bluuebunny/crossref_metadata_2025 huggingface dataset:
Drug Utilization Patterns
Authors: Abrahamowicz Michal , Tamblyn Robyn
Published: September 2014

We review methodological challenges in the observational studies of the patterns and outcomes of drug utilization. First, we address issues related to design and data collection in different studies that rely on administrative databases, discuss their limitations in accounting for clinical indication bias, and outline new opportunities created by the emergence of the integrated electronic patient records. Then, we focus on the different analytical issues related to such complexities as prescriber effects, assessing the impact of policy changes on drug utilization, description and classification of longitudinal patterns of medication use, alternative methods of measuring time‐dependent exposure status and medication dose, use of nonparametric modeling to represent time‐varying, lagged and/or nonlinear effects of exposure.
False Negative Rate
Authors: Walter Stephen D.
Published: September 2014

The false negative rate of a diagnostic or screening test is defined as the complement of sensitivity. Its appropriate denominator is specified.
Diagnostic Tests, Likelihood Ratio
Authors: Roberts Robin S.
Published: September 2014

This article concerns the use of diagnostic test information in medical diagnosis. Traditionally, the association between a diagnostic test and the true state of disease has been quantified in terms of sensitivity and specificity. The application of Bayes Theorem allows the formal incorporation of the diagnostic test information as a post‐test probability of disease. The positive and negative likelihood ratios (LRs) are alternative expressions of the informational properties of the diagnostic test, which are computed from sensitivity and specificity. LRs offer several advantages including (a) simplifying the calculation of post‐test probability (via odds), (b) providing a more direct indication of the diagnostic value of the test, and (c) avoiding the need to dichotomize a quantitative test. Given enough data, one could estimate a continuous LR curve and thus compute post‐test probability for any test value.
Pharmacoepidemiology, Adverse and Beneficial Effects
Authors: Guess H. A.
Published: September 2014

Pharmacoepidemiology has been defined as “the study of the distribution and determinants of drug‐related events in populations and the application of this study to efficacious drug treatment.” The termdrugis generally understood to include vaccines and the populations are understood to be human. Pharmacoepidemiology is an essential component of pharmaceuticaloutcomes researchand ofrisk management of pharmaceutical products. Many pharmacoepidemiologic studies are observational, and selection bias and confounding, includingconfounding by indication, are especially critical problems to be addressed in design, analysis, and interpretation. Information bias from misclassification of exposures, outcomes, and covariates is also of particular concern and may impair the ability to control confounding. Another problem is that the hazard function for drug‐related adverse and beneficial effects often varies with duration of therapy. The hazard function may be influenced not only by the biology but also by depletion of susceptibles.
Predictive Modeling of Prognosis
Authors: Marshall G.
Published: September 2014

Prognosis is defined in terms of the relative probability of developing one of the several alternative outcomes in the natural history of a disease. This entry discusses predictive models for prognosis using a general Markov model in which the exact transition times are not observed. Data from a longitudinal study of young patients with diabetes are used to determine factors affecting the natural course of diabetic retinopathy. Other examples mentioned include hepatocellular carcinoma and HIV infection.
Prevalence of Disease, Estimation from Screening Data
Authors: Pickles Andrew , Dunn Graham
Published: September 2014

Simple estimation of prevalence from sample survey data may be biased because of imperfections in the measurement instrument. An alternative approach is to use two‐phase sampling in which a subsample of screened individuals is investigated more intensively with a gold standard measure to reveal their true disease status. A variant design is to use a calibration or validation sample of individuals who are assessed using both the screen and gold standard methods. The analysis of two‐phase samples is described, including the use of simple conditional probabilities, inverse probability weights, and a more general framework in which the EM algorithm is used. Bayesian approaches are also mentioned briefly. Statistical software for the analysis of multiphase data is mentioned, together with an outline of certain design considerations.
Secondary Attack Rate
Authors: Halloran M. Elizabeth
Published: September 2014

The secondary attack rate is defined as the probability that infection occurs among susceptible persons within a reasonable incubation period following known contact with an infectious person or an infectious source. It is a key epidemiologic parameter in infectious diseases that are transmitted by contact. It can be estimated using a variety of epidemiologic study designs, models, and methods of estimation. Inference needs to take into account the correlation of susceptibles exposed to the same infectious source.
Epidemic Models, Multi‐Strain
Authors: Scalia‐Tomba Gianpaolo
Published: September 2014

Deterministic and stochastic models are briefly described for an infectious process caused by two strains of a pathogenic agent, where infection by one strain confers immunity to the other.
Epidemic Models, Spatial
Authors: Renshaw Eric
Published: September 2014

