bluuebunny/crossref_metadata_2025 huggingface dataset:
Square Contingency Table
Authors: Lipsitz Stuart R.
Published: September 2014

This article discusses the analysis of contingency tables for which the number of rows equals the number of columns. Three types of models for such tables are discussed: marginal models for comparing margins of the table, loglinear models for describing the joint distribution and the association patterns, and conditional models that consider the distribution of one of the categorical variables conditional on the outcome for the other one. Other topics discussed include testing marginal homogeneity and symmetry, the models of quasi‐symmetry and quasi‐independence, and modeling agreement.
S
tat
X
act
Authors: Mehta Cyrus R. , Patel Nitin R. , Senchaudhuri Pralay , Corcoran Christopher D.
Published: September 2014

StatXact is a software package providing exact
P
values and confidence limits for categorical and nonparametric data, including contingency tables. Computations are based on permutation distributions of appropriate statistics. An associated package, LogXact, deals with binary data in the presence of covariates using a logistic regression model.
Computer‐Aided Diagnosis
Authors: Weiner M. G. , Pifer Eric , Williams Sankey V.
Published: September 2014

A provider's ability to make correct decisions regarding patient care is predicated on the correct identification of a patient's diagnoses. However, the process of developing diagnostic certainty remains a challenging task despite an increasingly sophisticated array of available diagnostic modalities and techniques. Clinicians need support to integrate a broad range of findings from these tools along with a patient's symptoms and signs. This chapter discusses the evolution and utility of computer‐aided diagnostic decision support systems including recent developments in neural networks, microarray technology, and syndromic surveillance.
Normal Clinical Values, Design of a Study
Authors: Harris Eugene K.
Published: September 2014

This entry describes the estimation of normal ranges for clinical variables. A key issue is the identification of the appropriately healthy segment of the general population, and ways in which it can be sampled without bias. Techniques such as convenience sampling often do involve potential bias. Topics such as exclusion and partitioning criteria for the sampling are described. Sample size determination for establishing normal limits is reviewed.
Normal Clinical Values, Reference Intervals for
Authors: Royston Patrick
Published: September 2014

This entry describes the estimation of reference intervals for clinical variables. The appropriateness of various healthy populations to establish such intervals is discussed. The design and sample size of reference interval studies are reviewed. Estimation of the intervals may be done using nonparametric or parametric methods, and may be carried out in subgroups or take covariates into account. Goodness of fit to the data and identification of outliers are touched upon. Estimation of reference intervals is usually iterative, and specialized software to carry out the calculations is identified.
Risk Assessment in Clinical Decision Making
Authors: Shannon H. S. , Jadad A.
Published: September 2014

This article discusses the technical definition of risk and focuses on risks to patients in a clinical setting, particularly of events involving physical harm due to therapeutic intervention. It is shown that risk estimation involves both calculation of the risks and benefits involved, but also includes elements of risk communication, perception, and acceptance.
Utility in Health Studies
Authors: Torrance George W.
Published: September 2014

Utility is a quantitative expression of strength of preference. The more something is preferred, the greater is its utility. Formal utility theory for decision making under uncertainty was defined by von Neumann & Morgenstern. Utilities in their theory are measured using the standard gamble. Alternatively, time trade‐off and visual analog scales are used to measure preferences, and these also are sometimes called utilities. Utilities are an integrative measure of health‐related quality of life. Utilities, representing quality of life can be combined with quantity of life to form quality‐adjusted life years. These, in turn, are used in cost–utility analyses. For most clinical studies, the simplest and the preferred way to measure utilities is to use one of the multiattribute health status classification systems that include a utility scoring formula; for example, EuroQol EQ‐5D, Health Utilities Index, or Quality of Well‐Being Scale.
Intention to Treat Analysis: Basic
Authors: Ellenberg Jonas H.
Published: September 2014

In a randomized clinical trial design for the comparison of treatments, subjects are randomly assigned to different treatments. The intention‐to‐treat principle requires that any comparison of the treatments be based upon comparison of all patients in the treatment groups to which they were randomly assigned. This approach maintains the benefits of randomization, one of which is that the characteristics of the group of patients receiving the different treatments should be roughly equivalent at the onset of the trial.

