphisms, used for the study of population structure, linkage and association analysis, paternity testing, and forensics, that segregate in a simple Mendelian fashion. They include blood cell antigens, restriction fragment length polymorphisms, minisatellites (variable number of tandem repeat), microsatellites (short tandem repeat), and single nucleotide polymorphisms.
Blood Groups
Authors: Reisner E.
Published: September 2014

The term
blood groups
encompasses the products of genes expressed as molecules on the surface of red cells in humans. They were first investigated for their clinical importance in the transfusion of blood products and then became useful as genetic markers for population genetic studies. Included in the blood groups are the ABO, Rhesus (Rh), MNSs, P, Kell, Duffy, Kidd, Lutheran, and Lewis blood.
Candidate Gene
Authors: Elston Robert C.
Published: September 2014

The “candidate gene” approach presupposes that we have reason to believe that a particular metabolic pathway underlies a disease. We then try to confirm or refute this belief by seeking a disease–marker association or linkage, where the marker is the causative gene, in linkage disequilibrium with the causative gene, or tightly linked but in equilibrium with it.
Case‐Only Gene Mapping
Authors: Weir B. S. , Nielsen D. M.
Published: September 2014

When individuals can be characterized as being affected or unaffected by a disease, marker‐trait association can be addressed by comparing marker frequencies between these two categories. It is also possible to infer linkage or association on the basis of marker data from affected individuals only. When there is linkage disequilibrium a test for Hardy‐Weinberg proportions among marker genotypes in the affected population is a test about marker allele and genotype associations with the disease locus. However, rejection Hardy‐Weinberg at the marker locus in the affected population may simply reflect nonrandom mating in the whole population.
Commingling Analysis
Authors: Mendell Nancy Role , Finch Stephen J.
Published: September 2014

Commingling distributions have been proposed as a logical extension to the genetic model for genetic traits that are either polygenic or multifactorial. Mixture models can be used to describe unobserved heterogeneity in many situations. For normal component distributions with equal variance, the EM algorithm can be used to find the maximum likelihood estimates of the parameters. The asymptotic distribution of the likelihood ratio test of the null hypothesis of a single normal distribution is unknown but can be approximated by the parametric bootstrap. A Bayesian approach to this problem can also be taken.
Disease‐Marker Association
Authors: Schaid D.
Published: September 2014

Disease–marker association can be due to both genetic and nongenetic causes. The former are either that the marker plays a functional role in the disease process or that it is in close proximity to a disease locus (in linkage disequilibrium with it). Association can also be due to population stratification or admixture. Usual contingency table chi‐square methods assume independence of alleles and/or individuals. In the absence of this independence, it is possible to allow for population heterogeneity by using genomic control (i.e. markers that are unlinked to the disease) or family‐member controls, which provide various degrees of matching on ethnicity. Association studies can also use extended pedigree data provided that allowance is made for the statistical dependence of the members in the same pedigree.
Family History Validation
Authors: Petersen Gloria M.
Published: September 2014

Analysis of family data relies on accurate reports of family history. Sensitivity and specificity of family history of disease or exposures can be measured, and have been found to vary, depending upon different factors. These include: who provides the history (self versus proxy), the circumstances of the condition being studied, and demographic characteristics of the informant.
Genetic Heterogeneity
Authors: Olson Jane M.
Published: September 2014

The term genetic heterogeneity refers to the underlying diversity in genetic architecture of different individuals in the population with a common phenotype. Two types of genetic heterogeneity are commonly distinguished: locus heterogeneity in which variants at different loci can predispose to the same phenotype, and allelic heterogeneity in which different variants at the same loci can predispose to the same phenotype. Although allelic heterogeneity is certainly of interest, once a susceptibility locus is identified, the presence of locus heterogeneity so dramatically reduces the power of gene mapping for complex traits that it remains a major statistical challenge. In this article, we primarily discuss models of linkage analysis that allow for locus heterogeneity, including more recent models that allow for heterogeneity‐related covariates.
Genetic Map Functions
Authors: Speed Terry P.
Published: September 2014

