bluuebunny/crossref_metadata_2025 huggingface dataset:
Two‐Mutation Carcinogenesis Model
Authors: Moolgavkar Suresh H.
Published: September 2014

The two‐mutation carcinogenesis model, better known as the two‐stage clonal expansion (TSCE) model, is a type of multistage carcinogenesis model that explicitly considers stochastic growth of intermediate cells on the pathway to malignancy. Because of this fact, this model can be used not only for analysis of time‐to‐tumor data, which requires the hazard function, but also for analysis of intermediate lesions, such as colon polyps, which requires expressions for the number and the size distribution of such lesions. The model has been extensively used for analyses of both types of data. Extensions of the model to incorporate more than two mutations have also been proposed.
Viral Population Growth Models
Authors: Tuckwell Henry C.
Published: September 2014

The steps involved in viral reproduction are described briefly. Mathematical models for the growth of viral populations without random effects take the form of systems of differential equations and these are reviewed, including the standard HIV equations, with correction terms. The validity of the “mass action” assumption is questioned for low virion levels. Stochastic differential equation models for HIV are described, including those with both discontinuous and diffusion processes. Simulation results are given for early HIV population growth, including the distribution of the time to reach a detection level, for which an analytical theory is also available. The long‐term model of Tan and Wu is discussed briefly as well as mutation and its effect on drug treatments. Despite some useful analysis of patient data with respect to simple differential equations, it is concluded that modeling, either deterministic or stochastic, of viral populations requires much further development to give an accurate and useful representation of the underlying biological processes.
Y
ule Process
Authors: Chiang Chin Long
Published: September 2014

The Yule process is a birth process with a constant birth rate. The number of births in time interval (0,
t
),
Y
(
t
), has a negative binomial distribution, which is the same as the distribution of the number of new species of a genus produced during (0,
t
) in Yule's study of evolution.

The numbers of species produced in a sequence of time intervals,
Y
(1), …,
Y
(
r
), have a chain of negative binomial distributions.
Bias, Overview
Authors: K. Choi Bernard C. , P. Pak Anita W.
Published: September 2014

This article provides an overview on bias. Bias is defined as the deviation of results or inferences from the truth, or processes leading to such deviation. The article also provides a catalogue of 109 biases at the following stages of research: literature review 4, study design 31, study execution 3, data collection 46, analysis 15, interpretation of results 7, and publication 3.
Bias from Loss to Follow‐up
Authors: Gail Mitchell H.
Published: September 2014

Loss to follow‐up bias results when subjects lost from a cohort have different health response distributions from subjects who remain in follow‐up.
Bias from Nonresponse
Authors: Gail Mitchell H.
Published: September 2014

Nonresponse bias results when some members of the intended study population fail to provide required data (the nonresponders), and when those who respond are not representative of the entire study population.
Bias in Case–Control Studies
Authors: Shapiro Samuel , Rosenberg Lynn
Published: September 2014

We discuss two classes of bias that arise in case–control studies, selection bias and information bias. A third source of bias, confounding, is not considered in this article. Selection bias arises either when cases in the study sample are not representative of cases arising from the source population (“study base”) or when controls are not representative of corresponding noncases in the study base. Sources of selection bias include bias in identifying cases or noncases in the study base, nonrepresentative sampling techniques, and selective refusal to participate (nonresponse bias). Use of nonrepresentative controls, such as hospital‐based controls, can lead to bias if the exposure distribution differs from that of controls in the study base. Information bias arises from inaccurate reporting of exposures and other covariates by cases or controls, and can result in serious distortion of estimated odds ratios when the errors in reporting by cases and controls are differential (recall bias).
Bias Toward the Null
Authors: Gail Mitchell H.
Published: September 2014

Bias toward the null usually refers to an effect of
nondifferential errors
in exposure measurements that reduces the apparent effect of the exposure on the dependent variable, which might be a health outcome.
Birth Defect Registries
Authors: Hanson James W. , P. Oakley Godfrey
Published: September 2014

Birth defects registries can be used to determine the prevalence of birth defects in newborns and can serve as a source of cases for etiologic investigations. We discuss the types of information in population‐based and nonpopulation‐based birth defects registries. We describe some prevention policies that were based on studies of registered cases and population controls.
Cancer Registries
Authors: Hankey B. , Fritz April
Published: September 2014

We review the history of cancer surveillance and distinguish hospital‐based from population‐based cancer registries. Registry operations include case ascertainment, abstracting and coding, data collection, quality control, and producing statistics on health outcomes. The characteristics of the National Cancer Institute's Surveillance, Epidemiology and End Results (SEER) program are described, and we give web site addresses that describe SEER procedures, facilities for making data and analytical tools available, and a strategic plan for cancer surveillance research.
Case–Cohort Study
Authors: Langholz Bryan
Published: September 2014