Several conditions can influence the actual versus intended protocol under which subjects are studied including: nonadherence to the assigned treatment regimen, or termination of participation prior to outcome measurement. Investigators may attempt to compare groups defined by factors that might be influenced by the treatments under test, for example, the comparison of only those patients in the trial that actually complied with the prescribed treatments. Any observed differences among treatment groups constructed in this manner may be due not to treatment, but to factors associated with compliance.
Drug Approval and Regulation
Authors: Lewis John A. , Jones David R.
Published: September 2014

Before it can be marketed, any new medicinal product generally requires the regulatory approval of the appropriate governmental agency. This article describes the contribution of statistics and statisticians to the regulatory process. It addresses the role of statisticians within regulatory agencies worldwide. It provides an explanation of the various ways in which statistical assessment is carried out within the major regulatory agencies. It concludes by considering the statistical aspects of the regulatory application written by the applicant company to support their claims for their medicinal product.
Number Needed to Treat: Overview
Authors: Bender Ralf
Published: September 2014

The number needed to treat (NNT) has gained much attention in the past years as a useful way of reporting the results of randomized clinical trials. Defined as the reciprocal of the absolute risk reduction (ARR), NNT is the average number of patients needed to be treated to prevent an adverse outcome in one additional patient. This measure has been applied to a number of other research areas involving the development of more sophisticated techniques to calculate and interpret NNTs in medical research. The most important application areas are screening studies, public health research, epidemiology (case–control and cohort studies), crossover studies, studies measuring continuous and survival time data, risk–benefit analyses, and systematic reviews. An overview of the recent developments regarding NNT is given as well as a discussion of the advantages, drawbacks, and usefulness of the NNT statistic as a reporting tool.
Postmarketing Surveillance of New Drugs and Assessment of Risk
Authors: Anello C. , O'Neill R.
Published: September 2014

The goal of this article is to review the role, rationale, objectives, and design of quantitative methods of surveillance strategies developed for drug safety assessment over the past 30 years. Emphasis is placed on the quantitative methods that have been developed in response to regulatory requirements to monitor adverse drug event data for the purpose of assessing potential risk.
Sequential Analysis ‐ Further Developments
Authors: Lai T. L.
Published: September 2014

This article gives an overview of sequential analysis and its applications to data and safety monitoring of clinical trials, determination of median effective dose and maximum tolerated dose, fixed‐width confidence intervals, and adaptive design of biomedical studies.
Cooperative Cancer Trials
Authors: Rubinstein Lawrence V. , Ungerleider Richard S.
Published: September 2014

The clinical trials cooperative group program was established by the US National Cancer Institute in 1955. The number of groups increased to 18 by 1985 and then decreased to 11. This article describes the work of the groups since the mid‐1980s. It covers Phase I, II, and III studies, in a range of histologic groups, including breast, colorectal, lung, ovary, central nervous system, and prostate. Developments include intergroup studies, formalized interim monitoring systems, and increased emphasis on pediatric cancer and cancer prevention and control. Various methodologic developments are summarized.
Cooperative Heart Disease Trials
Authors: Geller Nancy L. , Friedman Lawrence M.
Published: September 2014

This review of multicenter trials in heart disease sponsored by the United States National Heart, Lung, and Blood Institute illustrates some design and biostatistical advances that selected trials have developed or used effectively to answer important public health questions. Many of the design, organizational characteristics and methods for monitoring modern clinical trials were implemented as a result of the Greenberg Report completed in 1967 and were first used in the Coronary Drug Project during the 1960s. While more recent studies employed more efficient organizations, newer designs, and newer monitoring guidelines, the genesis of multicenter clinical trial practice in heart disease was outlined in the 1960s and that early structure has endured the test of time.
Salk Vaccine
Authors: Meier Paul , Smith Rebecca Pringle
Published: September 2014

The Salk vaccine was a killed‐virus vaccine against poliomyelitis, an uncommon but dangerous disease during the early twentieth century. In 1954, over one million children in the United States took part in a study of the effectiveness of the vaccine. Over 700 000 entered a randomized double‐blind study. Over a million were in a study in which second‐grade children were offered the vaccine and first‐ and third‐ grade children were observed as controls. In both forms of study, the incidence was reduced by about half in the groups that received the vaccine. The problems of design, execution, and interpretation are described in some detail.
UniversityGroupDiabetesProgram (UGDP)
Authors: Knatterud Genell L.
Published: September 2014

The University Group Diabetes Program (UGDP) was one of the first large‐scale cooperative clinical trials designed and implemented in the United States. The three objectives of the program were (1) evaluation of the effects on the development of vascular complications in adult‐onset noninsulin dependent diabetes of the different hypoglycemic treatments accepted in clinical practice in late 1950s and early 1960s (diet, insulin, and oral agents); (2) study of the natural history of vascular disease in adult‐onset diabetics; and (3) development of methods appropriate for the design and conduct of cooperative clinical trials.

The study provided little evidence that insulin treatment was any better than diet alone in altering the course of vascular complications in stable adult‐onset diabetes. This was true whether insulin was given in a fixed dose or adjusted to maintain blood glucose within certain levels.

