bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stochastic processes including birth, death, Yule, Polya and divergent processes
Authors: Chiang Chin Long 
Published: September 2014

Abstract
Several articles in the Encyclopedia of Biostatistics describe various topics in stochastic process, while the present paper covers the main elements of the field, such as random walk, gambler's ruin, Markov chains, a general birth process, an equality in stochastic processes, death process and survival distribution, staging process, birth–death processes, and finite Markov processes. These topics together constitute an adequate representation of the field.
The main feature of this article is the explicit solution for every process presented, especially the K‐M epidemic model, the higher order transition probabilities in Markov chains, and the explicit solution for the Kolmogorov differential equations.
These solutions make it easy for the application of stochastic processes in research in biostatistics, epidemiology, health care, and medical sciences.
Tumor Growth
Authors: White R. Allen 
Published: September 2014

AbstractThe measurement of tumor growth rates has applications in such areas as tumor detection, prognosis, and control. The proliferation of tumors can be measured at different levels including gross measures of size, population progression through the cell cycle, and at the molecular level with biomarkers. Each of these methods has specific requirements and problems, particularly, including the heterogeneity both within and between tumors. Since tumors typically consist of a very heterogeneous mixture of normal and tumor cells, problems of interpretation of results must be made with care. Moreover, since tumors typically are measured only at detection with variable unknown histories and, after detection, at only a single or, at most, a few time points, problems in analysis require special methods.
Two‐Mutation Carcinogenesis Model
Authors: Moolgavkar Suresh H. 
Published: September 2014

AbstractThe 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

AbstractThe 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

Abstract

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

Abstract
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 in Case–Control Studies
Authors: Shapiro Samuel , Rosenberg Lynn 
Published: September 2014

Abstract
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).
Birth Defect Registries
Authors: Hanson James W. , P. Oakley Godfrey 
Published: September 2014

AbstractBirth 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

Abstract
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

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: September 2014

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, Sequential
Authors: O'Neill Robert T. 
Published: September 2014

AbstractRecruitment 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

Abstract
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

Abstract
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

Abstract
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

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.
Ecologic Study
Authors: Morgenstern Hal 
Published: September 2014

AbstractAn 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.
Geographic Epidemiology
Authors: Bithell John F. 
Published: September 2014

AbstractGeographical 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–Prevalence Relationships
Authors: Keiding Niels 
Published: September 2014

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: September 2014

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, Statistics inhealthy worker effect+
Authors: Axelson Olav 
Published: September 2014

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: September 2014

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: September 2014

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.
Counter‐Matching
Authors: Langholz Bryan 
Published: September 2014

Abstract
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.
Dose‐response in Pharmacoepidemiology
Authors: Murphy J. R. 
Published: September 2014

Abstract
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

AbstractDrug 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

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.
Incubation Period of Infectious Diseases
Authors: Brookmeyer Ron 
Published: September 2014

AbstractThe 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

Abstract
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.