bluuebunny/crossref_metadata_2025 huggingface dataset:
Medical Devices
Authors: Campbell Gregory , Irony Telba Z. , Lao Chang S. , Li Heng , Pennello Gene , Vishnuvajjala R. Lakshmi , Yue Lilly Q.
Published: July 2005

A medical device is any item that treats or diagnoses a health condition but whose action is primarily not chemical or biological. The main focus concerns the evaluation of clinical studies to establish the safety and effectiveness of different kinds of medical devices. The design and analysis of medical device studies pose unique statistical challenges. Both nondiagnostic and diagnostic devices are considered. Nondiagnostic devices include therapeutic ones and implants, among others. Statistical issues for these kinds of devices include the placebo effect, sham controls, inability to perform blinded studies, noninferiority, survival analysis, repeated measures, and historical controls. Diagnostic devices pose a very diverse set of challenges, with markedly different design and analysis considerations. Special attention is devoted here to microarrays in particular. Bayesian approaches to medical device studies are discussed.
Missing Data in Clinical Trials
Authors: Murray Gordon D.
Published: July 2005

Missing data is an almost universal problem in clinical trials, and substantial bias can arise if the statistical analysis does not take proper account of the issue. The article reviews the different mechanisms that can lead to missing data, and describes appropriate approaches to the statistical analysis. As in so many aspects of clinical trials, the correct approach depends crucially on what question is being asked, and, in particular, whether the trial design is explanatory or pragmatic.
Multicenter Trials
Authors: Bryant John , Cronin Walter , Wieand Sam
Published: July 2005

The chapter deals with multicenter clinical trials, which are defined as studies involving two or more field sites conducted according to a common protocol that uses a single data coordinating center to receive, process, and analyze study data. Several examples are provided. Organizational, structural, and analytic aspects of trials, and guidelines for the conduct of such studies are discussed.
Quality of Life
Authors: Fairclough Diane L. , Ganz Patricia A.
Published: July 2005

In the health sciences, assessment of quality of life and other patient‐reported outcomes are now an integral component of patient‐focused research. Health‐related quality of life (HRQoL) is a multidimensional construct that assesses the extent to which one's usual or expected physical, emotional, and social well‐being are impacted by a medical condition or its treatment. HRQoL may be assessed using generic or disease‐specific instruments. These instruments consist of a set of questions, method of administration, instructions for administration, method of scoring and analysis, and interpretation. Validation of these instruments includes assessment of content validity, internal consistency, test–retest reliability, and discriminant and predictive validity. Some of the statistical issues that arise in the analysis of studies with HRQoL assessments are multiple endpoints, missing data (informative dropout), and interpretation (statistical versus clinical significance).
Sample Size Determination for Clinical Trials
Authors: Lakatos E.
Published: July 2005

Conceptual issues in sample size calculation for clinical trials are discussed. Each parameter and related issues are highlighted: type I and II errors and the balance between them, one versus two‐sided testing, the smallest clinically meaningful difference versus the anticipated treatment effect, endpoint selection, multiple comparisons, missing data, unequal allocation, repeated measures, equivalence, and group sequential trials. For survival trials, conceptual issues are discussed in further detail, including the binomial versus log rank statistic, exponential versus proportional and nonproportional hazards, and adjustment for treatment lag, staggered entry, noncompliance, drop‐in, loss‐to‐follow‐up and competing risks, and duration of treatment effect.
Textbooks in Clinical Trials
Authors: Massaro Joseph M.
Published: July 2005

