bluuebunny/crossref_metadata_2025 huggingface dataset:
H
aenszel,
W
illiam
M
Authors: Levy Paul S.
Published: September 2014

Born 1910; died 1998. Although William M. Haenszel is probably best known as the name to the right of the hyphen on the Mantel–Haenszel test and Mantel–Haenszel odds ratio, he made other equally important contributions throughout a very long and productive career as a biostatistician and epidemiologist. Of particular importance was his leadership in establishing and maintaining the population‐based cancer registry known as
SEER
(for
S
urveillance
E
pidemiology and
E
nd
R
esults); his comparative studies of cancer occurrence in foreign‐born and native‐born Japanese Americans; and his use and advocacy of biomarkers and pathological findings in studies of cancer etiology, especially those involving gastric and large bowel cancer. His work from the 1950s to 1970s at the National Cancer Institute was at the cutting edge of chronic disease epidemiology, and greatly enriched that field both substantively and methodologically.
I
rwin,
J
oseph
O
scar
Authors: Armitage Peter
Published: September 2014

After periods of work under Karl Pearson and R. A. Fisher, Oscar Irwin joined the staff of the Medical Research Council in 1931 and thereafter became the leading mathematical statistician in British medical research. He had a wide range of theoretical interests, including the use of the characteristic function, the development of the exact test for the 2 × 2 table, and the Waring distributions. His biometric interests included bioassay, factor analysis, accident proneness, and carcinogenicity experiments.
P
earl,
R
aymond
Authors: Dixon Dennis O.
Published: September 2014

Raymond Pearl (1879–1940) worked in Michigan as a biologist until 1905. After a year's study in London with Karl Pearson, he returned to the US and increasingly worked in areas of human biology. In 1918, he became the first Professor of Biometry and Vital Statistics in the Johns Hopkins School of Hygiene and Public Health, Baltimore, and started a prolific period of research in a wide range of human biology, including public health, eugenics and heredity. A major line of research was human population growth.
P
eirce,
C
harles
S
anders
Authors: Folks J. Leroy
Published: September 2014

Peirce (1839‐1914) was one of the greatest of American philosophers and wrote on a wide range of scientific and other topics. He wrote on randomness and the need for experimental randomization, and on scientific method, statistical inference and hypothesis testing.
Popper, Karl R
Authors: Fagot‐Largeault A.
Published: September 2014

Born 1902; died 1994. Famous as a philosopher of science, Popper maintained that scientific truth is not manifest, but that indeed that scientific theories are conjectures open to refutation or falsification. Popper controversially denounced inductive reasoning—validity of a scientific theory from empirical observations to general regularities—insisting instead that scientific theories should be open to testing. As a theory passes more severe tests, it becomes more highly “corroborated”. He called this approach
hypothetico‐deductive
. ‘Realism and the Aim of Science’ (1982) is his work most relevant to biostatistics, re‐examining in detail problems of truth, corroboration, and probability.
Y
ule,
G
eorge
U
dny
Authors: F. Edwards A. W.
Published: September 2014

G.U. Yule (1871–1951) worked from 1892 at University College London under Karl Pearson, and from 1912 to 1931 at Cambridge. He published
An Introduction to the Theory of Statistics
in 1911, the 11th and subsequent editions of which were coauthored by M.G. Kendall. He contributed to the theory of regression, correlation, genetics, epidemiology, and time series, and developed a coefficient of association for 2 × 2 tables. He described what is now usually known as Simpson's paradox for association in a 2 × 2 × 2 tables. He introduced the simple birth process in the study of evolution.
Binomial Distribution
Authors: Wypij David
Published: September 2014

The binomial distribution refers to the number of successes in
n
independent trials with a common success probability
p
in each. With large
n
, the distribution is approximated by the normal, and with large
n
and small
p
, it is approximated by the Poisson. This article reviews its properties and discusses point estimation, confidence interval estimation, and hypothesis testing for the parameter
p
. Extensions are also briefly considered.
Chi‐Squared Tests ‐ Further Developments
Authors: Koehler Kenneth
Published: September 2014

A chi‐square test investigates whether observed data are consistent with a proposed probability model. Best‐known chi‐square statistics are the Pearson statistic and the likelihood‐ratio statistic. This article also mentions various other statistics that have limiting chi‐square distributions, all of which are special cases of the power divergence statistic. The article begins by introducing simple goodness‐of‐fit tests, then discusses a variety of chi‐square statistics. Chi‐square tests are then presented that apply with fixed intervals or with random intervals. Applications are then discussed for categorical data, such as testing independence. Extensions are discussed for small samples and sparse tables, and for correlated responses and complex surveys.
Contingency tables
Authors: E. Altham P. M.
Published: September 2014