The case–cohort design includes a randomly sampled subcohort that is selected without regard to disease status or disease event times from the entire cohort, plus those cohort members who develop disease (cases) and who are not members of the subcohort. Selection of the subcohort offers several advantages, including: ability to gather data on subcohort members from the very beginning of the study and to implement quality control procedures immediately; ability to serve as a comparison group for a variety of disease endpoints; flexibility of analysis that comes from selection of the subcohort without matching on event times, including choice in the type of analysis model (e.g. semiparametric proportional hazards or Poisson regression models) and straightforward estimation of cumulative risk. We review analytical approaches for the case–cohort design and discuss its advantages and disadvantages, including issues of statistical efficiency, in comparison with the nested case–control design.
Case–Control Study, Hospital‐Based
Authors: Gail Mitchell H.
Published: September 2014

A hospital‐based case–control study is a case–control study in which cases with a given disease are selected from persons with that disease in a given hospital or group of hospitals, and controls are patients with other diseases from these hospitals, the strengths and weaknesses of hospital‐based control studies are discussed in comparison to population‐based case–control studies.
Case–Control Study, Nested
Authors: Langholz Bryan
Published: September 2014

Nested case–control studies refer to a class of cohort sampling designs used in epidemiologic research. This article describes the general principles, analysis methods, statistical properties, and relationship to other approaches. Methods are illustrated by studies from the epidemiological literature.
Case–Control Study, Sequential
Authors: O'Neill Robert T.
Published: September 2014

Recruitment of cases and controls for a case–control study can take an appreciable amount of time. There is potential for shortening the duration of a case–control study and reducing the numbers of required cases and controls by analyzing the accruing data sequentially and perhaps by stopping accrual early if a definitive early decision can be reached. We discuss the Wald sequential probability ratio test and group sequential tests for 1:1 matched case–control designs and tabulate the average reductions in sample sizes obtained by sequential procedures, compared with fixed sample size designs.
Causation
Authors: Greenland Sander
Published: September 2014

The concepts of cause and effect are central to most areas of scientific research, so it is not surprising that the literature on them could fill a small library. What may be surprising, given their importance, is that consensus about basic definitions and methods for causal inferences (at best) limited, despite some three centuries of debate. A brief review cannot do justice to the history and details of this debate, nor to all the schools of thought on causation. This article will, therefore, focus on a few major themes that have affected modern biostatistical practice. Of necessity, some aspects of the discussion are simplified relative to the literature, and the references should be consulted for more thorough descriptions. Entries on related topics are given in the final section.
Cohort Study, Historical
Authors: Samet Jonathan M.
Published: September 2014

A historical cohort study, also called a retrospective cohort study or a nonconcurrent cohort study, refers to a cohort whose members were at risk for disease outcomes before the study was initiated. Historical cohort studies are widely used in occupational epidemiology because they allow one to take advantage of employment records to identify cohort membership and exposures, and because one does not need to wait for disease outcomes before analyzing the data. As in other cohort studies, historical cohort studies allow one to estimate disease incidence rates as well as relative risks and other measures of exposure effect. Because administrative databases are often used, however, there is a potential for inadequate data to define cohort membership (selection bias), exposure status, and covariates needed to control for confounding, such as smoking status. In addition, there may be special problems with loss to follow‐up and misclassification of health outcomes. Historical cohort studies may be supplemented with special nested case–control or case–cohort designs to obtain additional detailed information on selected cohort members.
Confounder
Authors: Gail Mitchell H.
Published: September 2014

A confounder is a variable that alters the association between an exposure and a disease. A confounder is associated with disease risk in unexposed subjects, is not affected by exposure or disease, and is associated with exposure in the source population. Methods to control confounding are mentioned, as is a definition of confounding based on a counterfactual theory of causal effects.
Cross‐Sectional Study
Authors: Satten Glen A. , Grummer‐Strawn Laurence
Published: September 2014

Cross‐sectional studies estimate the distribution of quantities at a certain moment in time. Only one set of observations is taken from each subject in a cross‐sectional study. Cross‐sectional studies may be used to estimate prevalence of a disease or risk factor, make inferences about the association between variables such as an outcome and explanatory variable, or to make inferences about disease incidence. If population characteristics are constant over time, then analysis of the age distribution of a quantity of interest can describe age‐specific incidence. Multiple cross‐sectional studies may be used to monitor trends in the distribution of a variable. Cross‐sectional prevalence of a transient state early in disease can be used to measure disease incidence. Although cross‐sectional studies may reveal suggestive associations between explanatory variables and outcomes, causality cannot usually be inferred from cross‐sectional data. Cross‐sectional data may also be subject to sampling biases like length‐biased sampling or recall bias.
Cumulative Hazard
Authors: Gail Mitchell H.
Published: September 2014