Tolbutamide therapy was discontinued prior to the end of follow‐up for patients in the diet and insulin groups, when it became apparent that there was an excess cardiovascular mortality for patients in the tolbutamide treatment group compared to patients assigned to placebo or diet alone. About two years after the decision was made concerning tolbutamide, patients assigned to phenformin treatment were also discontinued when the observed mortality for all causes and from cardiovascular causes was higher than mortality in any of the other treatment groups.

Since the last UGDP major report was published in 1992, there has been little evidence provided to indicate benefit of therapy for the prevention of macrovascular diabetic complications in adult‐onset diabetes. The methods and procedures used in the UGDP Coordinating Center provided a framework for standards of operations for future Coordinating Centers of multicenter clinical trials.
Antithetic Variable
Authors: T. Morgan Byron J.
Published: September 2014

A brief overview of this concept and its application is given.
Computer‐Assisted Interviewing
Authors: Brinkley Eden , Finlay Elizabeth , Wensing Fred
Published: September 2014

Computer‐assisted interviewing (CAI) refers to the use of a computer to aid the interview process in survey data collection. The computer generally presents the question text on the screen, along with allowable response categories, and the interviewer or respondent records the answers directly into the computer. The computer can also be used to undertake various forms of processing at the time of the interview, and can facilitate electronic data transfer. The advantages and disadvantages of CAI relative to paper and pencil interviewing (PAPI) are discussed briefly.
Database Systems
Authors: Walker N. , Lawrence Catherine
Published: September 2014

The word “database” is used here in two overlapping ways to refer to a collection of related data and its storage method, or to programming system software used to organize and integrate a collection of related data. It is management, access, and control (including efficiency and recoverability), and not necessarily size, which distinguishes a database from a data set. This article concentrates on database software: choosing a database management system (DBMS), designing databases, object‐oriented approaches, and closes with an overview of resources that might be helpful.
Graphical Displays
Authors: Martin Michael A. , Welsh A. H.
Published: September 2014

Graphical methods are fundamental tools of modern data analysis. Graphics are useful for communicating complex information quickly and easily, and are thus excellent tools for describing data and for providing direction in data analysis. They are essential tools for building statistical models from data, and for assessing model adequacy. This article surveys the use of statistical graphics in describing data and for statistical modeling, including models for relating variables, for spatiotemporal dependence, and for survival data. Graphics are also critical tools for visualizing and analyzing high‐dimensional data. The article illustrates the use of graphics in statistical modeling through numerous examples drawn from the life sciences.
Numerical Integration
Authors: Smyth Gordon K.
Published: September 2014

This article focuses on the process of approximating a definite integral from values of the integrand when exact mathematical integration is not available. This problem arises in statistics when marginal density functions or expected values of random variables are required. The article describes classical univariate quadrature methods, including the trapezoidal rule, Simpson's rule, Newton–Cotes formulas, Clenshaw–Curtis integration, and Gaussian quadrature. Refinements including adaptive methods, treatment of singularities, and progressive rules of the Gaussian type are also mentioned. A survey is given of the possibilities and limitations of multiple integration methods, including product rules, globally adaptive methods, rules of polynomial degree, lattice methods, and Monte Carlo integration. Detailed pointers are given to available software.
Simulation
Authors: Morgan Byron J.T.
Published: September 2014

Computer simulation is widely used to mimic the rules of complex systems. The resulting simulation models may then be used to study the effect of changes to those systems.
Spline Smoothing
Authors: Turlach Berwin A.
Published: September 2014

Over the past few decades, several methods for fitting smooth curves to data have been proposed and intensively studied. Among these methods are kernel smoothers, orthogonal series estimators, wavelet smoothers, and spline smoothers. This entry provides a brief description of the commonly used spline smoothers – regression splines, smoothing splines, and penalized splines.
S
‐
PLUS
and
S
Authors: Venables W. N.
Published: September 2014

S‐PLUS is a commercial software environment for data analysis and graphics based on the programming language S, designed and initially implemented by John M. Chambers at Bell Laboratories. In addition to a large suite of software for standard tasks, the system aims to provide users with the means to implement new techniques or combine standard methodology in possibly new ways. The language is interpreted rather than compiled, and has some object‐oriented features. Versions of S‐PLUS are current for Unix, Linux, and Windows. The system has a command‐line interface only, but the Windows implementation offers a graphical user interface as well.
Spreadsheet
Authors: Hunt N.
Published: September 2014

The term
spreadsheet
is derived from the sheet of paper employed by an accountant to set out financial calculations, often so large that it had to be spread out on a table. The first electronic spreadsheet, VisiCalc, was developed in 1979 by Bob Frankston and Dan Bricklin at Harvard. Originally designed as a business problem‐solving tool, the remarkable versatility of the spreadsheet package has made it a popular working environment among computer users in many professions. The most commonly used spreadsheet packages include Excel, Lotus 1‐2‐3, Quattro Pro, and SuperCalc.
Support Vector Machines
Authors: Schölkopf Bernhard , Smola Alex
Published: September 2014