Contingency tables give counts for cross classifications of categorical variables. This article illustrates two‐dimensional tables (two variables). It then discusses the hypothesis of independence and presents chi‐squared tests. An example of quasi‐independence is presented, and small‐sample tests are mentioned. Extensions to multiway contingency tables and the relevant hypotheses for such tables are also discussed. Loglinear models describe the hypotheses, and there are corresponding logistic regression models when one of the variables is a binary response variable.
Iterative Proportional Fitting
Authors: Speed Terry P.
Published: September 2014

Iterative proportional fitting, also known as iterative proportional scaling, is an algorithm for constructing tables of numbers satisfying certain constraints. In its simplest form, the algorithm enables one to construct two‐way tables with specified row and column margins and prescribed association, measured by a family of cross‐ratios. The algorithm can be used to obtain maximum likelihood estimates of table entries under hierarchical loglinear models, and it can be used to adjust counts in survey sampling, where it is known as raking. Natural extensions exist to three and higher‐way tables, and to more general exponential families.
Logistic Regression, Conditional
Authors: W. Hosmer David , Lemeshow Stanley
Published: September 2014

This section describes the conditional logistic regression model. This model is appropriate when one wishes to model a binary outcome variable with matched or highly stratified data and when one is not interested in matched sets or stratum effects. The resulting fitted model may be used to estimate odds ratios in the same manner as is done with the usual logistic regression model. Examples of estimation and interpretation are given.
Mantel–Haenszel Methods
Authors: Landis J. Richard , Sharp Tonya J. , Kuritz Stephen J. , Koch Gary G.
Published: September 2014

This article provides an overview of generalized Mantel–Haenszel (MH) methods for the analysis of categorical data from factor–response and repeated measures study designs. These methods are illustrated using data from two different clinical research studies, investigating treatment differences within several different sets of 2 × 2 tables in a clinical trial, and within‐subject differences in an ordinal response across ordinal factor levels within a repeated measures design. The underlying multiple hypergeometric probability structure, based on a randomization model framework for hypothesis testing, is summarized for testing alternative hypotheses of 1) general association; 2) mean responses differ; and 3) linear trend in mean responses. These generalized MH methods can all be implemented directly within SAS and StatXact, with appropriate stratification and choice of scores.
Matched Pairs with Categorical Data
Authors: Davis Charles S.
Published: September 2014

Matched pairs with categorical data arise when two measurements of the same categorical variable are obtained from each experimental unit. Such data are generally displayed in a two‐way contingency table. When the variable of interest is binary (dichotomous), marginal homogeneity can be tested using McNemar's test or the exact binomial one‐sample test. For a polytomous variable, marginal homogeneity can be tested using Stuart's test, and by tests proposed by Bhapkar and Madansky. The likelihood ratio test of marginal homogeneity conditional on the assumption of quasi‐symmetry can also be used. The hypothesis of symmetry for a polytomous variable can be tested using Bowker's test and the likelihood ratio test. For ordered categorical variables, one degree of freedom tests of marginal homogeneity can be carried out using tests analogous to those of Stuart and Bhapkar. Another approach to testing marginal homogeneity for ordered classifications is based on the conditional symmetry loglinear model.
Proportions, Inferences and Comparisons
Authors: Connor Jason T. , Imrey Peter B.
Published: September 2014

This article selectively reviews asymptotic, exact, and Bayesian methods for inference about one proportion, or for comparing two proportions from separate samples. For a single proportion, interval estimation is emphasized, and coverage properties of Wald, Jeffreys, Wilson, Agresti–Coull, and Clopper–Pearson (exact) confidence intervals are compared. Hypothesis testing is also discussed, primarily in the context of inverting interval estimates. For comparing proportions between two samples, we consider tests including the power‐divergence family (which incorporates all asymptotic tests commonly used); both conditional and unconditional exact tests; Bayesian tests; and several interval estimates. Some general aspects of power computation and sample size determination are discussed briefly in both contexts.
Quasi‐Independence with Biomedical Examples
Authors: Mcdonald John W.
Published: September 2014

The quasi‐independence model is a generalization of the model of independence for a two‐dimensional contingency table. It states that a particular subset of cells satisfies the independence structure whereby the cell expected count is a product of a row effect and a column effect. It is often used for square tables (number of rows equal number of columns), the relevant subset being all cells not falling on the main diagonal. This article also discusses connections with loglinear models, extension to multidimensional tables, inference and testing of model fit, and applications and relationships with other models.
Quasi‐symmetry
Authors: Hatzinger Reinhold
Published: September 2014