Cumulative hazard is the integrated hazard rate.
Cumulative Incidence
Authors: Gail Mitchell H.
Published: September 2014

Cumulative risk is the proportion of individuals in a cohort who develop disease in a defined time period.
Density Sampling
Authors: Gail Mitchell H.
Published: September 2014

Density sampling refers to sampling controls from among the set of disease‐free individuals matched with time at the time of case incidence. Time‐matched analysis of case–control data with density sampling yields estimates of the relative hazard.
Descriptive Epidemiology
Authors: Gail Mitchell H.
Published: September 2014

Descriptive epidemiology is defined and contrasted with analytic epidemiology.
Differential Error
Authors: Gail Mitchell H.
Published: September 2014

Differential error is defined and contrasted with nondifferential error. Differential error can lead to biases in any direction.
Dynamic Population
Authors: Gail Mitchell H.
Published: September 2014

Dynamic populations and steady state dynamic populations are defined and some properties mentioned.
Ecologic Study
Authors: Morgenstern Hal
Published: September 2014

An ecologic study focuses on the comparison of groups rather than individuals; thus, individual‐level data are missing on the joint distribution of variables within groups. The purpose of an ecologic analysis may be to make biologic inferences about effects on individual risks or to make ecologic inferences about effects on group rates. Ecologic study designs may be classified on two dimensions: (a) whether or not the primary exposure is measured; and (b) whether subjects are grouped by place, time, or place and time. Despite practical advantages of ecologic studies, there are several methodologic problems that severely limit causal inference, including ecologic and cross‐level bias, problems of confounder control, within‐group misclassification, lack of adequate data, temporal ambiguity, collinearity, and migration across groups. Even when the study objective is to estimate ecologic effects, individual‐level data are generally needed to make valid causal inferences, preferably using multilevel analysis.
Eligibility Restriction
Authors: Gail Mitchell H.
Published: September 2014

Eligibility restriction is a method to control for confounding in observational studies and to eliminate individuals who are unlikely to benefit from treatments or to be able to comply to treatment in clinical trials.
Experimental Study
Authors: Gail Mitchell H.
Published: September 2014

An experimental study is a study in which conditions are controlled and manipulated by the experimenter. Often, randomized treatment assignment is used to assure that potential confounders have similar distributions in the various treatment groups.
Geographic Epidemiology
Authors: Bithell John F.
Published: September 2014

Geographical analysis of epidemiologic data may be useful in modeling risk as a function of geographic covariates, testing hypotheses, mapping disease patterns, identifying areas of high risk, or detecting clusters. Distinctions are drawn between case dependence and independence (the latter not requiring a spatially correlated error structure), and between areal and continuous data. Various models are described, and attention is given to disease mapping (involving data smoothing) and clustering.
Incidence Density
Authors: Gail Mitchell H.
Published: September 2014

An incidence density is an incidence rate.
Incidence Rate
Authors: Gail Mitchell H.
Published: September 2014

The incidence rate is the number of persons who develop a disease of interest over a defined interval of time or age divided by the corresponding person‐years at risk.
Incidence–Prevalence Relationships
Authors: Keiding Niels
Published: September 2014

The relation between prevalence, incidence, and duration is studied in the perspective of statistical modeling, with the Markov illness–death process as the main tool. The focus is on the classical relation prevalence = incidence × duration, for inference on incidence from prevalence, and on prevalence from incidence. The theory of screening for chronic diseases is related.
Measurement Error in Epidemiologic Studies
Authors: Carroll Raymond J.
Published: September 2014

This article concerns measurement error in epidemilogic studies, often called errors‐in‐variables. I discuss what measurement error is, its impact on parameter estimates and hypothesis tests, and how to perform statistical analyses that account for the measurement error.
Misclassification Error
Authors: Kuha Jouni , Skinner Chris , Palmgren Juni
Published: September 2014

Misclassification of categorical variables creates problems for analysis and interpretation, leading to biased estimates if the misclassification is ignored. For example, in two‐way tables, its effect is often to bias observed associations toward the null value. In three‐way tables, an association between two variables can be biased in any direction when a confounder variable is misclassified. More generally, complex combinations of effects can occur, and understanding them requires information about the misclassification parameters. Such information can be obtained from validation studies or repeated measurements. These also allow the misclassification biases to be removed or reduced using appropriately modified estimators. The most straightforward adjustment methods are based on simple back‐calculation, while in more complex cases, formal modeling methods are more suitable. These are essentially models for contingency tables where some variables are partially or completely unobserved.
Nondifferential Error
Authors: Gail Mitchell H.
Published: September 2014

Nondifferential error is defined and contrasted with differential error.
Occupational Epidemiology, Statistics inhealthy worker effect+
Authors: Axelson Olav
Published: September 2014