A genetic map function gives a relationr = M(d) connecting recombination fractionsrand genetic map distancesdbetween pairs of loci along a chromosome. They have been widely used in genetics because (a) genetic map distances are additive, whereas recombination fractions are not; and (b) recombination fractions are much easier to estimate from data. In effect, map functions correct for multiple exchanges in chromosomal intervals during meiosis. The most well‐known map function is that due to Haldane,r = 0.5(1 − exp(−2d)), which arises in the Poisson or no‐ interference model for recombination. Another is associated with the name Kosambi. In general, map functions should be regarded as organism specific, as they embody certain assumptions about recombination, in particular, the nature and extent of genetical interference. However, map functions only consider pairs of loci, and do not necessarily correspond to global models of recombination. While genetical interference has some consequences for map functions, it is not well captured by the associated map function. When multilocus mapping is carried out with a full probability model for recombination, map functions are not needed.
Genetic Risk Ratios
Authors: Kopciuk K. A. , Bull Shelley B.
Published: September 2014

Genetic risk ratios measure the association between known or putative genetic factors and the occurrence of a particular disease. These risk quantifiers are expressed as relative risks (RRs) or odds ratios (ORs). Risk ratios for relatives, such as measures of familial aggregation or recurrence risk in relatives, use family disease history to assess the evidence for an inherited genetic basis for the disease. Allelic, genotype, and haplotype risk ratios evaluate the association of a disease with a measured candidate gene or with one or more genetic markers. Risk ratios estimated under a case–control design may be population‐based or family‐based depending on the type of control group.
Mendel's Laws
Authors: Wijsman Ellen M.
Published: September 2014

Gregor Mendel's experiments with peas lead to the development of two laws that describe, in quantitative terms, trait distributions in the offspring of a mating. The first, which still forms the fundamental basis behind much of genetics, is the law of segregation. In modern terms, this law states that each individual, as a parent, transmits to an offspring, exactly one randomly chosen allele of the two present at each locus in the individual. The second law, or law of independence, describes the joint behavior of more than one gene. This law states that alleles at one locus segregate independently of alleles at other loci. This proposition is now known to be false for genes on the same chromosome, but still, usually adequately describes the inheritance patterns of multiple genes, other than those on the same chromosome.
Multilocus (gene x gene interaction)
Authors: Cordell Heather J.
Published: September 2014

An important topic in molecular and quantitative genetics is epistasis, or the interaction between genes. There has been much confusion in the literature owing to differing definitions and interpretations of gene–gene interaction. This article reviews some of the most popular definitions used in the literature and notes that the correspondence between statistical and biological interaction effects may be more limited than is sometimes assumed.
Mutation
Authors: Elston Robert C.
Published: September 2014

Mutations arise from mistakes in copying DNA. They may be nonsense mutations (resulting in the formation of an incomplete protein) or missense mutations (resulting in the change of an amino acid in a protein).
Parental Effects
Authors: Fallin Dani , Palmer Lyle J.
Published: September 2014

An individual's risk for a genetic disease or phenotype, conditional on their own genotype, may depend on parental effects such as imprinting, or the genotype or phenotype of a particular parent. For example, under imprinting, a risk‐conferring allele may only be expressed in an individual if it is inherited from a particular parent. Alternatively, a mother's genotype, even if not transmitted to her offspring, may influence
in utero
conditions and increase risk and/or interact with genetic predisposition for particular diseases among those offspring. The existence of such parental effects may lead to incorrect interpretations of the (marginal) effects of particular genes when performing human genetic studies at the individual level, unless such effects are accounted for in the analysis. We discuss potential scenarios of such parental effects, and present an overview of current methods for accommodating these effects when performing segregation analysis, linkage analysis, or genetic association studies.
Pedigrees, Sequential Sampling
Authors: Vieland Veronica
Published: September 2014