We describe the central ideas of Support Vector (SV) learning in a nutshell. This includes the notion of learning from data via risk minimization and the notion of kernels as an efficient means of encoding prior information. We give an overview over the optimization problems arising from SV classification and discuss various extensions such as kernel PCA and automatic margin adaptation.
Resampling Procedures for Sample Surveys
Authors: Frankel Martin R.
Published: September 2014

This entry discusses the two basic resampling techniques that have found widespread application in survey sampling: Jackknife Repeated Replication (JRR) and Balanced Repeated Replication (BRR). To date, resampling methods based on the bootstrap have found only limited application in samples, which involve complex (stratified and clustered) sample design.
Back‐Calculation
Authors: Bacchetti Peter
Published: September 2014

Back‐calculation estimates past infection rates of an epidemic disease by working backward from observed incidence. This requires: external knowledge about the distribution of incubation times between infection and disease, possibly including how it is changing over time; incidence data that have been adjusted for reporting delay, underreporting, and possibly seasonal effects; and a parametric model or smoothness assumption that imposes some structure on past infection rates. In the case of human immunodeficiency virus, sensitivity analyses have found that uncertainty in back‐calculation is mainly due to uncertainty in these inputs. Projected incidence is obtained by applying the assumed incubation distribution to the estimated (and extrapolated) infection rates, but direct extrapolation of observed incidence may do as well or better in some circumstances.
Conception, Models for
Authors: Weinberg Clarice R.
Published: September 2014

Time to conception offers a stochastic surrogate for fertility, in that subfertile couples not using contraception tend to take longer to conceive than do fertile couples. Couples can be studied retrospectively, by asking about how long it took them to achieve an index pregnancy, or prospectively, by recruiting couples during or prior to an attempt to conceive. The resulting data play out in discrete time, as measured by the number of menstrual cycles required. Because of heterogeneity among couples in their fecundability (per cycle probability of conception), the data are overdispersed compared to the geometric. This overdispersion can be modeled parametrically (e.g. with a beta‐geometric) or nonparametrically (based on the moments of the dispersed but unknown underlying fecundability distribution). If more detailed data are available, on days with intercourse and time of ovulation, then the models for conception can additionally incorporate the strong effects of timing.
Hidden Markov Models: Biostatistical Applications
Authors: Churchill Gary A.
Published: September 2014

Hidden Markov models are a generalization of the finite mixture model that allow for dependence in a sequence of observations via Markovian dependence of an unobserved (hidden) sequence of states. Inference problems associated with hidden Markov models include parameter estimation and restoration of the hidden state sequence. The recursive algorithms employed to solve these problems are related to the Kalman filter and have spawned a wide variety of applications. Areas of application range from speech recognition and signal processing through analysis of molecular sequence data.
Mathematical Biology, Overview
Authors: Matthysse Steven
Published: September 2014

Mathematical biology has become a flourishing field in which real mathematics combines with real biology. The most distinctive contribution of mathematics to biology lies, not in simulation, but in proving theorems about models that, although simplified, are biologically adequate. Five examples are selected as examples; illustrating diverse ways that mathematical reasoning can enhance our understanding of biological systems.
Migration Processes
Authors: Chiang Chin Long , Ball Frank
Published: September 2014

A migration process describes the temporal evolution of a population of individuals who reside in colonies. There is both immigration and emigration, and also migration between colonies. However, there is no reproduction in the population. In the models described in this article, individuals behave independently according to continuous time Markov processes. A single colony model is treated in some detail and the distribution of population size at any given time is derived. Extension to a population comprising
m
 > 1 colonies is described. The single colony model is used to motivate a survival model for an individual who is continuously exposed to a low level of toxic material.
Ornstein–Uhlenbeck Process: Overview
Authors: Blackwell Paul G.
Published: September 2014

The Ornstein–Uhlenbeck process (OUP) is a unique Gaussian, stationary diffusion process. Originating as a model for the Brownian motion of a particle, it has a wide range of applications in biology and elsewhere. These include the modeling of membrane potentials of nerve cells, modeling the location or velocity of anything from whole organisms to individual cells and the use of the OUP as a limiting approximation to population models in epidemiology and genetics. Another area of application is the large‐sample behavior of likelihood ratios for multiple genetic markers. As well as including discussion of these (and other) applications, this article describes likelihood‐based inference and mathematical properties such as first passage times.
Queuing Processes
Authors: Chiang Chin Long , Ball Frank
Published: September 2014