For a proper understanding of the dispersal and control of infectious diseases, it is often necessary to consider spatial models in which individuals do not mix homogeneously. A birth, death, and migration/infection process for separate colonies, with migration/infection to nearest neighboring colonies, is considered from both deterministic and stochastic viewpoint. Topics covered include Monte Carlo simulation and threshold levels, with particular reference to the transmission of HIV/AIDS and rabies.
Reproduction Number
Authors: Dietz K.
Published: September 2014

The basic reproduction number (or ratio) is the expected number of secondary cases of an infection, which one typical case could generate during its infectious period in a completely susceptible population. It is the most important theoretical concept in communicable disease epidemiology.
Average Age at Death
Authors: Kravitz Howard M.
Published: September 2014

This article describes the fallacy of “average age at death”, a common misuse of age data for summarizing mortality experience and estimating life expectancy. An example is presented to illustrate this fallacy. Considerations that should be taken into account for more accurate analysis of these data are reviewed.
Chain Binomial Model
Authors: M. Longini Ira
Published: September 2014

The chain binomial model arises from chains of binomial probability mass functions, where each function is conditioned on previous function. Products of these probability mass functions give the probabilities of particular sequences of binomial realizations. This chain structure allows for detailed analysis of physical phenomena that can be represented by chain binomials. The simple chain structure allows for statistical inference based on likelihood theory.

The chain binomial model forms the statistical underpinnings of the life table. In addition, the chain binomial model has been important in the analysis of infectious disease spread in small groups such as households. For example, the Reed–Frost model is a chain binomial model where the number of new infectives at some time
t
depends on the number of infectives and susceptibles at time
t
 − 1. This model has been used to estimate epidemiological parameters for the spread of such diseases as measles, smallpox, and influenza.
Epidemic Models, Deterministic
Authors: P. Heesterbeek J. A. , Roberts M. G.
Published: September 2014

In this article, we briefly review some of the approaches to deterministic modeling of epidemic phenomena. We have chosen to structure the approaches by specific biological examples that exemplify a certain approach and that were, in addition, historically important in shaping the development of the deterministic theory. These examples are measles, malaria, helminth infections, and HIV. For types of models, we briefly discuss ODE models and PDE age‐structured models of compartmental SIR‐type (both for microparasitic agents), ODE models for helminth parasites, formulated in terms of average parasite load, and the more general microparasite models in terms of integro‐differential equations. In the latter, there is flexibility in defining the infectivity of individuals as a function of infection‐age, which does not exist in the compartmental models. We also discuss the way to calculate the basic reproduction ratioR0for each class of models.
Econometric Methods in Health Services
Authors: Maciejewski Matthew L. , Hebert Paul L. , Conrad Douglas A. , Sullivan Sean D.
Published: September 2014

The purpose of this article is to provide a brief summary of prominent econometric applications in modern health economics, by providing a broad overview of problems to which econometric methods have been applied. These methods typically are used to address problems in health, health care delivery, and health care costs from a cross‐section of observations at a given time. The chapter considers methods for analyzing continuous outcomes, such as cost; continuous, nonnormally distributed outcomes; simultaneous equation methods; and methods for count data, such as utilization data.
Health Economics
Authors: Conrad Douglas A. , Sullivan Sean D.
Published: September 2014

This chapter examines the major research questions facing health economics today and describes the larger context for those questions in terms of allocative efficiency and technical efficiency. The theory underlying various decision‐making algorithms is also elucidated–cost–effectiveness, cost–benefit, and cost–utility analyses being the most prominent examples. The Pareto‐efficiency criterion is explicated, and the underlying assumptions of the different algorithms are presented. The chapter concludes with examples of the application of various decision rules and develops the concepts of risk, return, and the time value of resources devoted to health investments.
Health Services Data Sources in Canada
Authors: Taillon S.
Published: September 2014

Canada has a predominantly publicly financed health insurance system that covers medically necessary hospital (inpatient and outpatient) and physician services for all residents. This system, popularly known as “Medicare”, consists of 13 interlocking health plans administered by the 10 provinces and three territories.

In December 1993, the Canadian Institute for Health Information (CIHI) was established as an independent, nongovernmental, not‐for‐profit corporation and the Deputy Ministers of Health confirmed the CIHI board as its principal advisor on health‐information‐related matters. The CIHI was established to serve as the national mechanism to coordinate the development and maintenance of a comprehensive and integrated health information system for Canada. In addition, the CIHI provides and coordinates the provision of accurate and timely information required for the establishment of sound health policy; the effective management of the Canadian health system; and to generate public awareness about factors affecting good health.