In its simplest form, quasi‐symmetry is a model for square contingency tables that states that certain odds ratios are symmetric around the main diagonal. A special case is complete symmetry, by which probabilities themselves are symmetric around the main diagonal. Another special case is quasi‐independence, by which the odds ratios equal 1. This is equivalent to quasi‐symmetry for 3 × 3 tables. This article discusses its multiplicative formula, gives an example, discusses generalizations to higher‐dimensional tables, discusses tests related to complete symmetry and marginal symmetry, and mentions related models.
Two‐By‐Two Table: Overview
Authors: Haber Michael
Published: September 2014

This article summarizes the most commonly used statistical techniques for analyzing two by two frequency tables. It presents methods for the estimation of measures of association (risk ratios, risk differences, odds ratios) and agreement (kappa). Tests of hypotheses are described for large samples (based on asymptotic theory) and for small samples (exact tests). Both conditional (Fisher's exact test) and unconditional exact tests are discussed, and the controversy regarding the use of conditional versus unconditional tests is summarized. The article advocates the use of the mid
p
‐value exact test for small sample sizes. Methods for the determination of sample sizes for tests on two by two tables are also described.
Decision Analysis in Diagnosis and Treatment Choice
Authors: A. Tosteson Anna N.
Published: September 2014

Decision analysis, a quantitative approach to decision making under conditions of uncertainty, is introduced and its use in clinical decision making is described. Decision analysis identifies the optimal course of action as that which maximizes (or minimizes) the expected value of the outcome as specified by the decision maker. Through sensitivity analysis, the parameters that affect the optimal course of action are identified. The expected value of clinical information and perfect information are defined in the context of a clinical example.
Observer Reliability and Agreement
Authors: Vet H.
Published: September 2014

The termsobserver reliabilityandobserver agreementrepresent different concepts. Reliability coefficients express the ability to differentiate between subjects. Agreement parameters determine whether the same value is achieved if a measurement is repeated. Parameters for reliability are ICCs for measurements on an interval scale. Parameters for agreement are kappa for measurements on a nominal scale, weighted kappa for an ordinal scale, and levels of agreement for measurements on an interval scale.

Assessing observer reliability and agreement is essential for the interpretation of clinical observations in research and medical practice. Moreover, only if the sources of measurement errors are known, can they be anticipated, thereby improving the quality of measurements.
Computerized Therapeutic Decision Support
Authors: Pifer Eric , Weiner M. G. , Williams Sankey V.
Published: September 2014

The increasing breadth and depth of clinical information stored electronically has enabled computerized therapeutic decision support systems to provide appropriate and timely advice to providers, which that is tailored specifically to a patient's clinical status. This chapter provides a taxonomy for therapeutic decision support systems in terms of the goals of the advice, the mechanism in which the advice is given, the information needed to generate the advice, and the underlying reasoning used. Examples of applications of therapeutic decision support systems are provided with an assessment of their utility within the complex processes of clinical care.
Clinical Trials Protocols
Authors: Collins Joseph F.
Published: September 2014

A system is chaotic if it is governed by a set of deterministic equations, but displays erratic, apparently random, behavior. The mathematical structure may be the iteration of linked difference equations in discrete time, or the evolution of a set of linked differential equations in continuous time. Stochastic interpretations, especially those involving time series analysis, are described.
Software for Clinical Trials
Authors: Arena Vincent C. , Rockette Howard E.
Published: September 2014

This chapter provides a comprehensive overview of software that is relevant to the design, management, and analysis of clinical trials. Guidance is provided on selecting programs appropriate to the needs of the researcher. There are useful suggestions on keeping software up to date. An extensive table is included referencing most of the available software packages with brief descriptions of the features most relevant to clinical trials, vendor names, contact information, and web URLs.
Sample Size Determination in Survival Analysis
Authors: Collett D.
Published: September 2014

This article outlines how the sample size for a study can be determined when the response variable is a survival time. This is a two‐stage process in which the number of events required to meet prespecified power requirements is first determined. The occurrence of censoring generally means that not everyone recruited to a study will experience the end point, and so the next step is to calculate the number of individuals who need to be recruited to give the required number of events. This number will depend on parameters such as the anticipated recruitment rate and the follow‐up period. The procedure is illustrated using a numerical example.
Group Randomized Trials: Introduction
Authors: Murray David M.
Published: September 2014

Group‐randomized trials (GRTs) are comparative studies in which investigators randomize identifiable groups to conditions and observe members of those groups to assess the effects of an intervention. They face special problems of design and analysis that must be considered to ensure adequate power and valid results. The most critical problem is that observations taken on members of identifiable groups tend to be positively correlated, thereby violating the independence of errors assumption underlying the common analytic methods. Mixed‐model regression methods, generalized estimating equations, and randomization‐based methods can provide solutions to this problem if used properly and under the right circumstances. This article provides a brief review of these issues.
Artificial Intelligence
Authors: Hand David J.
Published: September 2014