Occupational epidemiology has developed especially since the 1970s, first focusing on cancer, particularly respiratory tract cancer (lung and nasal cancer). Later the interest has broadened to encompass most other cancer forms and also neurological, cardiovascular, and musculoskeletal disorders as well as reproductive hazards.Cancer risk and exposures to various chemical agents, especially to asbestos and other fibers, silica, and wood dust have been studied along with exposure to ionizing radiation, particularly radon progeny as well as electromagnetic fields. Clinical observations of suspected relations between work and disease have often initiated these kind of studies and so have animal experiments suggesting a hazard. Studies concerned with stressful working conditions and ergonomic workloads represent a later development. Regarding methods, cohort and proportional mortality studies were earlier predominating but case–control studies have become increasingly common and are also suitable in the context of so‐called molecular epidemiology, either when considering gene‐environment interactions or subclasses of tumors with particular mutations. A problem with the cohorts has often been that workers in general are healthier than the population at large when used as the reference population, then leading to a so‐called healthy worker effect. The concerns about confounding in occupational epidemiology studies are often exaggerated.
Odds Ratio
Authors: Gail Mitchell H.
Published: September 2014

The odds ratio is the ratio of the odds of an event in one group to the odds of an event in another group.
Prevalence Rate or Ratio
Authors: Gail Mitchell H.
Published: September 2014

The prevalence rate or ratio refers to the number of people who have a disease or condition at a given point in time in a given population divided by the number of people in that population.
Random Digit Dialing Sampling for Case–Control Studies
Authors: Waksberg Joseph
Published: September 2014

Random digit dialing (RDD) is a useful tool in epidemiological research, particularly for selecting controls in population‐based case–control studies. The paper discusses the two principal methods currently used for sample selection in RDD, and indicates why other, possibly simple methods, are unsatisfactory. The principal considerations that should be taken into account in determining the details of a sample selection method (e.g. geographical boundaries of cases and controls, whether to choose more than one control per household, response rates, etc.) are described. Issues relating to data collection, once the sample has been selected, are also described including how these follow‐up activities could influence the choice of sample design and could complicate the methods of analysis of the study results.
Random Error
Authors: Gail Mitchell H.
Published: September 2014

Random error is defined and contrasted with systematic error.
Recall Bias
Authors: Gail Mitchell H.
Published: September 2014

Recall bias occurs in case–control studies and refers to the bias that results when cases with the disease of interest tend to over‐ or underestimate their previous exposures, compared with controls without disease.
Relative Hazard
Authors: Gail Mitchell H.
Published: September 2014

The relative hazard is the ratio of two hazard rate functions at a given time.
Retrospective Study: Definition
Authors: Gail Mitchell H.
Published: September 2014

The term retrospective study describes case–control studies and other designs, such as historical cohort studies, in which relevant exposures and/or disease incidences occurred before the time of the study data collection.
Sex Ratio at Birth
Authors: Armitage Peter
Published: September 2014

Many studies of variations in the sex ratio at birth were conducted between the seventeenth and twentieth centuries as illustrations of the analysis of binary data. More recent epidemiologic studies show tendencies for the ratio of males to females to decline slightly in populations subject to deprivation of some sort. Family studies are often difficult to interpret, but provide no evidence of appreciable variation in the sex ratio between families.
Software, Epidemiological
Authors: Goldstein Richard
Published: September 2014

This article presents an overview of epidemiologically oriented software, emphasizing software that either uses epidemiological jargon or is specifically aimed at epidemiologists. A discussion of some of the advantages and disadvantages of specialized versus general‐purpose analytic software is included.
Systematic Error
Authors: Gail Mitchell H.
Published: September 2014

Systematic error is defined and contrasted with random error.
Counter‐Matching
Authors: Langholz Bryan
Published: September 2014

Counter‐matching is an exposure‐stratified nested case‐control technique used in epidemiological research. In this article, individually matched and grouped data versions of the design, and analysis methods based on conditional likelihoods, are described. Four data examples are provided, which illustrate the use of counter‐matching in practice. Finally, a general discussion about when and how to use the design, a comparison to matching, and a short comparison to other designs is provided.
Clinical Epidemiology
Authors: Meenan Robert F. , Felson David T.
Published: September 2014

Clinical epidemiology focuses on the study of subjects with disease—their diagnosis, disease course, and outcomes. Its core concerns are measurement of clinical phenomena, diagnostic reasoning, treatment selection, and outcome assessment. To address these concerns, clinical epidemiologists apply quantitative methods from a variety of disciplines, particularly those from epidemiology.
Diagnostic Test Accuracy
Authors: Walter Stephen D.
Published: September 2014