This article describes some of the major Canadian health‐related data holdings, which may be accessible to researchers or others.
Health Services Research, Overview
Authors: Lohr Kathleen N.
Published: September 2014

Health services research studies how social factors, financing systems, organizational structures and processes, technology, and personal behaviors affect access to health care, the costs and quality of health care, and the health and well‐being of individuals and populations. HSR, a boundary‐crossing field, engages a very wide set of clinical and academic disciplines, investigates a broad array of questions relating to health care delivery and management, financing, clinical practice, personal health care decision‐making, and health policy, and employs virtually all quantitative and qualitative methods found in the social sciences, economics, outcomes measurement, management and administration, epidemiology and statistics, law, and policy analysis. It has one major professional organization, Academy Health, and is supported in the US in large measure by the federal Agency for Healthcare Research and Quality, certain other federal agencies, and philanthropic organizations; numerous journals exclusively or predominantly publish on HSR‐related topics.
Informatics in the Health Sciences
Authors: Lehmann Harold P.
Published: September 2014

Informatics is the use of information technology, broadly conceived, to advance a domain of work or inquiry. Concerns within informatics divide into system, role, function, workflow, information repository, information tools, standards, and technology. Individual efforts go through four phases: model formulation, system development, system installation, and study of effects. Areas within health sciences informatics include medical informatics, nursing informatics, public health informatics, and statistics informatics.
Admixture Mapping
Authors: Majumder Partha P.
Published: September 2014

Admixture between populations leads to linkage disequilibrium between loci if the allele frequencies are different between populations. One can test whether the linkage disequilibrium is due to linkage or admixture. One can condition the analysis on parental genotype or parental admixture. In the former case, what is scored is whether a haplotype in a parent is transmitted or not to an affected offspring.
Anticipation
Authors: Dawson Deborah V.
Published: September 2014

Anticipation denotes a phenomenon characterized by increasing severity and/or earlier onset of a disease in successive generations. It could be induced by ascertainment bias and this cast serious doubts among many geneticists about its reality as a biological, phenomenon. However, it is now recognised that it may occur as result of trinucleotide repeat expansion. The number of repeats has been found to correlate with severity and onset age, and this is often associated with a parent‐of‐origin effect. Anticipation has also been reported for a growing number of disorders not found to be associated with trinucleotide repeat expansion.
Ascertainment
Authors: Hodge S. E.
Published: September 2014

For most disorders of genetic interest, families are not selected for study at random but rather because they are segregating for the disorder. This biases the estimate of the segregation ratio if not properly allowed for in the analysis. The classical ascertainment model assumes families are ascertained if they contain at least one proband, an affected person who at any time was detected independently of the other members of the family. Ascertainment can be complete (“truncate selection”), multiple, or single; in the last situation, the probability of that a sibship will be ascertained is proportional to the number of affected sibs it contains. In practice, this independent “proband” model may not be appropriate and other models have been proposed. However, some situations still remain difficult or intractable.
Assortative Mating
Authors: Wilson Susan R.
Published: September 2014

Assortative mating occurs when the phenotypes of biological parents are more (or less) similar than would occur under random mating. In contrast to inbreeding, mechanisms for assortative mating are poorly defined. The classical model for assortative mating makes strong assumptions, such as independent genetic and environmental contributions to a trait and no interaction between genotype and environment. More complex models have been developed, especially for human behavioral traits. Assortative mating affects the estimations and interpretation of heritability and lowers the power of linkage studies.
Cladistic Analysis
Authors: Zhu Xiaofeng , Cooper Richard S. , Elston Robert C.
Published: September 2014

Haplotype based association analysis is playing an important role in dissecting the genetic factors underlying complex diseases. Cladistic analysis, which incorporates the evolutionary information among haplotypes, can improve the power to detect genetic factors by limiting the number of hypothesis tests that need to be performed. In this article, we describe the cladistic analysis method and its application to mapping quantitative trait loci.
Comparative Genomic Hybridization
Authors: H. Moore Dan
Published: September 2014

Comparative genomic hybridization is a method for directly identifying regions of gains and losses of genomic material in chromosomes. The results are measured as a series of test‐to‐reference signals from fluorescent dyes for a large number of distinct chromosomal regions and statistical methods are used to identify regions where the signal ratios are significantly different from 1.0. It is applied to microarrays to define abnormality in tumours.
Complex Diseases
Authors: Palmer Lyle J.
Published: September 2014