Artificial intelligence (AI) has been defined as the study of artificial systems that exhibit intelligent behavior. Research in AI is aimed either towards yielding greater understanding of how naturally intelligent systems, like human brains, function (i.e. a subdomain of cognitive psychology) or at building systems like computer programs or robots, say, that behave in apparently intelligent ways (i.e. a subdomain of engineering). In this overview, some important facets of AI research are examined.
Confidentiality and Computers
Authors: Maindonald John H. , Stott Helen P.
Published: September 2014

Computer systems that hold data of a private or confidential nature should be designed so that data are “disclosed only to authorized persons, entities and processes at authorized times and in the authorized manner” 24. Rights of privacy and confidentiality may be enshrined in legislation. Such rights are typically not absolute; they may, especially where data is required for research purposes, be weighed against a wider public interest. Demands for privacy and confidentiality impose responsibilities on designers and managers of computer systems that store sensitive data. Advances in computer technology, and especially the use of the internet, have created huge security challenges.
Floating Point Arithmetic
Authors: Maindonald John H.
Published: September 2014

Floating point number representations are a response to the demand to store and manipulate in a computer, using a fixed number of digits, numbers that range widely in magnitude. The consequent finite precision of floating point arithmetic has large implications for practical computation, of a kind that this article explores.
Numerical Analysis
Authors: Smyth Gordon K.
Published: September 2014

Numerical analysis is concerned with the accurate and efficient evaluation of mathematical expressions, especially on computers with floating point arithmetic. This article discusses the fundamental ideas of numerical accuracy including rounding error, approximation error, conditioning, and stability. The problem of computing a sample variance is considered as a simple illustrative example. The article goes on to describe briefly a number of key topics in numerical analysis, which are treated in more depth in separate articles, including linear equations and matrix computations, optimization and nonlinear equations, interpolation and approximation, and numerical integration. Brief pointers are given to available software.
Software Reliability
Authors: Dalal S. R. , Lyu M. R. , Mallows C. L.
Published: September 2014

The demand for complex software systems has increased more rapidly than the ability to design, implement, test, and maintain them, and the reliability of software systems has become a major concern for our modern society. In the last decade of the twentieth century, many reported system outages or machine crashes were traced down to computer software failures. Consequently, recent literature is replete with horror stories regarding software problems. This entry focuses on software reliability models and measurements.
Statistical Map
Authors: Lawson A. B.
Published: September 2014

The concept of a map is defined, and the area of statistical map construction and related interpretational issues are discussed.
Software for Sample Survey Data
Authors: Carlson Barbara Lepidus
Published: September 2014

In sample surveys, certain methods are used to improve the precision and control the costs of survey data collection. Among these methods are stratification, multistage sampling, clustering, and unequal probabilities of selection. These methods introduce a complexity to the analysis, which must be accounted for in order to produce unbiased estimates and their associated levels of precision. Not accounting for a complex sample design in the variance estimates can result in artificially small standard errors. This article provides a brief introduction to the impact these design complexities have on the sampling variance, and summarizes the characteristics and availability of software to carry out analysis on sample survey data while accounting for the design.
Partially Balanced Incomplete Block Design
Authors: Street D. J. , Street A. P.
Published: September 2014

This article provides a brief overview of the most common partially balanced incomplete block designs and discusses other designs to which they are related.
Bacterial Growth, Division, and Mutation
Authors: Gusev Y. , Armitage Peter
Published: September 2014

Models of cell growth and division in bacteria were considered more than 60 years ago. The evolution of models of bacterial growth was closely followed by the development of models of the cell cycle in budding and fission yeast, cell cultures, and mammalian cells. In some of these theoretical models, new mechanisms have been hypothesized, which later have been confirmed in experiments on spontaneous and induced mutations, signal transduction pathways, cell cycle regulation, and programmed cell death. Thus, for many years, modeling of the bacterial cell cycle has been a “proving ground” for refinement of theoretical models in cell biology. The major questions addressed in bacterial growth models have included: stochastic and deterministic models of cell population dynamics; different models of the cell cycle in individual cells, such as growth control, random transition, and mitotic clocks; generational dependence models; unequal cell‐division models; spontaneous mutation models and fluctuation tests.
G
alton–
W
atson Process: Introduction
Authors: Seneta E.
Published: September 2014

The simple branching process is a probability model describing the evolution through successive generations of the descendants of a single ancestor. If the mean number of offspring per individual is less than or equal to one, extinction occurs with probability one; otherwise there is a positive probability of survival.
Michaelis–Menten Equation
Authors: Dunn Graham
Published: September 2014