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.
Penetrance
Authors: Wijsman Ellen M.
Published: September 2014

Penetrance is a probability model that relates, for an individual, the observed phenotype to the underlying genotype. Penetrance functions may be continuous or discrete, may include measured covariates, and may involve more than one locus. All variables not explicitly in the model are assumed to be randomly distributed among individuals, leading to variance in estimates of penetrance, in part because of variability in unmeasured covariates and genetic background among populations.
Polymorphism
Authors: Elston Robert C.
Published: September 2014

Polymorphism refers to the different forms an organism can have. In genetics, it refers to the different genotypes possible. The term is used by some authors merely to mean a common allele.
Segregation Ratios
Authors: Elston Robert C.
Published: September 2014

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.
Clustering, Single Linkage
Authors: Massaro Joseph M.
Published: September 2014

Single linkage clustering is used in hierarchical clustering analysis to partition a set of
n
observations into
g
groups or clusters on the basis of
p
variables measured on the
n
observations. The analysis is performed on an
n
 × 
n
matrix of pairwise distance measures between observations. The procedure starts by combining the two observations with the smallest distance between them. The procedure continues to combine clusters with the smallest distances until only one cluster, including the entire sample, remains. The number of groups,
g
, is determined to represent the final number of clusters to retain.
Biological Standardization
Authors: Gaines Das Rose E.
Published: September 2014

The potency of a biological product usually needs to be assessed in terms of its biological activity rather than in terms of its chemical or physical properties. Biological variability means that this assessment depends on statistical techniques, such as bioassay and on the availability of biological standards, which have known properties. Standards can be local, as might be used within a company for the assessment of a new assay or international standards, which are required for the primary calibration of biological products.
Dilution Method for Bacterial Density Estimation
Authors: Gaines Das Rose E.
Published: September 2014

There are many methods for the measurement of bacterial mass and concentration. Different measures reflect different characteristics of the bacterial mass, and not all measures reflect the number of viable bacteria. If direct counting and characterization of individual bacterial cells is not possible or feasible, indirect methods must be used. Determination of the “most probable number” and serial dilution assays are indirect methods intended to reflect the number of viable bacteria.
Immunotoxicology
Authors: Portier Christopher J. , Germolec Dori
Published: September 2014

Immunotoxicology is the study of the response of the immune system to toxicological insults, which are usually induced by xenobiotics. Experiments are carried out
in vitro
or
in vivo
. These can be
host resistance assays
, which concern the ability of the immune system to protect the host from infectious agents and so on or they can be
immune function assays
, which measure the response of the immune system to a stimulus. The former are largely analyzed using methods for categorical data, whereas the latter usually involve very large counts and these are generally analyzed using methods for continuous data. There is current interest in using immunotoxicity data in risk assessment.
Quality Control in Laboratory Medicine
Authors: Strike P. W.
Published: September 2014

An overview of the principles and practices of Quality Control (QC) in laboratory medicine, encompassing the origins of bias and variability in laboratory measurement, the regulation of same through internal (within‐lab) and external (between‐lab) QC programs, and concluding with an account of the strategies proposed for defining meaningful analytical goals in respect of the “quality” component of QC.
Median Effective Dose
Authors: Liu Jen‐pei , Chow Shein‐chung
Published: September 2014