In general, whether you are a student, clinical trials professional, or an instructor, choosing references or course textbooks can be overwhelming, even after deciding upon a specific learning objective. In this article, we hope to offer guidance on appropriate textbooks or references to those interested in (a) an introduction or overview of the design and components of clinical trials; and (b) statistical analysis of clinical trial data. What follows is not a critical review of texts, since the number of texts is too numerous for such a handling here. Rather, what follows is a brief description of various texts discussing clinical trials and the audience for which the text is meant. Also presented is this author's judgment on whether each text might be more useful as a textbook or a reference for a clinical trial statistics course or for a clinical trial introduction/overview/design course. Note that, again, this article focuses on various texts giving an introduction or overview of clinical trials, and on those texts focusing on analysis. The numerous other texts focusing on issues such as regulatory aspects or data management, or focusing on clinical trials in certain indications are not included here.
Women's Health Initiative: Statistical Aspects and Selected Early Results
Authors: Prentice Ross L. , Anderson Garnet L.
Published: July 2005

An overview is provided of the women's health initiative clinical trial and observational study of 161 808 postmenopausal women in the age range 50 to 79. The clinical trial includes 68 132 women in a partial factorial design having four intervention comparisons: a low‐fat eating pattern among 48 835 women; a hormone therapy trial of 0.625 mg per day of conjugated equine estrogen among 10 739 women who were posthysterectomy at baseline; a hormone therapy trial of this same estrogen plus 2.5 mg per day of medroxyprogesterone acetate among 16 608 women with uterus at baseline; and a calcium (1000 mg/day) plus vitamin D (400 international units/day) trial among 36 282 women. Each CT component had its specified primary and secondary hypotheses, along with an overriding emphasis on overall health benefits versus risks. A so‐called global index was defined for each CT component as the earliest occurrence of a set of clinical outcomes plausibly affected by the intervention, or death from other causes. Specialized data management communication, quality assurance, and data monitoring procedures were established to support this large and complex research program. Some detail is provided on the results of the combined hormone trial component, which was stopped early in 2002 when the external Data and Safety Monitoring Board judged that overall health risks exceeded benefits over an average follow‐up period in excess of five years.
Data Archives
Authors: Davey Smith George
Published: July 2005

Data archives that allow secondary analysis are valuable in allowing replication of analyses, individual data meta‐analyses, and possibilities to detect fraud.
Gestational Age
Authors: Macfarlane Alison
Published: July 2005

The World Health Organization defines the duration of a pregnancy as the time since the first day of the last normal menstrual period. This can be problematic if women conceive soon after stopping oral contraception or have irregular periods; so ultrasound estimates are increasingly used in countries where it is in common use. The World Health Organization also subdivides gestational ages into preterm, term and postterm.
Nutritional Exposure Measures
Authors: Bingham S.
Published: July 2005

In all methods of measurement of nutritional exposure, some estimate of the weight of food consumed is required, and for the determination of nutrient or other food component intake, either an appropriate description for use with food tables is needed or a portion must be available for chemical analysis. Different methods are available to assess the weight of food, and they vary in accuracy, complexity, and cost. All methods have different types of error structure, so that the magnitude of the error varies according to the method and may not be predictable. Validation studies of less accurate methods may be conducted to assess accuracy but regression dilution may be considerably underestimated due to error correlation between the reference and test methods. In case control studies, estimates of past dietary consumption are closely related to present consumption, and the discrepancy between actual and recalled past diet is greater the longer the period of recall attempted. All methods may be subject to systematic bias from under‐ or overreporting.
Occupational Mortality
Authors: Lynge Elsebeth
Published: July 2005

The study of occupational mortality involves the systematic tabulation of mortality by occupational or socio‐economic group. Three main methods are used to conduct these studies: cross‐sectional studies, death certificate studies, and follow‐up studies. Cross‐sectional studies were undertaken in England and Wales from 1851 to 1979–1983, and these studies have provided key data on social inequalities in health. Death certificate studies have been used for identification of occupational groups with high excess risks from specific diseases. Follow‐up studies require linkage of individual records, typically from censuses and death certificates. They have been undertaken in, for example, all the Nordic countries. The follow‐up studies have shown a high mortality for all marginal groups of the labor market, a social gradient in overall mortality, low mortality among farmers, and a widening gap between the mortality rates of blue and white collar workers.
Social Classifications
Authors: Kunst Anton E.
Published: July 2005