This entry discusses accuracy for a diagnostic test in terms of the disease prevalence, and test sensitivity and specificity. Clinical implications of test errors are mentioned as well as alternative ways to assess accuracy.
Diagnostic Test Evaluation without a Gold Standard
Authors: De Bock G. H. , Van Houwelingen J. C.
Published: September 2014

In case of unknown sensitivity and specificity of a reference test, the sensitivity and specificity of a diagnostic test can be estimated by the EM algorithm.
Dose‐response in Pharmacoepidemiology
Authors: Murphy J. R.
Published: September 2014

Pharmacoepidemiology deals with the action of drugs within a population. Because of differences in the method of data collection, estimation of pharmacoepidemiologic dose–response parameters is somewhat different from that in biological assay or clinical trials. Pharmacodynamic models to estimate these parameters are reviewed, with a specification of appropriate dose–response variables. Parameter estimation is described, together with mention of appropriate software to do so.
Drug Interactions
Authors: Mick Rosemarie
Published: September 2014

Drug interaction is the alteration of drug disposition (i.e. pharmacokinetics) and/or drug effect (i.e. pharmacodynamics) of one drug due to the presence of a second drug. In pharmacokinetic interaction, one drug's elimination rate or enzyme‐mediated metabolism may be inhibited or induced by a second drug. During pharmacodynamic interaction, the net drug effect may be enhanced or diminished or a new side‐effect may appear that was not seen with either drug alone. To assess drug interaction, a randomized complete block design is employed in which all treatments (e.g. each single drug and the combination) are administered to each subject. The order of the treatments is randomly assigned. Disposition and/or effect measures are repeated after each treatment is administered. Using planned contrasts, the interaction is tested by comparing the treatment means for a single drug to that of the combination.
Epidemiology as Legal Evidence
Authors: Gastwirth Joseph L.
Published: September 2014

Epidemiologic studies demonstrating or refuting an increased risk of disease from exposure to a potentially toxic chemical or drug are useful evidence in product liability litigation, environmental regulation, and the determination of a worker's eligibility for compensation due to exposure in the workplace. Different laws often require different amounts of scientific evidence and the strength of the association, usually measured by the relative risk, to convince the fact‐finder. In particular, a lesser degree of scientific certainty is needed for eligibility for worker compensation because of the humanitarian nature of these laws and the fact that an employee accepting these payments forgoes their right to sue for other damages. The role of epidemiologic findings in several types of cases is described. Recent developments in the United States concerning the judicial scrutiny given expert testimony based on the results of scientific and technical studies are described.
Gold Standard Test
Authors: Walter Stephen D.
Published: September 2014

This entry describes the concept of an error‐free diagnostic test, and the practical difficulties of attainting this ideal. The consequences of assuming a test to be error‐free when in fact it is not are also mentioned.
Incubation Period of Infectious Diseases
Authors: Brookmeyer Ron
Published: September 2014

The incubation period is the time interval between exposure to a disease‐causing agent and the onset of symptomatic disease. For example, the incubation period of an infectious disease refers to the time interval between infection or exposure to a viral or bacterial agent and the onset of symptomatic (clinical) disease. The incubation period is also called the clinical latency period. The focus of this article is on modeling and estimating the incubation period of infectious diseases. However, some of the ideas may also be applicable to the incubation period of noninfectious disease, for example, the incubation period of radiation‐induced cancer that refers to the time interval from radiation exposure to cancer diagnosis. Alternative designs for the estimation of an incubation period are reviewed, and methods to synthesize studies on incubation periods are mentioned.
Kappa and Its Dependence on Marginal Rates
Authors: Cook Richard J.
Published: September 2014

The kappa statistic is a widely used measure for quantifying agreement between raters using nominal scales. It is advocated on the grounds that it “corrects” for a level of agreement that would be expected by chance alone based on the marginal rates. The operating characteristics of kappa, however, show that it responds in a complicated and unintuitive way to subject‐specific and marginal agreement. Modifications to kappa and supplementary statistics, which have been proposed to help interpret kappa are discussed. Remarks are made as to why the kappa statistic has such problematic features.
Meta‐Analysis of Diagnostic Tests
Authors: Macaskill Petra , Glasziou Paul , Irwig Les
Published: September 2014

Meta‐analysis can summarize the performance of a diagnostic test based on all available high quality studies, investigate reasons for variation in test performance, and compare the performance of two or more tests. Strategies are outlined for finding all relevant primary studies, and assessing the quality of studies. Study quality, study design factors, and patient characteristics should be assessed and explored as possible sources of heterogeneity in diagnostic performance. Summary ROC analysis (SROC) is the most common meta‐analytic method for diagnostic studies. In addition, linear regression is used to model test accuracy as a function of implicit test threshold and other study level covariates. The SROC approach shows the trade‐off in sensitivity and specificity as the threshold for a positive test result varies. The more complex, hierarchical SROC model, takes account of both within and between study variability, and can be used to explore variation in both accuracy and threshold.
Diagnostic Tests, Multiple
Authors: Begg Colin B.
Published: September 2014