This article is primarily concerned with two queuing models,M/M/sandM/G/1, both of which assume Poisson (M) arrivals. In theM/M/squeue, there aresservers and the service time is exponentially (M) distributed. In theM/G/1 queue, there is a single server and the service time follows an arbitrary but specified distribution (G). For theM/M/smodel, the equilibrium distribution of the number of customers in the system is derived, as are other equilibrium properties of the system, such as the mean waiting time of a typical customer and the probability that a given server is busy. Two approaches to analyzingM/G/1 queues are outlined,embeddingand themethod of stages. Formulae are given for the equilibrium mean number of customers in the system and the equilibrium mean total time spent in the system by a typical customer, and properties of a typical busy period of the server are outlined. TheM/M/∞ model, in which there are infinitely many servers, is described briefly.
Renewal Processes
Authors: Chiang Chin Long , Ball Frank
Published: September 2014

Renewal theory is concerned with a sequence of events in which the waiting times between successive events (known as renewal times) are independent and identically distributed. This article provides a brief introduction to the properties of renewal processes, such as the time until thenth renewal and the number of renewals by timet, treating the cases of discrete and continuous renewal time distributions separately. In the latter case, the limiting distributions as of the backward and forward recurrence times at timet(i.e. the time since the previous renewal and the time until the next renewal) are considered. Delayed, equilibrium and alternating renewal processes are also described briefly, as are renewal reward processes. The theory is illustrated by a few examples.
Semi‐Markov Processes ‐ Further Results
Authors: Mode Charles J.
Published: September 2014

Semi‐Markov processes are useful generalizations of a class of stochastic processes commonly referred to as Markov jump processes in continuous time. In most of the applications of this class of processes in biostatistics and biology, the state space is chosen as a discrete set with finite or countable elements that have biological meaning. One of the properties of Markov processes is that it follows from the Markov property in continuous time that the sojourn time (holding time) in any state has an exponential distribution with a parameter depending on that state. Semi‐Markov processes are a generalization of Markov jump processes in that the distribution of the sojourn time in any state can, in principle, be arbitrary. Furthermore, in semi‐Markov processes the finite dimensional distributions of the sample paths of the process are modeled directly in terms of arbitrary distributions of sojourn times for each state and conditional distributions of a jump to another future state, given the present state. In many applications, this property makes it possible to set down a formula for the likelihood function of the data with relative ease. Briefly, the paper contains substantive examples and a description of the mathematical tools needed to work with semi‐Markov processes.
Shape Analysis
Authors: Dryden Ian L.
Published: September 2014

Shape is defined to be all the geometrical information that is invariant under a set of registration transformations. For example, the registration transformations might be translation, rotation, and scaling. We discuss Kendall's shape space for two‐dimensional sets of landmarks, which is the complex projective space. Probability distributions for planar shapes are reviewed and some approximate distributional results for higher‐dimensional cases are given. Practical shape inference using Procrustes analysis is discussed, and particular emphasis on multivariate analysis in tangent spaces is given. We discuss principal component shape analysis and hypothesis tests, and provide some examples from biology and medicine. Finally, we discuss some applications of shape analysis in image analysis.
Tumor Incidence Experiments
Authors: Dinse Gregg E.
Published: September 2014

This article describes a variety of methods for analyzing data from animal tumorigenesis experiments. The primary endpoint of interest is the tumor incidence rate. Both the estimation of tumor incidence rates and the comparison of these rates across treatment groups are discussed. Analyses are complicated by the fact that all onset times are censored when tumors are occult, censoring is informative unless tumors are completely nonlethal, and biases arise if proper adjustment is not made for survival differences across groups. Existing methods address such concerns by assuming a particular level of tumor lethality, assigning individual causes of death, requiring some animals to be sacrificed, specifying parametric models, or imposing functional restrictions. This article describes each of these approaches and summarizes the associated advantages and disadvantages.
Radiation Epidemiology
Authors: Ron Elaine , Lubin Jay H.
Published: September 2014

Radiation epidemiology is a specialized field of epidemiology that seeks to characterize and quantify the health risks associated with exposure to ionizing and nonionizing radiation. One of the principal aims of radiation epidemiology is to provide the human data needed to recommend or set protection standards for workers and the general public. The data also are used to estimate levels of risk from diagnostic radiation procedures, to indicate how radiotherapy protocols can be improved to reduce acute and long‐term side effects, to better understand individual susceptibility, and to learn more about disease mechanisms.
Bias from Exposure Effects on Controls
Authors: Gail Mitchell H.
Published: September 2014

In hospital‐based case–control studies, exposures in cases are compared with exposures in controls in the hospital, who have other diseases. Exposure odds ratios (and disease odds ratios) will be biased if the exposures under study are associated with the control diseases.
Bias from Survival in Prevalent Case–Control Studies
Authors: Gail Mitchell H.
Published: September 2014