This article describes various approaches to the analysis of quantitative responses assumed to follow a hyperbolic dose–response relationship (the Michaelis–Menten equation) characterized by the values of two parameters: the Michaelis constant (the dose at half‐maximal response) and the asymptotic maximum response. Methods include diagnostic graphical representations, including the double reciprocal or Lineweaver–Burk plot, the Hanes plot and the Eadie–Hofstee plot. Curve‐fitting and parameter estimation is described using either least squares or maximum likelihood. The latter includes using a generalized linear model incorporating a reciprocal link function and the possibility of nonnormally distributed errors. Robust and distribution‐free estimation procedures, such Theil's method (known as the direct linear plot in biochemistry) are also covered.
Bias in Observational Studies
Authors: Hill Holly A. , Kleinbaum David G.
Published: September 2014

We consider three sources of bias affecting observational studies and ways to address them: selection bias, information bias, and confounding. We discuss these sources of bias with respect to cohort and case–control designs. Even if a study is internally valid and not subject to these sources of bias, a judgment must be made whether the results generalize to some other target population.
Case–Control Studies: Details
Authors: Wacholder Sholom , Hartge Patricia
Published: September 2014

We review design options for case–control studies and considerations for determining whether a case–control or cohort study is more appropriate for a given purpose. One issue is exposure data quality, which may be poorer in a case–control study than in a cohort study, and subject to differential error (recall bias). This must be weighed against the cost efficiencies and other advantages of the case–control design. We describe case and control selection, including the use of hospital‐based controls (secondary base studies) in which controls are not sampled from the population base that gave rise to cases. Other design options, including matching and the use of two‐phase studies are described, as well as approaches to analysis. It is pointed out that not only odds ratios, but also absolute risks, are estimable from population‐based case–control studies.
Cohort Study
Authors: Prentice Ross L.
Published: September 2014

A cohort study is based on a sample of individuals who are followed to determine disease events over a defined follow‐up period. Disease rates can be calculated from such a study as well as relative risks associated with measured exposures and other covariates. If the cohort identification precedes the follow‐up period, the study is prospective; otherwise, the cohort study is retrospective. Some advantages of the cohort design, compared to the case–control design, include the ability to study a variety of health outcomes and to obtain high quality exposure information before disease develops, thus avoiding recall bias. We review basic elements, including study design and sample size, cohort selection and follow‐up, covariate history ascertainment, study conduct, data analysis, effects of measurement error, and validation substudies. We discuss the cohort study in relation to case–control designs, nested case–control designs, and case–cohort studies, as well as the role of intervention studies versus observational cohort studies.
Correlational Study
Authors: Gail Mitchell H.
Published: September 2014

This article defines correlational study as an ecologic study and refers the reader to related article in the Encyclopedia of Biostatistics.
Ecologic Fallacy
Authors: Morgenstern Hal
Published: September 2014

The ecologic fallacy is the mistaken assumption that a statistical association observed between two ecologic group‐level variables is equal to the association between the corresponding variables at the individual level in the same population. This mistaken assumption often leads to incorrect inferences about the effects of exposures on disease occurrence.
Latent Period
Authors: Tan Wai‐yuan , Chen Chao W.
Published: September 2014

Different definitions of latency used in the biological and statistical literature have been reviewed and illustrated by examples. In particular, a distinction has been made between the biological latent period and the epidemiological latent period. This article also discusses the latency of infectious diseases, such as the incubation period of HIV and the time to tumors in cancers. Some stochastic models of the time period to cancer tumors and its applications have been discussed in some detail.
Odds
Authors: Gail Mitchell H.
Published: September 2014

If an event
E
has probability Pr(
E
), the odds of the event is defined as Pr(
E
)/[1 − Pr(
E
)].
Overmatching
Authors: Gail Mitchell H.
Published: September 2014

Overmatching refers to the unnecessary or inappropriate use of matching in a cohort or case–control study.
Population‐Based Study
Authors: Gail Mitchell H.
Published: September 2014

A population‐based study is a study of properties of a well‐defined population, such as individuals residing in a defined geographic area in a given time period.
Prevalent Case
Authors: Gail Mitchell H.
Published: September 2014

A prevalent case is a subject with a given disease or condition, who is alive in a defined population at a given time.
Relative Odds
Authors: Gail Mitchell H.
Published: September 2014

The relative odds, or odds ratio, is the ratio of the odds of disease in an exposed cohort divided by that in an unexposed cohort.
Confidentiality in Epidemiology
Authors: Leufkens Hubert G.
Published: September 2014

This entry describes the need for confidentiality in conducting clinical and epidemiologic research, particularly with respect to informational privacy of individuals. Ethical principles (including beneficence, nonmalfeasance, autonomy, and justice) are reviewed, and mention is made of the legal framework and guidelines to prevent the violation of confidentiality.
False Positive Rate
Authors: Walter Stephen D.
Published: September 2014