In medical practice, diagnostic tests are rarely used in isolation. Typically, the evidence for forming a diagnosis comes from multiple sources, in the form of signs and symptoms and other patient characteristics, in addition to specific tests that are ordered to rule in or rule out a candidate diagnosis. Thus, the evidence from individual diagnostic tests must be used collectively in forming the diagnosis, and the statistical dependency of the information from these multiple sources becomes an important factor in assessing the weight of evidence. In this article, we consider the two settings in which data from many diagnostic tests are relevant. In the first, we consider discriminant analysis, where one wishes to develop diagnostic rules on the basis of an available data set encompassing information on multiple tests. In the second setting, we consider the problem of sequentially updating diagnostic probabilities as new tests are selected and performed in the course of making a diagnosis for an individual patient.
Pharmacoepidemiology, Study Designs
Authors: Strom B. L.
Published: September 2014

Pharmacoepidemiology studies the use and effects of pharmaceuticals in populations. In general, its study designs are the same as those for other areas of clinical epidemiology. However, because of the existence of previously conducted premarketing clinical trials, and the need in pharmacoepidemiology to conduct very large studies very quickly, there are special issues that attend to the conduct of postmarketing pharmacoepidemiology studies. This chapter reviews the special data resources used to address these issues, the issues of data validity that arise from the use of these databases, and the special methodological approaches used to apply them.
Receiver Operating Characteristic (ROC) Curves
Authors: Hanley J. A.
Published: September 2014

The receiver operating characteristic(ROC) curve describes the relationship between the false positive fraction and true negative fraction associated with a diagnostic test as the test threshold defining the boundary between individuals classified as cases or noncases is varied. The ROC curve avoids certain arbitrariness involved in the choice of a single overall index of accuracy. This entry describes how the ROC curve is derived from data, and reviews summary indices that may represent its performance. The comparison of ROC curves for competing tests is discussed, and appropriate software is identified.
Screening, Overview
Authors: Oortmarssen Gerrit J.
Published: September 2014

This entry describes a series of measures that may summarize the performance of a screening program, with respect to possible outcomes, impact, and frequency in the target population. A conceptual framework that may be used to establish the effectiveness of screening is presented, together with possible biases that may emerge in alternative study designs for screening evaluation. Monitoring of ongoing screening is also briefly reviewed.
Sensitivity
Authors: Walter Stephen D.
Published: September 2014

The sensitivity of a diagnostic or screening test is defined, and a relation to the test likelihood ratio is described. Clinical implications are mentioned. Alternative uses of the term “sensitivity” are also mentioned, in the context of measuring devices, outlier detection, and robustness.
Sensitivity Analysis: Introduction
Authors: Goldsmith Charles H.
Published: September 2014

Sensitivity analysis is a reassessment of the model used for data summary that attempts to detect whether changing any of its assumptions leads to different interpretations of the outcome. Sensitivity analysis can be carried out simply by varying parameter values to assess whether a different interpretation of the response emerges, or more elaborate ideas from Monte Carlo analysis and factorial experiments can also be used. Sensitivity analyses have been used recently for meta‐analysis. Other techniques such as graphical displays and comparison of alternative model formulations are also briefly mentioned.
Screening, Sojourn Time
Authors: Duffy Stephen W.
Published: September 2014

Sojourn time is the duration of a disease before clinical symptoms become apparent but during which it is detectable by a screening test. Its clinical relevance is that it represents the duration of the temporal window of opportunity for early detection. Its statistical interest lies in the estimation of the mean sojourn time of a screened population, taking into account the sensitivity of the screening test. Common models for its distribution include the exponential and Weibull distributions. Methods of estimation include maximum likelihood, generalized estimating equations, and Markov Chain Monte Carlo.
Specificity
Authors: Walter Stephen D.
Published: September 2014

The false positive rate of a diagnostic or screening test is defined, and its clinical implications are discussed. An alternative use of the term “specificity” in factor analysis is also mentioned.
Accident Proneness
Authors: Kemp Adrienne W. , Kemp C. D.
Published: September 2014

The entry reviews five competing models for the occurrence of accidents: (1) pure chance, (2) true contagion, (3) apparent contagion, (4) spells, and (5) liability. The distributions for accident count data that they yield (e.g. Poisson, negative binomial, Neyman type A, short, and generalized Waring) can, however, arise from more than one model. Model identification, in order to modify behavior and hence reduce the number of accidents, has proved largely ineffective. Since 1985, research has moved from model detection to risk evaluation and analysis.
Chronic Disease Models
Authors: Manton Kenneth G.
Published: September 2014