Odds ratios comparing prevalent cases with controls may be biased if the exposures of interest affect the survival of incident cases.
Bias in Cohort Studies
Authors: Hill Holly A. , Kleinbaum David G.
Published: September 2014

Selection biases can arise in cohort studies if the analysis cohort is not representative of the cohort of interest, either as a result of biased sampling, selective losses to follow‐up, or nonresponse. A particular type of biased sampling can arise when one studies a cohort with prevalent rather than incident disease. Misclassification of disease or exposure status can also result in bias, particularly when the misclassifications are differential. Confounding also results in bias in cohort studies.
Bias, Nondifferential
Authors: Gail Mitchell H.
Published: September 2014

The nondifferential bias is bias that affects each exposure (or treatment) group in such a way that the exposure effect measure remains unbiased. A nondifferential error process may induce nondifferential bias with respect to the measure of exposure effect but not another.
Biased Sampling of Cohorts
Authors: Brookmeyer Ron
Published: September 2014

Selection biases can distort findings from cohort studies. Self‐selection into a study cohort can yield results that are not representative of the target population. Screening a population to detect persons with high blood pressure, for example, can lead to misleading estimates of the effects of antihypertensive medication, in the absence of a control group, because subsequent blood pressure measurements will tend to be lower solely by virtue of the selection process. Studying a selected group of workers who were healthy before hiring can obscure the effects of occupational exposures in comparison to a study of the general population. Incomplete follow‐up of cohort members can also lead to biased estimates of cumulative risk and relative risk. Some time‐related biases can affect cohort studies. Left truncation arises when one only studies subjects who have survived to a certain time; the analysis must take left truncation into account to avoid bias. Prevalent cohorts consist of subjects who have a prevalent disease at the time of enrollment. Because the age at disease onset is often unknown in prevalent cohorts, estimates of cumulative and relative risks are subject to various biases.
Case Series, Case Reports
Authors: Gail Mitchell H.
Published: September 2014

A case report refers to a description of a person with a particular disease, and a case series refers to a series of case reports, often representing all cases with the given disease in a defined time period in a particular practice, clinic, or geographic area.
Case‐Control Study, Population Based
Authors: Gail Mitchell H.
Published: September 2014

A population‐based
case–control study
is based on a well‐defined source population from which all cases that arise in a given time period can be enumerated.
Controls
consist of
random samples
of persons without the disease of interest from the source population. Cases consist of all cases or a random sample of those cases. This study design can, in principle, reduce selection bias and, when combined with census data on the source population, yield information on absolute as well as relative risk.
Case–Control Study, Prevalent
Authors: Lang Janet M.
Published: September 2014

Case–control studies based on incident cases are well suited to studies of etiologic factors that affect disease onset. A prevalent case–control study includes subjects who have survived to the time of the study and had a previous history of disease onset. Associations with prevalent disease may therefore reflect not only disease onset but also influences on survival following disease onset. In a steady state population, the prevalence odds ratio for an exposure equals the incidence odds ratio times the ratio of mean survival times following disease onset. We discuss the interpretation of prevalent case–control studies, with examples of nosocomial infection and of neural tube defects.
Case–Control Study, Two‐Phase
Authors: Breslow Norman E.
Published: September 2014

Two‐phase case–control studies, sometimes called two‐stage case–control studies, employ a double sampling design. In the first phase, cases and controls are sampled from a general population, and some stratifying information, such as a crude measure of exposure (e.g. smoker or nonsmoker) is obtained. At the second phase, subsamples are selected within strata defined by disease status (case or control) and by other stratifying information, and more refined information on exposure and other covariates is obtained for the subsample. Judicious allocation of the subsampling fractions can improve the efficiency of estimation for main effects and especially for interactions. Two‐phase designs can also be used to validate error‐prone exposure assessments by including “gold standard” measurements in a second phase validation sample. We discuss the efficiency of the design and methods of estimation, including fully efficient semiparametric estimators and pseudo‐score estimators.
Clustering
Authors: Cuzick Jack
Published: September 2014

Clustering problems arise in a number of epidemiological applications and the temporal and spatial pattern of disease incidence has been a major stimulus for the development of this subject. Evaluating apparent ‘clusters’ of disease in small communities has led to the development of a number of tests for spatial clustering. The study of unexpected outbreaks of disease has led to tests of temporal clustering and also of space–time clustering where closeness both temporally and geographically is required.
Collapsibility
Authors: Greenland Sander
Published: September 2014

Consider theI × J × Kcontingency table representing the joint distribution of three discrete variables,X,Y, andZ, theI × Jmarginal table representing the joint distribution ofXandY, and the set of conditionalI × Jsubtables (strata) representing the joint distributions ofXandYwithin levels ofZ. A measure of association ofXandYis said to bestrictly collapsibleacross Z if it is constant across the conditional subtablesandthis constant value equals the value obtained from the marginal table. Noncollapsibility (violation of collapsibility) is sometimes referred to as Simpson's paradox after a celebrated article by Simpson, but the same phenomenon had been discussed by earlier authors, including Yule. The termcollapsibility, however, seems to have arisen later in the work of Bishop and colleagues.
Competing Risks: Theory
Authors: Tsiatis A. A.
Published: September 2014