The false positive rate of a diagnostic or screening test is defined as the complement of specificity. Its appropriate denominator is specified.
Natural History Study of Prognosis
Authors: Cnaan Avital
Published: September 2014

Prognosis refers to the probable outcome of an attack of disease. The traditional definition would describe the evolution of disease in the absence of medical intervention, but nowadays we may also include the term “clinical course” to describe the natural history of a disease that has been affected by medical intervention. Various design considerations are reviewed for the study of natural history, including the use of cohort studies, the various biases that may affect such studies, and the definition of time zero as a clearly defined starting point. Other topics include the analysis of dichotomous outcomes, survival analysis and longitudinal data analysis. Combination of data from several studies, and situations involving multiple outcomes are also mentioned.
Predictive Values
Authors: Walter Stephen D.
Published: September 2014

This entry considers predictive values for a diagnostic test in terms of the disease prevalence, and test sensitivity and specificity. Clinical implications of high or low predictive values are discussed.
Screening Benefit, Evaluation of
Authors: Prorok Philip C.
Published: September 2014

Screening is the testing of apparently healthy individuals from a population for the purpose of separating them into groups with high and low probabilities of having a given disease such as cancer. This article examines various issues encountered in assessing the benefit of screening. The use of observational studies and possible involvement of selection bias is included, together with a discussion of lead time and length bias. Appropriate study endpoints are discussed. Various possibilities for randomized control trials are reviewed, and the use of observational designs to compare cancer incidence and mortality in a population before and after the introduction of a screening program is mentioned. The entry also lists important information requirements for the evaluation of screening programs.
Epidemic Models, Inference
Authors: Britton Tom
Published: September 2014

We consider stochastic models for the spread of an infectious disease in a population. We first focus on the simplest situation where individuals behave similarly and where there are no social structures. We derive estimates for model parameters including the basic reproduction number, both when data consists of the final size only, and where the outbreak is observed over time. We then look at household models where there is increased transmission within households and discuss other extensions and methodology for such situations, including Markov chain Monte Carlo (MCMC). Copyright © 2015 John Wiley & Sons, Ltd.
Epidemic Thresholds
Authors: Ball Frank
Published: September 2014

This article gives an overview of the threshold behavior of epidemic models, focusing primarily on stochastic closed population models. A key parameter is the reproduction numberR0of an epidemic. For a broad range of models, branching process theory is used to show that an epidemic initiated by a trace of infection in a large population can take off only ifR0is larger than one. These models assume either a homogeneous population or a stratified population in which all the subpopulations are large. Models for the spread of an epidemic among a population partitioned into a large number of small households, or among a spatially configured population, are also considered. In both of these casesR0no longer provides a good indicator as to whether an epidemic can take off. The article closes with a brief discussion of the threshold and long‐term behaviors of open population models, which incorporate demographic effects.
Nonresponse
Authors: Kviz F. J.
Published: September 2014

Nonresponse is a potential source of error in survey estimates because it may cause important segments of a target population to be underrepresented and it reduces statistical power. This article reviews the causes of survey nonresponse, the nature of nonresponse error and its potential impact on survey estimates, and techniques for measuring and reporting nonresponse.
Bias from Stage Migration in Cancer Survival
Authors: Blesch Karen Smith
Published: September 2014

Stage migration due to different methods of cancer staging can artifactually inflate cancer survival rates by shifting patients with a marginal prognosis out of a better prognosis group into a worse prognosis group. This bias can be avoided by ensuring that comparable staging methods are used when comparing survival across cancer cohorts.
Diagnosis Related Groups (DRGs): Measuring Hospital Case Mix
Authors: Wiley Miriam M.
Published: September 2014

The Diagnosis Related Group (DRG) system constitutes an approach to measuring hospital case mix that entails the separation of hospitalized patients into unique groups based on their diagnoses and procedures. Since originally developed in the United States in the 1980s, a number of versions of DRGs have developed to reflect the evolution in potential applications, in addition to developments in expertise, information technology, and data systems. Major initiatives in the development of DRG‐type systems have taken place in Australia with the production of AR‐DRGs and in a number of European countries. In addition to being applied extensively throughout North America, DRG‐based applications are now widespread internationally, particularly in Europe and Australia.
Health Care Financing
Authors: Lave Judith R. , Peele Pamela B.
Published: September 2014

The methods used to finance personal health care service play a major role in shaping a country's health care system. Personal health care services are those services such as hospital care, physician care, dental services, and drugs that are provided directly to individuals. The financing methods influence the terms under which people access the health care delivery system, the types of health care provided, and the mechanisms used to allocate health care services. They also influence how the costs of health care services are distributed over the population by income and by health status. Two aspects of health care financing are the focus of this article: the sources of funds for health care services and the mechanisms used to pay individuals and institutions who provide health care services.
Health Care Technology Assessment
Authors: Sheldon Trevor A.
Published: September 2014