This article provides recommendations on the measurement of the three key indicators of socioeconomic status: educational level, income level, and occupational class.
Age–Period–Cohort Analysis
Authors: Holford Theodore R.
Published: July 2005

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
Authors: Gail Mitchell H.
Published: July 2005

Bias is the expected deviation of an estimate from the true quantity to be estimated.
Bias from Exposure Effects on Controls
Authors: Gail Mitchell H.
Published: July 2005

In hospital‐based case–control studies, exposures in cases are compared with exposures in controls in the hospital, who have other diseases. Exposure odds ratios (and disease odds ratios) will be biased if the exposures under study are associated with the control diseases.
Bias from Loss to Follow‐up
Authors: Gail Mitchell H.
Published: July 2005

Loss to follow‐up bias results when subjects lost from a cohort have different health response distributions from subjects who remain in follow‐up.
Bias from Nonresponse
Authors: Gail Mitchell H.
Published: July 2005

Nonresponse bias results when some members of the intended study population fail to provide required data (the nonresponders), and when those who respond are not representative of the entire study population.
Bias from Survival in Prevalent Case–Control Studies
Authors: Gail Mitchell H.
Published: July 2005

Odds ratios comparing prevalent cases with controls may be biased if the exposures of interest affect the survival of incident cases.
Bias in Cohort Studies
Authors: Hill Holly A. , Kleinbaum David G.
Published: July 2005

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

We 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.
Bias Toward the Null
Authors: Gail Mitchell H.
Published: July 2005

Bias toward the null usually refers to an effect of
nondifferential errors
in exposure measurements that reduces the apparent effect of the exposure on the dependent variable, which might be a health outcome.
Bias, Nondifferential
Authors: Gail Mitchell H.
Published: July 2005

The nondifferential bias is bias that affects each exposure (or treatment) group in such a way that the exposure effect measure remains unbiased. A nondifferential error process may induce nondifferential bias with respect to the measure of exposure effect but not another.
Biased Sampling of Cohorts
Authors: Brookmeyer Ron
Published: July 2005

Selection biases can distort findings from cohort studies. Self‐selection into a study cohort can yield results that are not representative of the target population. Screening a population to detect persons with high blood pressure, for example, can lead to misleading estimates of the effects of antihypertensive medication, in the absence of a control group, because subsequent blood pressure measurements will tend to be lower solely by virtue of the selection process. Studying a selected group of workers who were healthy before hiring can obscure the effects of occupational exposures in comparison to a study of the general population. Incomplete follow‐up of cohort members can also lead to biased estimates of cumulative risk and relative risk. Some time‐related biases can affect cohort studies. Left truncation arises when one only studies subjects who have survived to a certain time; the analysis must take left truncation into account to avoid bias. Prevalent cohorts consist of subjects who have a prevalent disease at the time of enrollment. Because the age at disease onset is often unknown in prevalent cohorts, estimates of cumulative and relative risks are subject to various biases.
Case Series, Case Reports
Authors: Gail Mitchell H.
Published: July 2005

A case report refers to a description of a person with a particular disease, and a case series refers to a series of case reports, often representing all cases with the given disease in a defined time period in a particular practice, clinic, or geographic area.
Case–Cohort Study
Authors: Langholz Bryan
Published: July 2005

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

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

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

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

Clustering problems arise in a number of epidemiological applications and the temporal and spatial pattern of disease incidence has been a major stimulus for the development of this subject. Evaluating apparent ‘clusters’ of disease in small communities has led to the development of a number of tests for spatial clustering. The study of unexpected outbreaks of disease has led to tests of temporal clustering and also of space–time clustering where closeness both temporally and geographically is required.
Competing Risks
Authors: Tsiatis A. A.
Published: July 2005

“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

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.
Controls
Authors: Gail Mitchell H.
Published: July 2005

