bluuebunny/crossref_metadata_2025_split huggingface dataset:
Meta‐Analysis of Diagnostic Tests
Authors: Macaskill Petra , Glasziou Paul , Irwig Les 
Published: September 2014

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

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.
Pharmacoepidemiology, Study Designs
Authors: Strom B. L. 
Published: September 2014

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

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

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

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

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

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

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

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

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

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

Abstract

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

Let
p

ij

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

j

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

ij

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

i

to
R

d

,
Q

id

(0,
t
), are also given.

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

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

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

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

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

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

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

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

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

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.
Epidemic Models, Stochastic
Authors: Gani J. 
Published: September 2014

AbstractThis article describes the most commonly used stochastic epidemic models. For discrete time, the Greenwood and Reed–Frost chain binomials are outlined, with examples. In continuous time, the simple SI, the general SIR and the carrier‐borne CSR models are discussed, and Whittle's Threshold Theorem stated. The article concludes with some references to recent literature.
Health Care Utilization and Behavior, Models of
Authors: Aday Lu Ann , Andersen Ronald M. 
Published: September 2014

Abstract
Models of health care utilization provide guidance for defining variables, specifying the relationships between them, and evaluating programs and policies concerned with access to and utilization of health care services. This chapter reviews four major types of utilization models: (a) models of patient decision making, grounded in sociological theory and research; (b) the health belief model, based in psychological theory; (c) economic models of the demand for medical care; and (d) the behavioral model of health services utilization that has guided much health services research on access to and utilization of health care services.
Health Care Utilization Data
Authors: Ash Arlene S. 
Published: September 2014

Abstract
Many important questions about the fairness, efficacy, or efficiency of health care delivery systems require utilization data. Ideally, we would like to have information on the nature, timing, cost, and setting of each person's utilization, and the ability to link that information with personal demographics, health‐status characteristics, and health outcomes, including declines in functional status and death. We discuss the major sources of health care utilization data in the United States and the characteristics that make them more or less useful for health services research.
Health Care Utilization Data, Analysis
Authors: Ash Arlene S. 
Published: September 2014

Abstract
Data on health care utilization are used to address important questions about what is done for whom, and can shed light on why and with what outcome, in the large segment of economic life that “delivers” health care services to populations. We describe the purposes of and major issues associated with common study types (e.g. small area variations, disparities, provider profiling, cost effectiveness). Methodologic issues include: defining the study scope in accordance with its purpose (data source, population, timeframe, units of analysis and units of inference); choosing appropriate models (dealing with clustered observations, skewness, heteroscedasticity, and “point masses” at zero); and addressing the “censoring” effect of death on cost.
Health Status Instruments, Measurement Properties of
Authors: SchÜnemann Holger , Juniper Elizabeth , Guyatt Gordon 
Published: September 2014

AbstractIn this article, we present the key measurement properties necessary for a useful health status instrument. These instruments can measure health status at a moment in time to differentiate between individuals (discriminative instruments) or they can measure change over time (evaluative instruments). We describe how investigators establish validity, responsiveness, and interpretability of a health status measure. Furthermore, we describe advantages and disadvantages of various administration methods for health status measures.
Instrumental Variables in Health Services Research
Authors: Newhouse Joseph P. 
Published: September 2014

Abstract
Instrumental variable (IV) estimators provide consistent estimates of regression coefficients if an explanatory variable or variables are correlated with the error term. They require a variable or variables that do not explain variation in the dependent variable directly but are correlated with those explanatory variables that are correlated with the error term. Although developed for applications in economics, they have recently become popular in health services research.
League Tables
Authors: Spiegelhalter David J. 
Published: September 2014

AbstractWhen comparing organizations, it is tempting to place them in rank order according to some summary statistic, and hence implicitly estimate their “true rank”. This can be a very unreliable exercise, which can be emphasized by making formal inference on the true rank. Mortality rates of New York surgeons are used for illustration. A comparative plot that does not involve explicit ranking is recommended.
Medicare Data
Authors: Ash Arlene S. , Virnig Beth 
Published: September 2014

Abstract
Medicare is a federal health insurance program for nearly all Americans aged 65 and over, for younger persons entitled to Social Security disability payments for at least 2 years, and for people with end‐stage renal disease (ESRD). It is administered by the Center for Medicare and Medicaid Services (CMS, formerly the Health Care Financing Administration, HCFA) in the US Department of Health and Human Services. In 2003, persons with entitlement due to these mandates numbered approximately 34.7 million, 5.8 million, and 300 000 respectively. Medicare data, especially the program's extensive repository of enrollment and administrative claims information, is a unique resource for health service research, due to the program's size, national representativeness, continuity of enrollment, presence of race and date‐of‐death data, well‐documented history of use in research, and the help available for researchers wishing to use the data for studies of potential benefit to Medicare beneficiaries.
Quality of Life and Health Status
Authors: Patrick Donald L. 
Published: September 2014

Abstract
Clinical studies, epidemiological investigations, population surveys, and clinical practices increasingly incorporate self‐reported measures of health status and quality of life. These help to determine whether treatments are doing more good than harm, whether health and quality are improving or worsening, and to see if health status differs between groups. Self‐reported outcomes are often compared to clinical or performance‐based measurements that remain the primary endpoints for most clinical trials and are important markers of disease, injury, and their trajectories. Self‐reported measures of health and quality of life, however, often have more meaning to the persons affected by disease or treatment. Because perceptions of health and illness influence what people do about their health (e.g. visit doctors, go to hospital, or ignore signs and symptoms), policy makers are also increasingly interested in self‐reported outcomes.
Small Area Variation Analysis
Authors: Diehr Paula 
Published: September 2014

Abstract
Small area variation analysis examines the variation in the rates of performance of a medical procedure, such as an appendectomy, across geographic areas. The approach is used to identify procedures or DRGs in which there is too much variation, with the goal of improving guidelines or identifying outlying providers. Unless the number of procedures per area is very large, variations analyses present a variety of statistical challenges.