Health technology assessment is a scientific exercise to provide evidence for health care decision makers who wish to ensure that the introduction of health technologies, such as drugs and devices, is managed in order to control costs and optimize the benefits. Health technology assessment (HTA) is the process of assessing the effectiveness, cost‐effectiveness and, more broadly, the medical, social, ethical, and economic implications of the use of a health technology in practice. Thus, it ranges from the very technical activities of investigating the reliability of a diagnostic test, through the evaluation of the cost‐effectiveness and safety of a drug all the way to policy analysis. The results are used to inform the decisions of health care policymakers, funders and providers of health care, and by the public.
Outcomes Research
Authors: Lipscomb Joseph
Published: September 2014

Outcomes research is a multidisciplinary field of study to understand and improve the end results of healthcare. These end results may include survival, health‐related quality of life, satisfaction with care, economic impact, and other outcomes important to a range of decision makers: patients, individuals at risk to disease, providers, public and private payers, regulators, and standards‐setting organizations. Outcomes research is substantially focused on assessing and enhancing the quality of health care. This requires continuing efforts to develop and apply valid, reliable, feasible, and appropriately responsive outcome measures; to evaluate the impact of interventions on these outcomes; and to translate findings into information useful to decision makers.
Quality of Care
Authors: Normand Sharon‐Lise T.
Published: September 2014

Quality of care refers to the attributes or characteristics of the delivery and subsequent outcomes of mental and physical health care. Data are collected to assess quality in order to improve care, to provide accountability, and to quantify information for market choices. Analyses of health data, however, are challenging due to their nonexperimental nature and complex structure. These characteristics lead to potential problems with confounding when identifying factors that impact quality, with nonindependence of outcomes associated with individuals treated within health care units, as well as with clustering due to multiple mixed outcomes.
Operations Research, Simulation
Authors: Sharkey Phoebe D.
Published: September 2014

Simulation models have been indispensable tools for decision makers in many different public and private environments. The methodology of developing a simulation model involves the abstraction of a real‐life system into a symbolic model format. To discuss the multiple stages required in a Monte Carlo simulation study, including the (1) Problem Statement; (2) Information Gathering and Data Preparation; (3) Model Conceptualization; (4) Model Implementation; (5) Analysis and Interpretation; (6) Verification and Validation; and (7) Implementation and Documentation. Simulation models are created in order to provide valuable insight to decision makers by replicating real‐life decision scenarios and providing more complete information about the risks and uncertainties in those environments. Once a model has been verified and validated as accurately representing the real‐world environment, many “what if ” questions about the system can be posed. Illustrative examples are presented.
Familial Correlations
Authors: Keen K. J.
Published: September 2014

Methods of statistical inference for correlations among members of families and pedigrees are summarized.
Family‐based association for quantitative traits.
Authors: Horvath Steve , Laird Nan M.
Published: September 2014

Tests of allelic association are designed to demonstrate relationships between genetic and phenotypic information. The class of tests using transmission information from parents to offspring (TDT and its generalizations) are very popular with dichotomous phenotypes because of their lack of sensitivity to population heterogeneity. Here we discuss the generalization of this approach to quantitative phenotypes.
Linkage Analysis, Multipoint
Authors: Speed Terry P.
Published: September 2014

Multipoint linkage analysis is the analysis of linkage data involving three or more linked loci. Such analyses are carried out to order or map a set of loci, to position a new locus in relation to a mapped set of loci, or to exclude a locus from a region containing two or more loci. Multipoint linkage analysis can be viewed as an attempt to make more efficient use of recombination data in furthering the aims of linkage analysis. It is more complicated than two‐point linkage analysis in several ways: the order of loci needs to be specified; a joint distribution for all recombination patterns needs to be specified; and the calculations can become prohibitive rather quickly. Likelihood methods are used in multipoint linkage analysis; there are two principal approaches to calculating likelihoods in the literature: the Elston–Stewart algorithm, which works on a relatively few loci and many individuals in a pedigree, and the Lander–Green algorithm, which is effective for many loci and relatively few individuals in human pedigrees.
Linkage Analysis, Multivariate
Authors: de Andrade Mariza , Amos Christopher I.
Published: September 2014

This article provides an overview of the methodology currently available for multivariate linkage analysis. We presented and discussed model‐free multivariate methods that have been developed and used in linkage analysis.
Paternity Testing
Authors: Baur Max P.
Published: September 2014