“Competing risks” refers to the study of mortality patterns in a population of individuals, all subject to the samek ≥ 2 causes of death. The object is to isolate the effects of a given risk or subset of risks. Certain quantities, such as the “crude probability” of dying of a particular disease in the presence of the other competing risks, and cause‐specific hazard rates, and functions of them, are estimable from observable data without unverifiable assumptions. Other quantities, such as the “net probability” of dying of a disease in the absence of other causes of death cannot be estimated without unverifiable assumptions on the joint distribution of times to death from the various causes of death. Bounds on the net probability are available, however. We review these concepts and related methods of estimation and inference.
Confounder Summary Score
Authors: Greenland Sander
Published: September 2014

Consider an observational study of the effect of an “exposure” variable
X
on an outcome variable
Y
in which multiple confounders must be controlled. Simultaneous stratification on all observed confounder combinations may lead to many uninformative strata (i.e. strata in which there is either no variation in the exposure or no variation in the outcome). The usual method of coping with this problem is to estimate exposure effects from coefficients in a regression model for the dependence of the outcome on the exposure and confounders. Confounder summary scores are alternatives that use fitted models to define strata.
Controls
Authors: Gail Mitchell H.
Published: September 2014

Controls are subjects against whom comparison is made in experimental or observational studies. Various types of controls are described.
Cornfield's Inequality
Authors: Gastwirth Joseph L. , Krieger Abba M. , Rosenbaum Paul R.
Published: September 2014

Cornfield developed an inequality on the observed relative risk,RO, between an exposed and unexposed group, which could be explained by an unmeasured confounder,U. To be precise,ROis no greater than the ratio of the prevalence ofUin the exposed to that in the unexposed population. It is also true thatRO ≤ RU, whereRUis the ratio of risk in those withUcompared to those withoutU. We discuss a sharper bound onRO, which can be computed when information onRUand on the prevalences ofUin the exposed and unexposed populations are available. We also consider related bounds for risk differences and sensitivity analyses for tests of hypotheses in the presence of an unmeasured confounder.
Crude Risk
Authors: Gail Mitchell H.
Published: September 2014

Crude risk (or absolute risk) is defined and contrasted with “net risk”, which refers to the probability of developing a given disease if competing risks are eliminated. Crude risk may also refer to risk in a heterogeneous population.
Cumulative Incidence Rate
Authors: Gail Mitchell H.
Published: September 2014

The cumulative incidence rate is a cumulative hazard for a specific disease and should be distinguished from crude (or absolute) risk.
Cumulative Incidence Ratio
Authors: Gail Mitchell H.
Published: September 2014

The cumulative incidence ratio is a relative risk.
Dose–Response
Authors: Gail Mitchell H.
Published: September 2014

Dose–response refers to a relationship between an amount of exposure or treatment and the degree or probability of an outcome in an individual or population.
Effect Modification
Authors: McKnight Barbara
Published: September 2014

Effect modification occurs when a measure of the association between an exposure and the risk of disease is modified, or changes, with values of a third variable called the
effect modifier
. For a given measure of association between exposure and disease risk, there is usually a corresponding regression model scale on which the presence of effect modification corresponds to the presence of statistical interaction between exposure and the effect modifying variable. This correspondence is described for several additive and multiplicative measures of association.
Exposure Effect
Authors: Gail Mitchell H.
Published: September 2014

An exposure effect is a quantitative measure of the impact of an exposure on an outcome measure. Exposure effects, such as relative risk or risk difference, correspond to coefficients of terms in regression models. In the presence of interactions, an exposure effect defends on the levels of other factors, Exposure effects based on regression models can be regarded as summary measures of individual‐level causal exposure effects.
Frequency Matching
Authors: Gail Mitchell H.
Published: September 2014

Frequency matching is a sampling design used in case–control studies to assure that cases and controls have the same distributions over strata defined by matching factors. Frequency matching is also used in cohort studies to insure that exposed and unexposed individuals have the same distributions over strata defined by known risk factors.
Incidence Density Ratio
Authors: Gail Mitchell H.
Published: September 2014

The incidence density ratio is the ratio of the incidence density in one group to that in another group.
Incident Case
Authors: Gail Mitchell H.
Published: September 2014

An incident case is a subject who has just developed a disease of interest for the first time.
Interviewer Bias
Authors: Gail Mitchell H.
Published: September 2014