The principal distinction between “simple” monogenic diseases and “complex” diseases is that the latter do not exhibit classic patterns of Mendelian inheritance. Complex diseases tend to involve greater difficulties in phenotype definition and present challenges for genetic epidemiology. There is, typically, incomplete penetrance and genetic heterogeneity and they are often closely associated with “intermediate” phenotypes that are themselves highly heritable. The use of isolated populations, inbred animal models, and linkage and association analysis with haplotypes are often used in the study of complex diseases.
E
lston–
S
tewart Algorithm
Authors: Elston Robert C.
Published: September 2014

The Elston–Stewart algorithm is a recursive algorithm that allows one to calculate the pedigree likelihood of an oligogenic model as a function of founder genotypic probabilities, penetrance functions, and genetic transition probabilities. The amount of time required for the computation increases linearly with the size of the pedigree, but exponentially with the number of loci in the genotype. In contrast, the Lander & Green algorithm has computational time that increases exponentially with the size of the pedigree, but linearly with the number of loci. An analogous algorithm allows the likelihood to be calculated for a pedigree of
n
individuals under polygenic inheritance without needing the inversion of an
n
 × 
n
symmetric matrix.
Eugenics
Authors: Pelias Mary Z.
Published: September 2014

The Eugenics Movement was born in England in the late nineteenth century and rapidly found ardent followers in England, the United States, and Germany. The goal of eugenics was to describe and improve the genetic endowment of man. Investigations in eugenics included studies of physical traits and criminality, social traits and pathology, mental traits, and racial and ethnic traits. Many of these studies were later discounted as pseudoscience, strongly influenced by ethnocentric biases of the investigators. The pursuit of eugenic goals eventually led to legal sterilizations, restrictive immigration laws, and to obscene violations of human rights and barbarous excesses of racism and genocide in Nazi Germany. Today, eugenic principles are no longer imposed by law, but they remain alive in the choices that humans make about transmitting their genes to their offspring.
Gene
Authors: Elston Robert C.
Published: September 2014

A commonly accepted definition of a gene is a combination of segments of DNA that together code for a functional unit; but the word is used with other meanings, better differentiated as “locus” and “allele”, depending on the nationality, age, and specific genetic discipline of the writer.
Linkage Disequilibrium
Authors: Chakravarti Aravinda
Published: September 2014

The nonrandom association between alleles at different loci is termed linkage disequilibrium. This note discusses the origin of this concept, its theoretical genetic basis, its estimation and tests of significance, and applications. Linkage disequilibrium studies are critical in human disease gene mapping and is now a major area of research with the availability of variation of the human genome sequence.
Linkage Information Content
Authors: Guo Xiuqing
Published: September 2014

The probability of knowing IBD sharing status of relative pairs at polymorphic genetic markers is an important factor for a model‐free linkage study. Here, Linkage Information Content (LIC) value is introduced, which measures the informativeness of a marker for identity‐by‐descent (IBD) sharing status of relative pairs. LIC value is independent of the mode of inheritance of the disease, but is specific to the type of relative pairs used.
Polymorphism Information Content
Authors: Elston Robert C.
Published: September 2014

The polymorphism information content of a marker is the probability that the marker genotype of the offspring of a heterozygous parent affected with a dominant disease allows one to deduce which marker allele the offspring inherited from the parent. It is a measure of a marker's usefulness for linkage analysis.
Privacy in Genetic Studies
Authors: Pelias Mary Kay
Published: September 2014

The 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.
Bioinformatics in Functional Genomics
Authors: Kohane Isaac S. , Butte Atul
Published: September 2014

Databases of DNA sequences, physical maps, genetic maps, gene polymorphisms, protein structures, and gene expression have produced a need for systematic quantitative analysis that goes by the name of bioinformatics. To manage these large datasets in the domain of functional genomics requires algorithmic implementation on computers. Functional genomics will continue to be hypothesis‐driven and hypothesis‐generating biological research. Because the datasets are of high dimensionality, involving relatively small numbers of cases, a large number of solutions can explain the data; the computational techniques to unravel these involve both supervised and unsupervised learning.
Animal Screening Systems
Authors: Haseman Joseph K.
Published: September 2014

Animal screening systems are experimental protocols designed primarily to screen substances in laboratory animals for possible adverse effects in humans. These experiments include long‐term rodent toxicity/carcinogenicity bioassays and shorter duration studies that focus on reproductive and developmental toxicology endpoints. These experiments are the first step of a broader risk assessment process to determine the magnitude of human risk associated with exposure to a test substance. Statistical issues that arise in these studies include (i) selection of the number, magnitude, and spacing of doses; (ii) use of statistical methodology that adjusts for potentially confounding factors such as survival, body weight, and litter effects; (iii) multiple comparison adjustments for the large number of variables evaluated; (iv) use of historical control data; and (v) development of appropriate statistical methodology to permit extrapolation of experimental results from high to low doses and from species to species.
Extrapolation, Low Dose
Authors: Gaylor David W.
Published: September 2014