We discuss the need for multidimensional stochastic models of physiological processes to describe the evolving risk of chronic disease over a lifetime. Modifications of that type of model needed to describe different types of risk factor and health effects collected in different longitudinal studies are described. How health processes are “captured” in different longitudinal study designs is discussed, as is the description of the intra‐individual dynamics of degenerative processes. The concept of hormesis is introduced as are more standard types of hazard models (Weibull and Gompertz) drawn from reliability theory. Measurements that may be made in molecular epidemiological studies are discussed and issues that they raise for statistical modeling of disease processes reviewed. Methods for analyzing the genetic contributions to chronic disease risks are presented and discussed.
Epidemic Curve
Authors: Rosenberg Philip S.
Published: September 2014

An epidemic curve is a plot of time trends in the occurrence of a disease or other health‐related event for a defined population and time period. The event rate that is plotted may measure the number of cases per unit time, or it may express the numbers of events relative to the number in the study population (incidence rates). In infectious disease epidemiology, the epidemic curve is used to document the scope and duration of an epidemic, to help determine the source of the infection and the modes of transmission or exposure, and to glean information about the incubation period of the disease. In other settings, including behavioral and chronic disease epidemiology, epidemic curves are used to document the scope of public health problems.
Epidemic Models, Control
Authors: Clancy Damian
Published: September 2014

Two approaches are considered in relation to classic deterministic and stochastic epidemic models: to reduce the basic reproduction ratio below unity; or to define costs of intervention and infection and choose the optimal intervention policy.
Epidemic Models, Recurrent
Authors: Nåsell Ingemar
Published: September 2014

Deterministic and Stochastic epidemic models are described and various aspects of long‐term behavior are contrasted. The threshold predicted by the deterministic model is a poor approximation to the stochastic analogue. Damped oscillations given by the deterministic model are seen when stochastic models are averaged over realizations, but not for individual realizations. Extinction is seen only in stochastic models, with a probability dependent on population size.
Epidemic Models, Structured Population
Authors: O'Neill P. D.
Published: September 2014

Real‐life epidemics invariably occur within populations composed of individuals who do not mix with each other entirely at random. Instead, there is frequently a nonhomogeneous mixing structure, so that individuals are more likely to spend time with those they live or work with, or who are geographically close, for example. Here we review a number of stochastic epidemic models, which attempt to incorporate population structure, providing descriptions of the models and highlighting associated results of interest. Aspects of related statistical inference are also considered. Such models offer a more realistic alternative to those that assume homogeneous mixing of either the entire population, or large subpopulations.
F
ix–
N
eyman Process
Authors: Chiang Chin Long
Published: September 2014

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

Let
p

ij

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

j

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

ij

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

i

to
R

d

,
Q

id

(0,
t
), are also given.

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

The model is useful in areas of research where states
S
1
and
S
2
can be defined.
Infectious Disease Models
Authors: Becker Niels G.
Published: September 2014

Infectious disease models are used to gain insights into strategies for the control of infectious diseases. Stochastic versions are also used in the analysis of infectious disease data, where they help to focus the analysis on epidemiologically meaningful parameters, take care of dependence in the data, and compensate for the fact that only part of the infection process is observable. Different characteristics of disease transmission and progression have promoted a variety of models, including transmission chain models that keep track of infected individuals in “generations” and models that monitor the transmission dynamics continuously over time. Models formulated to describe outbreaks in households are particularly useful, because households are a convenient unit for empirical studies.
Epidemic Models, Sensitivity Analysis
Authors: G. Cairns Andrew J.
Published: September 2014

In this article, we consider how one can analyze the sensitivity of epidemic models to changes in the various inputs. We start by discussing the different reasons for conducting a sensitivity analysis. The article then proceeds by discussing the different methods of sensitivity analysis that can be employed.

One of our main concerns is to assess which parameters have a significant impact on epidemic dynamics and which do not. However, when we consider relatively complex models, we find that the impact of changes in one parameter can be almost cancelled out by a corresponding change one or more other parameters (e.g. by keeping the reproduction number,R0, fixed). This concept introduces the concept of primary component analysis. The aim here is to identify the small number of components of a model (such asR0) that are the key determinants of epidemic dynamics with the remaining parameters having a relatively small impact.
SIREpidemic Models
Authors: Lefèvre Claude
Published: September 2014

These models describe the spread of infectious diseases in which an individual may move from Susceptible to Infected to Removed. Simple models, including the Reed–Frost chain binomial and the general epidemic, are Markovian with the implication of an exponentially distributed infectious period. The generalized epidemic removes this restriction. More complex models allow for individual or structural heterogeneity in susceptibility, infectivity, and/or mixing behavior.
Simpson's Paradox
Authors: Dong Jianping
Published: September 2014