Controls are subjects against whom comparison is made in experimental or observational studies. Various types of controls are described.
Cornfield's Inequality
Authors: Gastwirth Joseph L. , Krieger Abba M. , Rosenbaum Paul R.
Published: July 2005

Cornfield developed an inequality on the observed relative risk,RO, between an exposed and unexposed group, which could be explained by an unmeasured confounder,U. To be precise,ROis no greater than the ratio of the prevalence ofUin the exposed to that in the unexposed population. It is also true thatRO ≤ RU, whereRUis the ratio of risk in those withUcompared to those withoutU. We discuss a sharper bound onRO, which can be computed when information onRUand on the prevalences ofUin the exposed and unexposed populations are available. We also consider related bounds for risk differences and sensitivity analyses for tests of hypotheses in the presence of an unmeasured confounder.
Cross‐Sectional Study
Authors: Satten Glen A. , Grummer‐Strawn Laurence
Published: July 2005

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.
Crude Risk
Authors: Gail Mitchell H.
Published: July 2005

Crude risk (or absolute risk) is defined and contrasted with “net risk”, which refers to the probability of developing a given disease if competing risks are eliminated. Crude risk may also refer to risk in a heterogeneous population.
Cumulative Incidence Ratio
Authors: Gail Mitchell H.
Published: July 2005

The cumulative incidence ratio is a relative risk.
Density Sampling
Authors: Gail Mitchell H.
Published: July 2005

Density sampling refers to sampling controls from among the set of disease‐free individuals matched with time at the time of case incidence. Time‐matched analysis of case–control data with density sampling yields estimates of the relative hazard.
Dose–Response
Authors: Gail Mitchell H.
Published: July 2005

Dose–response refers to a relationship between an amount of exposure or treatment and the degree or probability of an outcome in an individual or population.
Hazard Rate
Authors: Gail Mitchell H.
Published: July 2005

The hazard rate is defined and related to other quantities used in epidemiology and survival analysis.
Incidence Density
Authors: Gail Mitchell H.
Published: July 2005

An incidence density is an incidence rate.
Incidence Rate
Authors: Gail Mitchell H.
Published: July 2005

The incidence rate is the number of persons who develop a disease of interest over a defined interval of time or age divided by the corresponding person‐years at risk.
Incidence–Prevalence Relationships
Authors: Keiding Niels
Published: July 2005

The 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.
Incident Case
Authors: Gail Mitchell H.
Published: July 2005

An incident case is a subject who has just developed a disease of interest for the first time.
Measurement Error in Epidemiologic Studies
Authors: Carroll Raymond J.
Published: July 2005

This article concerns measurement error in epidemilogic studies, often called errors‐in‐variables. I discuss what measurement error is, its impact on parameter estimates and hypothesis tests, and how to perform statistical analyses that account for the measurement error.
Misclassification Error
Authors: Kuha Jouni , Skinner Chris , Palmgren Juni
Published: July 2005

Misclassification 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

Occupational 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.
Odds Ratio
Authors: Gail Mitchell H.
Published: July 2005

The odds ratio is the ratio of the odds of an event in one group to the odds of an event in another group.
Prevalence Rate or Ratio
Authors: Gail Mitchell H.
Published: July 2005

The prevalence rate or ratio refers to the number of people who have a disease or condition at a given point in time in a given population divided by the number of people in that population.
Prevalent Case
Authors: Gail Mitchell H.
Published: July 2005

A prevalent case is a subject with a given disease or condition, who is alive in a defined population at a given time.
Random Digit Dialing Sampling for Case–Control Studies
Authors: Waksberg Joseph
Published: July 2005

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.
Random Error
Authors: Gail Mitchell H.
Published: July 2005

Random error is defined and contrasted with systematic error.
Rate
Authors: Gail Mitchell H.
Published: July 2005

Rate refers to a limiting ratio of the changes in two quantities as these changes tend to zero.
Recall Bias
Authors: Gail Mitchell H.
Published: July 2005