While many substances are toxic to humans at high doses, naturally there is concern about the safety of long‐term exposure to much lower doses of certain materials. Such effects are difficult to observe directly and the usual approach is to expose animals to high doses of the substance, establish a dose–response relation, and use this to extrapolate much lower doses. The use of maximum likelihood to estimate extreme quantiles of the tolerance distribution is explained. Methods for measuring carcinogenic potency, such as the dose causing a tumor in 50% of animals, TD50, and the maximum tolerable dose (MTD) are introduced. For noncancer outcomes, the concepts of acceptable daily intake (ADI) and no observed adverse effect level (NOAEL) are also mentioned.
Infectivity Titration
Authors: Armitage Peter
Published: September 2014

The virulence of a suspension of particles, typically viruses or bacteria, can be assayed by administering several dilutions of the suspension into host organisms and observing the number of hosts that become infected at each dilution. The usual analysis uses likelihood methods to estimate a parameter that depends on both the density of particles in the suspension and on the chance that each particle will cause an infection. In the simplest model, each particle acts independently of the others and each gives rise to an infection with the same probabilityp. In these circumstances, the probability that a host becomes infected can be found using the Poisson distribution. More elaborate models allowpto vary from host to host according to some distribution. Methods for detecting suchhost variabilityare available. Some discussion is given to the effects of failure of the assumption of independence.
Mutagenicity Study
Authors: Piegorsch Walter W.
Published: September 2014

Biometric methods are discussed for analyzing data from bioassays of mutagenic response. Emphasis is placed on the classic microbial system in environmental mutagenesis: the Ames/Salmonellamicrosome assay. The assay produces count data to quantify the mutational response; statistical issues of interest when analyzing such data include testing for extra‐Poisson variability, trend testing for a dose response, and trend testing under a nonmonotone dose response.
Parallel‐Line Assay
Authors: Redmond Carol K.
Published: September 2014

Parallel‐line assays are the most common form of indirect analytical assays. The aim is to estimate the potency of substance under test relative to the potency of a standard. The test and standard preparations are administered at several dose levels. The key assumption is that the expected response for 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: ρ is known as the relative potency. It is further assumed that the expected response is linearly related to the logarithm of the dose. If this obtains, the graphs of expected response against log dose for the test and standard preparations will give parallel lines. The response can be continuous, in which case estimation is through a linear model, or quantal, in which case analysis usually assumes a logistic model. The estimator of ρ is defined in terms of ratios of regression coefficients and interval estimates are often obtained by use of Fieller's theorem.
Stochastic approximation with biomedical applications
Authors: Gross Alan J. , C. McLean David
Published: September 2014

Stochastic approximation is a method for approximating the root of the equationM(θ) = m0, when the functionM(.) is not directly observable but can be cast as the expectation of a suitably defined regression function. The method was introduced by Robbins and Munro in 1951 and has since been extensively refined and extended. It has found application in many areas of biostatistics and in the application of neural networks.
Analysis of Variance for Longitudinal Data
Authors: Dunn Graham
Published: September 2014

This entry describes how traditional analysis of variance (ANOVA) techniques might be adapted for use in the analysis of serial measurements. Typically, one might recognize data coming from a longitudinal study as coming from a nested or split‐plot design with serial or repeated measurements (subplots) being nested within each of the subjects (plots). The difference between the results of a typical split‐plot experiment and that involving repeated measurements arises from the fact that the latter are likely to be serially correlated. Methods of adapting ANOVA methods to allow for serial correlation (i.e. the Greenhouse–Geisser correction) are described, together with suggestions for alternative data analytic approaches.
Broadband Smoothing
Authors: Castiglioni Paolo
Published: September 2014

Broadband smoothing is a procedure to reduce the estimation variance of fast Fourier transform (FFT) power spectra, which results in estimation variance and frequency resolution not constant over the frequency axis, as in conventionally smoothed spectra.
Generalized Linear Models for Longitudinal Data
Authors: Zeger Scott L. , Diggle Peter J. , Huang W.
Published: September 2014

The analysis of longitudinal data needs to take into account the correlations between the repeated measures of the response variable to draw valid and efficient inferences about parameters of scientific interest. There are a variety of approaches that can be taken depending on what is the target of the estimation process. This article outlines marginal models, random effects models and transition models.
Linear Mixed Effects Models for Longitudinal Data
Authors: Pinheiro José C.
Published: September 2014