Simpson's paradox occurs when the direction of a measure of association between two variables is reversed after pooling over a covariate. For example, a treatment can be effective for both males and females, but ineffective when the data for males and females are combined. Since Simpson's original example in his 1951 paper, numerous real‐life examples of Simpson's paradox have been reported in many areas and the paradox has been generalized to many forms of association reversals. Necessary and sufficient conditions for Simpson's paradox as well as generalized forms of association reversals are given so we know, for example, when a high‐dimensional contingency table can be safely collapsed, avoiding the paradox. It is also of interest to know what the appropriate conclusion is when Simpson's paradox does occur.
Denominator Difficulties
Authors: Kravitz Howard M.
Published: September 2014

This article describes the fallacy in calculating rates and proportions without having denominator data. Missing, wrong, and unknown denominators are discussed. Examples are presented to illustrate this fallacy.
Incomplete Follow‐up
Authors: Kravitz Howard M.
Published: September 2014

This article describes a fallacy associated with longitudinal (follow‐up) data analysis. Bias that can occur from not accounting for study duration and from inappropriate management of missing data are considered.
Treatment Delay
Authors: Armitage Peter
Published: September 2014

Delay in the reporting of symptoms or in the start of treatment for a life‐threatening illness may, paradoxically, be associated with increased survival. This is due to the confounding effect of severity of illness, the more rapidly developing forms of disease being associated both with shorter pretreatment intervals and with decreased survival. The point is illustrated by published data on breast cancer and tetanus.
Framingham Study
Authors: Gordon Tavia , B. D'agostino Ralph
Published: September 2014

Initially, the Framingham Study was the Framingham Heart Study. The Framingham Study has grown dramatically over the years. In 1971, the Framingham Offspring Study began consisting of children of the original cohort and their spouses, 2489 males and 2646 females, with examinations conducted every four years. In 2002, a sample study of over 3500 third generation children and their spouses began. The Framingham Study is unique as a genetics study. Not only is there the risk factor collection (phenotypes), but there is also DNA and cell lines for many of the three cohorts (Original Cohort, Offspring Study, and Gen3 Study). The original study consisted mainly of whites of European extraction. A new substudy, the Omni Study, consisting on nonwhites is now an integral component of the study. Further, a number of ancillary studies evaluating stroke, dementia, arthritis, hearing, vision, sleep apnea, cancer, and nutrition are major components of the study. In addition to all the examination components of the original cohort, new technologies such as carotid ultrasound, heart and brain MRIs, and echocardiograms are part of the evaluations. Further, the study often leads the field in collecting new and novel risk factors such as homeostatic (clotting) factors including fibrinogen, inflammatory markers, and infection marker.
Leukemia Clusters
Authors: Bithell John F.
Published: September 2014

Clusters of cases of childhood leukaemia have been reported near nuclear installations. They are difficult to explain in terms of the level of environmental radiation, and viral etiologies have been suggested. The selective nature of the data makes rigorous analysis difficult. Evidence for a general tendency of leukaemia to cluster is equivocal.
Smoking and Health
Authors: Holland W. W.
Published: September 2014

Increasing rates of incidence and mortality from lung cancer were noted in the 1930s and smoking was one of a number of suggested causes. Various workers in 1950 published persuasive confirmation from case–control studies, which showed a large relative risk for cigarette smokers, increasing with amount smoked. Prospective cohort studies provided further confirmation and showed that other causes of death are involved. The effect of passive smoking is discussed. Although smoking has been reduced in developing countries, concern exists about the lesser degree of reduction by women or by poorer people, and about the targeting of developing countries in tobacco promotion.
Agreement, Measurement of
Authors: Shoukri M. M.
Published: September 2014

This entry reviews some well‐known indices of agreement, the conceptual and statistical issues related to their estimation, and their interpretation for both categorical and interval scale measurements. Particular measures of agreement that are discussed include kappa, a measure of category distinguishability, measure for agreement with nominal responses, monotonic agreement, and agreement measures for 2 × 2 tables. Agreement of multiple raters for the same subject is also discussed, together with the use of unanimity and majority rules. Software to carry out these calculations is also mentioned.
Epidemic Models, Stochastic
Authors: Gani J.
Published: September 2014

This article describes the most commonly used stochastic epidemic models. For discrete time, the Greenwood and Reed–Frost chain binomials are outlined, with examples. In continuous time, the simple SI, the general SIR and the carrier‐borne CSR models are discussed, and Whittle's Threshold Theorem stated. The article concludes with some references to recent literature.
B
ayesian Decision Models in Health Care
Authors: Alemi F.
Published: September 2014

The paper discusses how probability of rare events, for example, breach of patients' privacy, can be assessed from clues in the environment using Bayesian probability models. The paper shows how likelihood ratios associated with various clues in the environment could be assessed from interviews with panels of experts.