bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hidden Markov Models: Biostatistical Applications
Authors: Churchill Gary A. 
Published: September 2014

Abstract
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.
Migration Processes
Authors: Chiang Chin Long , Ball Frank 
Published: September 2014

Abstract

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

AbstractThe 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

AbstractThis 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

AbstractRenewal 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

Abstract
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

AbstractShape 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

AbstractThis 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

AbstractRadiation 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 in Cohort Studies
Authors: Hill Holly A. , Kleinbaum David G. 
Published: September 2014

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.
Biased Sampling of Cohorts
Authors: Brookmeyer Ron 
Published: September 2014

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‐Control Study, Population Based
Authors: Gail Mitchell H. 
Published: September 2014

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, Prevalent
Authors: Lang Janet M. 
Published: September 2014

AbstractCase–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

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: September 2014

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.
Collapsibility
Authors: Greenland Sander 
Published: September 2014

AbstractConsider 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

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.
Confounder Summary Score
Authors: Greenland Sander 
Published: September 2014

Abstract

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.
Cornfield's Inequality
Authors: Gastwirth Joseph L. , Krieger Abba M. , Rosenbaum Paul R. 
Published: September 2014

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.
Effect Modification
Authors: McKnight Barbara 
Published: September 2014

Abstract

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

Abstract
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.
Meta‐Analysis in Epidemiology
Authors: Stroup Donna F. , Thacker Stephen B. 
Published: September 2014

AbstractMeta‐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

AbstractThe 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

Abstract
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.
Validity and Generalizability in Epidemiologic Studies
Authors: Rothman Kenneth J. , Greenland Sander 
Published: September 2014

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: September 2014

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: September 2014

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.
Diagnostic Tests, Evaluation of
Authors: Begg Colin B. 
Published: September 2014

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.
Drug Utilization Patterns
Authors: Abrahamowicz Michal , Tamblyn Robyn 
Published: September 2014

AbstractWe review methodological challenges in the observational studies of the patterns and outcomes of drug utilization. First, we address issues related to design and data collection in different studies that rely on administrative databases, discuss their limitations in accounting for clinical indication bias, and outline new opportunities created by the emergence of the integrated electronic patient records. Then, we focus on the different analytical issues related to such complexities as prescriber effects, assessing the impact of policy changes on drug utilization, description and classification of longitudinal patterns of medication use, alternative methods of measuring time‐dependent exposure status and medication dose, use of nonparametric modeling to represent time‐varying, lagged and/or nonlinear effects of exposure.