Linear mixed‐effects (LME) models have become a popular tool for analyzing longitudinal data that arise in areas as diverse as clinical trials, epidemiology, agriculture, economics, and geophysics. The increasing popularity of these models is explained by the flexibility they offer in modeling the within‐subject correlation often present in longitudinal data, by the handling of both balanced and unbalanced data, and by the availability of reliable and efficient software for fitting them. This paper describes a general LME model for longitudinal Gaussian data, presenting the model definition and its underlying assumptions. Maximum likelihood and restricted maximum likelihood estimation methods are discussed, as well as asymptotic inference methods. Available software for fitting and analyzing LME models are briefly reviewed. The model and methods are illustrated through an example from a longitudinal growth study.
Nonlinear Growth Curve
Authors: Crowder M. J.
Published: September 2014

Growth curves are important in several areas of medicine, for example, the monitoring of height or weight of children over several years. In this article, growth curves in which the parameters defining the curve occur nonlinearly are considered.
Population Growth Models
Authors: Diamond Ian
Published: September 2014

Models for forecasting the growth of human populations have been of interest for at least two hundred years. In this article, several models are described including the logistic curve, and ARMA and ARIMA time series models.
Window Estimate
Authors: Davies Neville
Published: September 2014

Window estimate is a term used both in frequency domain estimation in time series analysis and in kernel density estimation. In this article, both situations are considered.
Zero Padding
Authors: Castiglioni Paolo
Published: September 2014

Zero‐padding is a procedure by which the length of a time series is extended by the addition of zeros, enabling the power spectrum to be evaluated over a fuller range of frequencies.
Nonparametric Regression Analysis of Longitudinal Data
Authors: Wang J. ‐L.
Published: September 2014

Nonparametric approaches have recently emerged as a flexible way to model longitudinal data. This entry reviews some of the common nonparametric approaches to incorporate time and other covariate effects for longitudinally observed response data. Smoothing procedures are invoked to estimate the associated nonparametric functions, but the choice of smoothers can vary and is often subjective. Both fixed and random effects may be included for vector or longitudinal covariates. A closely related type of data is functional data, where the prevailing approaches to model random effects are through functional principal components analysis and B‐splines. Related semiparametric regression models also play an increasingly important role.
General Practice
Authors: Peters T. J.
Published: September 2014

Research in general practice is principally focused on common, chronic medical conditions, covers organizational as well as clinical issues and is a multidisciplinary endeavor. At the same time, conditions seen frequently in particular specialities are relatively uncommon, and this has implications for the statistical performance of diagnostic and screening tests. Generally, biostatistical methods in general practice are characterized by sources of variation operating at various levels, including the primary care center, practitioner, and individual. Both study design and data analysis must make reasonable allowance for this, in particular, in experimental studies where the unit of randomization is a group of individuals. Practical, ethical, and financial constraints mean that a variety of study designs will remain valuable in general practice research, but the central role of the pragmatic randomized trial will continue.
Midwifery, Obstetrics and Neonatology
Authors: Macfarlane Alison , Elbourne Diana
Published: September 2014

Midwifery is the profession responsible for care in normal pregnancy and labor, obstetrics is the profession that deals with pregnancies with clinical complications, and neonatology is concerned with care for sick newborn babies. Members of these professions and others concerned with evaluating the care they give in the context of clinical, environmental, and genetic factors have measured the outcome in terms of maternal and infant mortality and morbidity and used a range of descriptive, analytical, and experimental approaches to do so. From the mid‐nineteenth century onwards, routine vital statistics have been used to describe trends and variations. In latter half of the twentieth century, these were analyzed by correlation, regression, and later multivariate analysis, with techniques being developed for record linkage. Clinicians in these profession and associated researchers were among the first to use randomized trials to evaluate care and meta‐analyses to synthesis the results of trials.
Ophthalmology
Authors: Marsh Marta J. , Gange Stephen J.
Published: September 2014

The field of ophthalmology and vision research provides a rich source for both the application and the development of statistical theory and methodology, particularly with correlated data, due to the paired nature of much ocular data. This article begins with a brief history of ophthalmology, followed by a section describing the variety of types of data available in the field of ophthalmology and optometry. Finally, a section describing the multitude of common statistical issues that anyone analyzing ophthalmologic data must consider concludes this article, with emphasis on methods for the appropriate analysis of paired (correlated) data.
Otorhinolaryngology
Authors: Zielhuis Gerhard A. , Straatman Huub , M. Schilder Anne G.
Published: September 2014

Biostatisticians entering the field of ORL are confronted with large numbers of patients and a diversity of diseases and treatment options. Most of the statistical procedures used are similar to those used in other branches of medicine. Special features are the potential of each patient having two ears, the fluctuating course of many of the common ORL diseases, and the diversity of management options.
Psychiatry
Authors: Everitt Brian S.
Published: September 2014