Interviewer bias is a type of information bias that arises when an interviewer consciously or unconsciously elicits inaccurate information from study subjects. Methods to reduce the likelihood of interviewer bias are mentioned.
Meta‐Analysis in Epidemiology
Authors: Stroup Donna F. , Thacker Stephen B.
Published: September 2014

Meta‐analysis is a quantitative approach for systematically combining the results of previous research in order to arrive at conclusions about the body of research. The pressure for timely, evidence‐based decisions, and the explosion of the information in the scientific literature have resulted in an increased number of published meta‐analysis. Statistical methods are integral to each step of a meta‐analysis. In this chapter, we define meta‐analysis in the context of scientific synthesis, illustrate uses of Meta‐analysis in epidemiology and public health, describe the steps in a meta‐analysis, and illustrate some specific statistical issues, such as bias, and heterogeneity. We illustrate these steps with a case study of the effects of duration of estrogen on breast cancer risk and discuss statistical software and related internet resources. Statistical issues are critical to properly understanding and interpreting the results from meta‐analysis.
Propensity Score
Authors: Rosenbaum Paul R.
Published: September 2014

The propensity score is the conditional probability of exposure to treatment rather than control given observed covariates, or more generally, the conditional probability of selection into a group given observed covariates. It is used in an effort to adjust for nonrandom treatment assignment or nonrandom selection. Matching or stratifying on the scalar propensity score tends to balance the many observed covariates used to construct the score. If it suffices to adjust for the observed covariates, then it also suffices to adjust for the propensity score.
Smoothing Methods in Epidemiology
Authors: Greenland Sander
Published: September 2014

Standard statistical methods (such as regression analysis) presume the data are generated by an identifiable random process, and attempt to model that process in a parsimonious fashion. In contrast, observational data in the health sciences are generated by complex, nonidentified, and largely nonrandom mechanisms, and are analyzed to form inferences on latent structures. Despite this gap between the methods and reality, most observational data analysis comprises application of standard methods, followed by narrative discussion of the problems entailed by doing so. Alternative approaches employ latent‐structure models that include components for nonidentified mechanisms. Standard methods can still be useful, however, provided their modeling philosophy is modified to encourage preservation of structure, rather than achieving parsimonious description. With this modification they can be viewed as smoothing or filtering methods for separating noise from signal before the task of latent‐structure modeling begins. A hierarchical‐modeling implementation can be carried out with popular software.
Time Lag Effect
Authors: Gail Mitchell H.
Published: September 2014

The term
time lag effect
refers to the delay between the time of an intervention or exposure onset, such as the date on which a person begins smoking cigarettes, and the subsequent development of a health outcome, such as the diagnosis of lung cancer.
Validity and Generalizability in Epidemiologic Studies
Authors: Rothman Kenneth J. , Greenland Sander
Published: September 2014

The validity of a study of humans is often separated into two components: the validity of the inferences as they pertain to members of the source population (internal validity), and the validity of the inferences as they pertain to people outside that population (external validity). Scientific generalization extends beyond statistical generalization of study results to the formulation of abstract concepts relating the study factors. Internal validity is a prerequisite for the study to contribute usefully to this process of abstraction, but the generalization process is otherwise separate from the concerns of internal validity and the mechanics of the study design.
Sample Size in Epidemiologic Studies
Authors: Liu Guanghan
Published: September 2014

This article provides a broad overview of sample size determination methods for epidemiologic studies. Concepts related to sample size and power calculations are introduced. Formulas for sample size calculations are given for cohort and case–control (unmatched or matched) studies with binomial and Poisson outcomes. Methods for sample size calculations in survival studies and longitudinal studies are also presented. Factors that may affect the sample size and study power are discussed. A brief review of the computational methods and software packages commonly used for sample size calculations is also provided.
Administrative Databases
Authors: Roos Leslie L. , Romano Patrick S. , Fergusson Patricia
Published: September 2014

Administrative databases are often used to manage a health care system or to investigate health research questions. The data may be derived from population registries, vital statistics or other records of life events, or from health claims and services data. Questions that may be addressed by administrative datasets include determinants of variation in health care utilization, costs and outcomes, according to factors associated with individual patients, patient characteristics, types of health care organization, or geographic factors. Strengths and weakness of this approach are reviewed, and several new directions are identified.
Diagnostic Tests, Evaluation of
Authors: Begg Colin B.
Published: September 2014

This entry reviews issues in the evaluation of individual diagnostic tests, or the comparison of two alternative tests. Measures of test accuracy are described, and the receiver operating characteristic (ROC) curve method of analysis is mentioned. Various biases that may affect the evaluation of a diagnostic test are reviewed. Parametric and nonparametric methods to compare two tests are indicated, and issues concerning the design of studies to evaluate diagnostic tests are summarized.