Recall bias occurs in case–control studies and refers to the bias that results when cases with the disease of interest tend to over‐ or underestimate their previous exposures, compared with controls without disease.
Relative Risk
Authors: Gail Mitchell H.
Published: July 2005

The relative risk is the ratio of the
risk
of disease in an exposed
cohort
over a defined time interval to the risk of disease in an unexposed cohort over this same time interval.
Selection Bias
Authors: Gail Mitchell H.
Published: July 2005

Selection bias is a bias that arises when individuals included in a study are not representative of the target population for the study.
Sex Ratio at Birth
Authors: Armitage Peter
Published: July 2005

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.
Stratification
Authors: Gail Mitchell H.
Published: July 2005

Stratification refers in epidemiology, to a design that improves the efficiency of analytical procedures to control for confounding by causing controls to have the same distribution over strata defined by levels of potential confounders as cases in a case–control study or as the exposed cohort in a cohort study.
Systematic Error
Authors: Gail Mitchell H.
Published: July 2005

Systematic error is defined and contrasted with random error.
Validity and Generalizability in Epidemiologic Studies
Authors: Rothman Kenneth J. , Greenland Sander
Published: July 2005

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

This article provides a broad overview of sample size determination methods for epidemiologic studies. Concepts related to sample size and power calculations are introduced. Formulas for sample size calculations are given for cohort and case–control (unmatched or matched) studies with binomial and Poisson outcomes. Methods for sample size calculations in survival studies and longitudinal studies are also presented. Factors that may affect the sample size and study power are discussed. A brief review of the computational methods and software packages commonly used for sample size calculations is also provided.
Administrative Databases
Authors: Roos Leslie L. , Romano Patrick S. , Fergusson Patricia
Published: July 2005

Administrative databases are often used to manage a health care system or to investigate health research questions. The data may be derived from population registries, vital statistics or other records of life events, or from health claims and services data. Questions that may be addressed by administrative datasets include determinants of variation in health care utilization, costs and outcomes, according to factors associated with individual patients, patient characteristics, types of health care organization, or geographic factors. Strengths and weakness of this approach are reviewed, and several new directions are identified.
Agreement, Measurement of
Authors: Shoukri M. M.
Published: July 2005

This 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

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

This 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

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 Test Evaluation without a Gold Standard
Authors: De Bock G. H. , Van Houwelingen J. C.
Published: July 2005

In case of unknown sensitivity and specificity of a reference test, the sensitivity and specificity of a diagnostic test can be estimated by the EM algorithm.
Diagnostic Tests, Evaluation of
Authors: Begg Colin B.
Published: July 2005

This entry reviews issues in the evaluation of individual diagnostic tests, or the comparison of two alternative tests. Measures of test accuracy are described, and the receiver operating characteristic (ROC) curve method of analysis is mentioned. Various biases that may affect the evaluation of a diagnostic test are reviewed. Parametric and nonparametric methods to compare two tests are indicated, and issues concerning the design of studies to evaluate diagnostic tests are summarized.
Epidemiology as Legal Evidence
Authors: Gastwirth Joseph L.
Published: July 2005

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.
False Positive Rate
Authors: Walter Stephen D.
Published: July 2005

The false positive rate of a diagnostic or screening test is defined as the complement of specificity. Its appropriate denominator is specified.
Kappa
Authors: Cook Richard J.
Published: July 2005

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

In 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

This entry describes the estimation of reference intervals for clinical variables. The appropriateness of various healthy populations to establish such intervals is discussed. The design and sample size of reference interval studies are reviewed. Estimation of the intervals may be done using nonparametric or parametric methods, and may be carried out in subgroups or take covariates into account. Goodness of fit to the data and identification of outliers are touched upon. Estimation of reference intervals is usually iterative, and specialized software to carry out the calculations is identified.
Normal Values of Biological Characteristics
Authors: Harris Eugene K.
Published: July 2005