The median effective dose (MED) is the median of the tolerance distribution of the dose levels for a stimulus that generates, on average, predefined responses in 50% of experimental units. The median effective dose is usually denoted as ED50. An overview of the design and analysis for inference about ED50is provided. First, two commonly employed tolerance distributions—normal and logistic distributions—are introduced to characterize the relationship between the dose and response. Under a location/scale model for the tolerance distribution, estimation procedures for ED50are reviewed. Interval estimation methods include delta method, the Fieller's theorem, likelihood approach, and Bayesian procedures. The difficulties associated with interval estimation by the Fieller's theorem and likelihood method are addressed. Inference procedures for ED50are also summarized for the situations where (a) the underlying population consists of a mixture of homogeneous subpopulation, (b) the responses of individual experimental units are dependent, and (c) responses are observed at some prespecified time points. Nonparametric methods such as Spearman–Kärber estimators as well as other robust procedures are reviewed. An account of different nonsequential designs for MED under various optimality conditions is given. Finally, a brief summary of sequential procedures and designs is provided.
Minimum Therapeutically Effective Dose
Authors: Liu Jen‐pei , Chow Shein‐chung
Published: September 2014

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.
Slope–Ratio Assay
Authors: Redmond Carol K.
Published: September 2014

Slope‐ratio assays are similar to parallel‐line assays insofar as both are indirect analytical assays, which aim to estimate the potency of a test preparation relative to that of a standard. Slope‐ratio assays are most often encountered in microbiologic applications. As with a parallel‐line assay, the key assumption is that the expected response to a given dose of the test preparation is the same as that which would be obtained from the standard preparation administered at a dose that is ρ times the given dose. As in parallel‐line assays, the parameter ρ is known as the relative potency. Unlike the parallel‐line assay, the expected response, which is almost always continuous, is linear in the dose, not its logarithm. The graphs of the response against dose for test and standard preparation will be straight lines of different slopes and common intercept, as the assumptions of the assay demand the same outcome for standard and test preparations when the dose is zero. The analysis can be accomplished by a multiple regression, with the estimator of ρ being the ratio of the slope estimators. Interval estimates are often obtained using Fieller's theorem.
Stimulus–Response Studies
Authors: T. Morgan Byron J.
Published: September 2014

Simple stimulus–response experiments result in estimates of the probability of a hit (getting it right) and probability of a false alarm (getting it wrong), which produce a point on a receiver operating characteristic (ROC) curve. Signal‐detection models convert this information into a single measure of performance. There are several elaborations of the simple experiment. ROC curve methodology has been applied in areas such as ecology, medicine, engineering, and psychology.
Autocorrelation Function
Authors: Rabe‐Hesketh Sophia
Published: September 2014

The correlation between two random variables that are part of a time series is usually known as the
autocorrelation
. A series of such correlations can be found according to the time separation or lag of the two variables. A plot of these correlations against the lag, sometimes known as a correlogram, is often an important tool in identifying a suitable model for the series.
B
arahona–
P
oon Test
Authors: Castiglioni Paolo
Published: September 2014

The Barahona–Poon test is used to detect nonlinear dynamics in time series. The method compares linear and nonlinear models fitted to the data, and rejects the null hypothesis of a linear model if at least one nonlinear model is significantly more predictive than all the linear models considered.
Baseline Adjustment in Longitudinal Studies
Authors: Senn Stephen
Published: September 2014

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.
Change‐point problem with biostatistical applications
Authors: Pawitan Yudi
Published: September 2014

The change‐point problem is to determine the unknown location τ, based on an ordered sequence of observationsx1, …,xn, such that the two groups of observationsx1, …,xτandxτ+1, …,xnfollow distinct models. In most applications, each model is a regression model, so the overall model is a nonlinear regression model. A commonly used change‐point model is the piecewise linear model. Statistical inference on the change parameter τ is nonstandard, since the likelihood function is not differentiable with respect to τ. The article provides a review of methodology from a general quasi‐likelihood framework, and discusses an application of the change point in breast cancer incidence as a function of age.
D
iggle–
K
enward Model for Dropouts
Authors: Diggle Peter J.
Published: September 2014

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: September 2014

Many 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.
L
evinson–
D
urbin Algorithm
Authors: Castiglioni Paolo
Published: September 2014