Psychiatry is the medical speciality that deals with mental disorders. In early medieval times, beating, incarceration, and restraint were the “treatments” endured by the majority of the mentally ill, and a more enlightened perspective has evolved only slowly over the intervening 600 years. In the twentieth century, psychiatry struggled to attain scientific respectability by making quantitative observations on mentally ill patients, and subjecting these observations to a variety of statistical methodology ranging from simple descriptive techniques to complex multivariate analyses.
Radiation
Authors: Darby Sarah C. , Gilbert Ethel S.
Published: September 2014

Biostatistical applications in the area of ionizing radiation are primarily directed towards obtaining quantitative risk estimates for various cancers following radiation exposure. The relevant data arise mostly from observational studies on humans. The major populations studied include the survivors of the atomic bombings of Hiroshima and Nagasaki, patients given radiotherapy for a variety of malignant and other conditions, workers in the nuclear industry, persons exposed in countries of the former Soviet Union, uranium miners, and populations living in areas where residential radon concentrations are high. Experimental animal data are also available. The large quantity of information and the availability of quantitative dose estimates in many studies have enabled the development of a variety of complex risk models. Recent work has included bringing together data from several different studies, usually with the objective of producing more precise estimates of risk, and the development and application of methods that account for uncertainties in the estimates of radiation dose.
Axes in Multivariate Analysis
Authors: Krzanowski W.J.
Published: September 2014

Visualization of data is valuable in statistical analysis to reveal features that are difficult to detect in numerical summaries. When
p
variables are measured on
n
subjects it can be difficult to graphically depict patterns due to limits of dimensionality. Multivariate graphical analysis identified directions that can be projected into subspaces to highlight sample features. Projecting the original data into a subspace generated from several orthogonal linear combinations allows for low‐dimensional approximation to the full data representation.
G
uttman Scale
Authors: Sullivan Lisa M.
Published: September 2014

In a Guttman scale, a set of items are developed to measure a latent construct or attribute. The items are ordered hierarchically such that an affirmative response to one implies affirmative responses to each of the items preceding it. The sum of affirmative responses represents the subject's level of the attribute under study.
Independence of a Set of Variables, Tests of
Authors: Krzanowski W. J.
Published: September 2014

If
p
variables have been divided a priori into distinct sets, then within each set, individual values for each variable may be highly correlated; however, variables in different sets could be treated as independent. Testing independence of such sets of variables is considered through development of a likelihood ratio test. Derivation from this likelihood ratio test to canonical correlation and a union–intersection test is also shown.
L
ikert Scale
Authors: Dukes Kimberly A.
Published: September 2014

A Likert scale is computed by summing responses over several items hypothesized to measure the same latent variable or construct. Likert scales are often measured on Likert response formats, which often have three, four, or five response options and represent degrees of endorsement of items. When creating a Likert sale, usually each item is equally weighted, and assumed to have a linear relationship to the overall scale score.
Multiple Endpoints, Multivariate Global Tests
Authors: B. D'agostino Ralph , Russell Heidy K.
Published: September 2014

In clinical trials, multiple endpoints are often used to investigate a treatment effect. The statistical procedures available to analyze multiple endpoints fall into two categories: multivariate global methods and endpoint‐specific orPlevel methods. Multivariate global methods are used to make an omnibus assessment of the efficacy of the treatments, providing an overallPvalue to reflect whether there are any differences among the treatments on several correlated endpoints. Within multivariate global methods there are two types of tests. The first type involves combining multiple endpoints into a single test statistic, which provides an overall statement to declare whether there are any significant differences among the treatments. The second method arises from Bonferroni‐type adjustments. They adjust the univariate observedPvalues of the individual tests to maintain the familywise type I error rate at a prescribed level, and they provide an overall probability for the trial.
Multivariate Distributions, Overview
Authors: Jensen D. R.
Published: September 2014

Multivariate distributions arise throughout statistics and applied probability and they are defined on finite‐dimensional spaces. They serve as probabilistic models for dependent outcomes of random experiments. Biometric data typically comprises observations on multiple characteristics for each experimental subject, and joint distributions are central to the modeling and analyses of such data. From multivariate distributions, the distributions of various sample statistics of note in statistical inference can be derived. Multivariate distributions also characterize the behavior of stochastic processes through properties of their finite‐dimensional projections.
Multivariate t Distribution: Introduction
Authors: Jensen D. R.
Published: September 2014