The use of genetic marker systems for the analysis of questionable paternity situations has been introduced in 1920 to prove in a deterministic way, situations of nonpaternity. Statistical evaluation of positive results (nonexclusion) was first performed by Essen–Möller in 1938. The general idea, which holds for any question of possible relationship, is the formulation of two alternative hypotheses regarding the postulated relationships of the persons involved. Subsequently, the likelihoods of the marker‐typing results conditional on these hypotheses are calculated using population frequencies estimated from large population samples and Mendelian proportions for transmission from parents to children. The complete genetic information is contained in the likelihood ratio of these two conditional likelihoods and serves as the basis for the decision between the two alternative hypotheses. Introduction of prior probabilities for the two hypotheses allows the calculation of Bayesian posterior probabilities using these conditional likelihoods.
Bioinformatics
Authors: Wilson Susan R.
Published: September 2014

A brief summary of this emerging field is given.
Dose–Response Models in Risk Analysis
Authors: Gaylor David W.
Published: September 2014

Various biological insults, such as exposure to hazardous chemicals, pose a risk to human health. For various purposes, including attempting to set safe limits of exposure, it is useful to quantify the risk. Direct evidence from human exposure is seldom available and most substances are assessed through bioassays on animals. The form of the dose–response relation obtained from these studies is important, as extrapolation to doses lower than those used in the experiments is necessary to try to estimate risks at the doses encountered in human exposure. Various dose–response models are presented, including multistage and multimutation carcinogenesis models. Models for continuous data are also mentioned. Models for litters are discussed as these are important in reproduction and developmental studies.
Dynamic Allocation Index
Authors: Gittins J.
Published: September 2014

When a process comprises a succession of projects, with each chosen from a pool of competing projects, there may be advantages to undertaking the projects in one order rather than in others. An example of such a process would be testing different candidate compounds when searching for a new pharmaceutical product. Dynamic Allocation Indices are a way of attributing indices to projects so that at each stage, choosing the project with maximum index optimizes the whole process, with respect to some reward function.
Radioimmunoassay
Authors: Redmond Carol K.
Published: September 2014

Radioimmunoassays (RIA) use radioactive tracers to model biochemical reactions in which specific antigens bind to antibodies in the immune system. The radiation counts of bound labeled to free‐ligand labeled antigen (or antibody) at various fixed dose levels of antigen are utilized to estimate indirectly the unknown concentration of unlabeled antigen (or antibody) in the system. Statistical methods, analogous to those employed in standard quantal bioassay designs, are available to estimate the parameters of the models and the absolute or relative potency of the concentration of antigen (antibody) in the system and its associated confidence intervals.
Stereology including tessellations
Authors: C. Marriott F. H.
Published: September 2014

Sampling methods used in stereology are briefly discussed. The fundamental formulae give estimates of volume ratios for the volume of a particular phase, the area of two‐dimensional structures, the length of linear features, and the number of particles. Some inference is also possible about curvature of surfaces. Estimation of the size distribution of spherical particles is described, and the extension to ellipsoidal particles, possible only in restricted cases, is discussed. The properties of tesselations, particularly the cell model and the Johnson–Mehl model for crystal growth, are described. Mathematical morphology, developed by Matheron and Serra as a system of transformations of digitized images, is a valuable technique for revealing relevant features, and stereological indices based on the modified images may be compared.
Up‐and‐Down Method
Authors: Gross Alan J. , C. McLean David
Published: September 2014

The Up‐and‐Down method is employed in dose–response studies where the aim is to model the probability of a binary outcome in terms of covariates, one of which is the dose being administered. The method allows for sequential choice of doses, dependent on the outcome of the previous dose. It is used in toxicology experiments in which the outcome is death: following a death, the next unit receives a lower dose, whereas a survival leads to the dose being increased.
Quantal Response Models
Authors: Morgan Byron J.T.
Published: September 2014

In a quantal response experiment, a particular organism is selected and exposed in groups to different concentrations of a drug for a fixed time, and a discrete response is observed (such as death or survival). In the binary response case, a tolerance or threshold assumption yields a model for the probability of response at a particular dose that has the form of the cumulative distribution function of the tolerances. The median effective dose (ED50) is the dose level that results in an expected 50% response under certain model assumptions. The Spearman–Karber estimate is a nonparametric estimate of the ED50. This article also surveys parametric estimates, which can have improved precision. Parametric models are discussed, including extensions for polytomous responses and multivariate responses. Also discussed are the effect of overdispersion, and design issues such as sequential methods for finding the point at which a certain probability of response occurs.
Causal Direction, Determination
Authors: Jones B. , Wang J.
Published: September 2014

Causation is defined here in terms of probability, taking into account potential confounding variables. Path diagrams and approaches to causality in longitudinal studies are discussed briefly.
Correspondence Analysis of Longitudinal Data
Authors: Van der Heijden Peter G. M.
Published: September 2014

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