bluuebunny/crossref_metadata_2025_split huggingface dataset:
Age–Period–Cohort Analysis
Authors: Holford Theodore R. 
Published: July 2005

Abstract
Age–period–cohort analysis refers to the interpretation of temporal trends in disease incidence or mortality rates in terms of the three time scales, age, calendar date (period), and year of birth (cohort). We present graphical and analytic methods for understanding trends in disease rates. A fundamental issue of identifiability arises in some models because of a linear constraint on these time scales: year of birth plus age equals the current calendar year. We review parametric and nonparametric approaches to estimate identifiable aspects of the data, such as the curvature of temporal trends.
Bias in Cohort Studies
Authors: Hill Holly A. , Kleinbaum David G. 
Published: July 2005

Abstract
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 in Observational Studies
Authors: Hill Holly A. , Kleinbaum David G. 
Published: July 2005

AbstractWe consider three sources of bias affecting observational studies and ways to address them: selection bias, information bias, and confounding. We discuss these sources of bias with respect to cohort and case–control designs. Even if a study is internally valid and not subject to these sources of bias, a judgment must be made whether the results generalize to some other target population.
Biased Sampling of Cohorts
Authors: Brookmeyer Ron 
Published: July 2005

AbstractSelection 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–Cohort Study
Authors: Langholz Bryan 
Published: July 2005

Abstract
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: July 2005

Abstract
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, Population‐Based
Authors: Gail Mitchell H. 
Published: July 2005

Abstract

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, Two‐Phase
Authors: Breslow Norman E. 
Published: July 2005

Abstract
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: July 2005

AbstractClustering 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.
Competing Risks
Authors: Tsiatis A. A. 
Published: July 2005

Abstract“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.
Confounding
Authors: Greenland Sander 
Published: July 2005

Abstract

The word
confounding
has been used to refer to at least three distinct concepts. In the oldest usage, confounding is a
bias
in estimating causal effects (
see
Causation
). This bias is sometimes informally described as a mixing of effects of extraneous factors (called
confounders
) with the effect of interest. This usage predominates in nonexperimental research, especially in epidemiology and sociology. In a second and more recent usage, confounding is a synonym for noncollapsibility (
see
Collapsibility
), although this usage is often limited to situations in which the parameter of interest is a causal effect. In a third usage, originating in the
experimental‐design
literature, confounding refers to inseparability of main effects and
interactions
under a particular design. The term
aliasing
is also sometimes used to refer to the latter concept; this usage is common in the
analysis of variance
literature.
Cornfield's Inequality
Authors: Gastwirth Joseph L. , Krieger Abba M. , Rosenbaum Paul R. 
Published: July 2005

AbstractCornfield 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.
Cross‐Sectional Study
Authors: Satten Glen A. , Grummer‐Strawn Laurence 
Published: July 2005

Abstract
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.
Incidence–Prevalence Relationships
Authors: Keiding Niels 
Published: July 2005

AbstractThe 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.
Misclassification Error
Authors: Kuha Jouni , Skinner Chris , Palmgren Juni 
Published: July 2005

AbstractMisclassification 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.
Occupational Epidemiology
Authors: Axelson Olav 
Published: July 2005

AbstractOccupational 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.
Random Digit Dialing Sampling for Case–Control Studies
Authors: Waksberg Joseph 
Published: July 2005

Abstract
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.
Sex Ratio at Birth
Authors: Armitage Peter 
Published: July 2005

Abstract
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.
Validity and Generalizability in Epidemiologic Studies
Authors: Rothman Kenneth J. , Greenland Sander 
Published: July 2005

Abstract
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: July 2005

AbstractThis 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: July 2005

AbstractAdministrative 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.
Agreement, Measurement of
Authors: Shoukri M. M. 
Published: July 2005

AbstractThis 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.
Bioequivalence
Authors: Endrenyi Laszlo 
Published: July 2005

Abstract
Bioequivalence is defined as the comparable bioavailability of alternative drug formulations. Kinetic measures of bioavailability are reviewed, leading to a discussion of the statistical assessment of bioequivalence. Parametric and nonparametric methods of testing are described. Current research areas including the estimation of individual bioequivalence and alternative measures for the evaluation of bioequivalence are reviewed.
Agreement, Modeling of Categorical
Authors: Barlow William 
Published: July 2005

AbstractThis entry describes models that can be used to describe the agreement between observers in ways that are more satisfactory than using simple single measures such as kappa. These models may take chance agreement and ordinal association into account, and the effects of covariates may also be incorporated. Modeling techniques include the conditional logistic regression model and the loglinear model when more than two categories are involved. More complex models are required if there are more than two raters.
Co‐Morbidity
Authors: Kraemer Helena Chmura 
Published: July 2005

Abstract
The term “co‐morbidity” is often used merely to indicate two disorders that occur together. However, how one deals statistically with co‐morbidity in research depends on the nature of the co‐morbidity. Definitions for clinical, epidemiologic, familial, and random co‐morbidity are presented here, and their impact on research design, analysis, and interpretation are briefly discussed.
Diagnostic Tests, Evaluation of
Authors: Begg Colin B. 
Published: July 2005

AbstractThis 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.
Epidemiology as Legal Evidence
Authors: Gastwirth Joseph L. 
Published: July 2005

Abstract
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.
Kappa
Authors: Cook Richard J. 
Published: July 2005

Abstract
The kappa statistic is a widely used measure for quantifying agreement between two methods of classification into nominal categories. Part of the appeal of the kappa statistic is that it is a “chance‐corrected” measure, which reflects agreement beyond the level expected by chance based on the marginal frequencies. Generalizations to weighted kappa statistics are appealing for the analysis of ordinal data. Generalizations of the original kappa statistic facilitate agreement analyses between three or more raters.
Diagnostic Tests, Multiple
Authors: Begg Colin B. 
Published: July 2005

AbstractIn 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.
Normal Clinical Values, Reference Intervals for
Authors: Royston Patrick 
Published: July 2005

AbstractThis 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.