Multivariate versions of the Student'stdistribution derive through Studentization from multivariate normal models. Multivariatetdistributions are useful to model errors of a random experiment offering greater flexibility and heavier tails than multivariate normal models. There are two types of multivariatetdistributions. Type I distributions are derived by scaling each component of a normal vector by a single random scalar. Type II distributions involve separate scalings by elements of a further random vector, itself having a joint multivariate distribution.
O
ptres Rotation
Authors: Jackson J. Edward
Published: September 2014

Optres Rotation
is a three‐stage procedure for obtaining
oblique rotations
used to transform vectors associated with
principal component analysis
or
factor analysis
to
simple structure
. The first stage will be an
orthogonal
rotation such as
Varimax
. The second will be an oblique rotation such as
Promax
. The third rotation requires, for each vector, a specification of which coefficients are to be maximized and which are to be minimized. A numerical example is included.
Trees, Probabilistic Functional
Authors: Gama Joäo
Published: September 2014

Decision Trees are one of most used models for prediction tasks in machine learning, data mining, and statistics. The representation language of a standard decision tree uses splitting tests based on a single input variable and constants at tree leaves. The most recent advances in learning decision trees from data involve the use of splitting tests based on combinations of the input variables and functions at tree leaves. These types of models are denominatedfunctional trees. In this article, we present the issues and actual trends in learning functional trees from data.
Simplex Models
Authors: Browne Michael W.
Published: September 2014

Simplex models are used for the analysis of relationships among variables that can be arranged according to a logical ordering. Most methods for fitting the simplex require a prior knowledge of the ordering of the variables, although there are two‐stage procedures where the ordering is estimated in the first stage and procedures that do not require a prior specification. There are two types, a perfect simplex where no allowance is made for error of measurement and a quasi‐simplex where measurement error is taken into account.
Software Packages for Data Mining
Authors: Haughton Dominique
Published: September 2014

The term
data mining
refers to the identification—within a typically large database—of new, valid, and interesting patterns. While data mining has become most popular in the context of, for example, database marketing, most of the methods under the data mining umbrella have been widely applied in biostatistics. We describe which main applications of data mining have arisen in biostatistics, and introduce the reader to some of the available data mining software packages with a reference to biostatistical needs.
Double Sampling
Authors: R. S. Rao Poduri S.
Published: September 2014

Double sampling is of practical importance for the estimation of population characteristics from sample surveys and for other purposes. This approach for the estimation of stratum weights and the mean of the auxiliary variable for ratio and regression estimators is presented. Its applications for the ratio estimation with subsampling the nonrespondents in a survey, for determining sample sizes in analytical surveys, and for multistage and successive sampling are also described.
Establishment Surveys With Population Survey–Generated Sampling Frames
Authors: Sirken Monroe , Shimizu Iris
Published: September 2014

When stand‐alone sampling frames list all establishments and size measures, the Hansen–Hurwitz (HH) pps estimator is generally used to estimate the volume of transactions between establishments and households. The network sampling (NS) version of the HH estimator depends on a population‐survey‐generated establishment frame, which lists establishments that have transactions with households in a population sample survey and the number of transactions that establishments have with survey households. The NS estimator is a competitor of the HH estimator whenever stand‐alone frames of good quality are unavailable and, as this article indicates, the NS estimator may be competitive with the HH estimator when stand‐alone and population‐survey‐generated frames are flawless.
Probability Sampling
Authors: Hsia Jason
Published: September 2014

Probability sampling is a process used to select a sample from a defined population with the characteristic that every element of the population has a known, nonzero probability of being included in the sample. In probability sampling, we select the sample by a random mechanism under which each element of the population receives this known selection probability.
Sample Size Adequacy in Surveys
Authors: Penne Michael A. , Levy Paul S.
Published: September 2014

This entry presents concepts involved in determination of sample size adequacy in sample surveys. There is a broad discussion of issues such as stratification, sampling without replacement, finite population corrections, sampling of clusters, sampling in more than one stage, and unequal weighting of observations that have a profound effect on the distribution of estimates and on the sample sizes necessary to construct reliable estimates. We include formulas for determining sample size adequacy for some of the most widely used sample designs and some practical guidelines for statistical consulting on this issue. A set of references to texts and specific articles is included.
Errors in the Measurement of Covariates
Authors: Yi Grace Y. , Cook Richard J.
Published: September 2014

Measurement or misclassification errors in covariates lead to biased estimates of regression parameters. Corrections typically, require addition date such as replication of errors observations, validation through direct observation of correct values, or the introduction of further “instrument” variables. Methods described include regression calibration, simulation extrapolation, and the use of estimating equations. An example is based on a case–control study of cervical cancer.