The Levinson–Durbin algorithm is a method for finding the solution to a system of linear equations defined by a Toeplitz matrix (with constant diagonal elements).
Lomb Periodogram
Authors: Castiglioni Paolo
Published: September 2014

The Lomb periodogram is a generalization of the periodogram for unequally spaced time series. Since traditional spectral estimators need even sampling rates, unevenly spaced series need to be interpolated and resampled before spectral analysis. By contrast, the Lomb method does not require interpolation and resampling.
Longitudinal Data Analysis, Overview
Authors: Dunn Graham , Pickles Andrew
Published: September 2014

The 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.
Mining Time Series Data
Authors: Keogh Eamonn J.
Published: September 2014

Some of the problems raised by massive time series databases are briefly considered. They include indexing, clustering, classification, prediction, association detection, summarization, anomaly detection, and segmentation, most of which make explicit or implicit use of a distance measure. A key feature is the approximate representation of time series.
Multivariate adaptive splines for analysing longitudinal data
Authors: Zhang Heping
Published: September 2014

In 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

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.
Reverse Arrangement Test
Authors: Castiglioni Paolo
Published: September 2014

The reverse arrangement test is used for evaluating whether a sequence of ordered data is derived from independent observations of the same random variable by detecting whether a significant trend underlies the observations.
Reversibility
Authors: Lawrance A. J.
Published: September 2014

Reversibility is a concept applying to stochastic processes that are stationary in time but whose structure makes no recognition of the direction of time. However, the contrary is often the case, and interest is in features, which are not the same when time is considered in the reverse direction.
Robust Methods in Time Series Analysis
Authors: Lawrence Clive J.
Published: September 2014

In this article, a number of techniques for analyzing time series data that are robust to the presence of outliers are discussed. The general approach is to remove or suppress the possible effects of contamination or spurious information by smoothing.
S
lutzky–
Y
ule Effect
Authors: Loynes R. M.
Published: September 2014

Smoothing a time series by forming a moving average is a commonly employed approach. In this article, some of the problems that arise are discussed, in particular, the introduction of correlations even when the observations in the original series were independent.
Spectral Analysis
Authors: Janacek G. J.
Published: September 2014

Identifying cyclic effects in time series data is often an extremely important part of the analysis of the series. In this article, a number of approaches are discussed, including harmonic analysis and linear filters.
Spline Function: Introduction
Authors: Rao T. Subba
Published: September 2014

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

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

There 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 Series Regression
Authors: Campbell Michael J.
Published: September 2014

In multiple regression, one of the key assumptions is that the outcome variable is a sequence of independent observations. When this assumption is implausible, for example, when the outcome variable is ordered in fine, then we must modify the model fitting and testing procedure. This article discusses models for continuous and count outcomes.
Time‐By‐Time Analysis of Longitudinal Data
Authors: S. Matthews John N.
Published: September 2014

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.
Y
ule–
W
alker Equations
Authors: Cheng Bing
Published: September 2014

In a stationary autoregressive time series of order
p
, the Yule–Walker equations are a set of
p
linear equations for the regression coefficients in terms of the autocorrelation functions. By estimating the latter, one can obtain estimates of the former.
Z
Analysis
Authors: Castiglioni Paolo
Published: September 2014

Z
‐analysis is a method of measuring the association between two probabilistic events by a statistical link coefficient ranging from −1 for mutually exclusive events to +1 for independent events.
Communicable Diseases
Authors: Farrington C. P.
Published: September 2014

Many 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

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

Historically, 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

Assessing 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

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

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

Psychometrics 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

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

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.
Simple Structure
Authors: B. D'Agostino Ralph , Russell Heidy K.
Published: September 2014

In factor analysis, a simple structure is the ideal structure in which each factor is defined by a subset of the original variables with as little overlap as possible. This article defines the criteria for simple structure and gives some guidelines for application.
Classification, Overview
Authors: Gower John C.
Published: September 2014

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

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.