This entry focuses on clinical biochemistry and hematology measurements, this being the area where most progress has been made in the estimation of normal values. Other examples are noted in passing. Parametric and nonparametric methods to estimate normal ranges are described, together with the identification and treatment of outlying observations. Calculation of normal ranges for population subgroups is discussed. There is a brief review of the establishment of time‐dependent normal values, and estimation of multivariate normal indexes or regions for correlated clinical variables.
Pharmacoepidemiology, Overview
Authors: Suissa S.
Published: July 2005

Pharmacoepidemiology is the study of patterns of medication use in the populations and their effects on diseases. Particular interest is focused on unexpected risks, especially events such as rare side effects. These can typically not be adequately assessed in the context of randomized clinical trials. This entry describes the use of Pharmacoepidemiology through the case‐crossover design, dealing with confounding in observational studies, the specification of appropriate risk functions over time, the probabilistic assessment of causality, and study methods based on the use of prescription data.
Placebos
Authors: Holbrook Anne , Goldsmith Charles H. , Leung Mova
Published: July 2005

Placebos have a long and controversial history in clinical research and in practice. In this chapter, we review the meaning, the pharmacology, effectiveness, safety, influences on, and ethical issues involving placebos. Various international guidelines for the use of placebos in research are available and evolving. We then suggest novel factorial designs to allow a full assessment of efficacy and safety of a new therapy while minimizing patient exposure to placebo alone.
Postmarketing Surveillance of New Drugs and Assessment of Risk
Authors: Anello C. , O'Neill R.
Published: July 2005

The goal of this article is to review the role, rationale, objectives, and design of quantitative methods of surveillance strategies developed for drug safety assessment over the past 30 years. Emphasis is placed on the quantitative methods that have been developed in response to regulatory requirements to monitor adverse drug event data for the purpose of assessing potential risk.
Predictive Modeling of Prognosis
Authors: Marshall G.
Published: July 2005

Prognosis is defined in terms of the relative probability of developing one of the several alternative outcomes in the natural history of a disease. This entry discusses predictive models for prognosis using a general Markov model in which the exact transition times are not observed. Data from a longitudinal study of young patients with diabetes are used to determine factors affecting the natural course of diabetic retinopathy. Other examples mentioned include hepatocellular carcinoma and HIV infection.
Risk Assessment in Clinical Decision Making
Authors: Shannon H. S. , Jadad A.
Published: July 2005

This article discusses the technical definition of risk and focuses on risks to patients in a clinical setting, particularly of events involving physical harm due to therapeutic intervention. It is shown that risk estimation involves both calculation of the risks and benefits involved, but also includes elements of risk communication, perception, and acceptance.
Scan Statistics for Disease Surveillance
Authors: Wallenstein S.
Published: July 2005

Scan statistics provide the researcher with a measure of unusualness (statistical significance) associated with a clustering of events in time, as would occur when observing an unexpectedly large number of events in a short time period. The statistic can also be used to detect seasonal clustering, and as a method to detect geographical, time, or space–time clustering. The scan statistic can also be generalized and then applied to the analysis of DNA sequence data focusing on detecting a certain pattern.
Screening, Overview
Authors: Van Oortmarssen Gerrit J.
Published: July 2005

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
Authors: Goldsmith Charles H.
Published: July 2005

Sensitivity 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.
Specificity
Authors: Walter Stephen D.
Published: July 2005

The false positive rate of a diagnostic or screening test is defined, and its clinical implications are discussed. An alternative use of the term “specificity” in factor analysis is also mentioned.
Time‐Varying Treatment Effect
Authors: Lavori Philip W. , Dawson Ree
Published: July 2005

This entry is concerned with assessing the effect of treatments that vary in time on the outcomes for individual patients. For example, levels of compliance with therapy may vary over time among participants in a clinical trial. Assessment of treatment effects in this situation is complicated by the fact that the relevant comparison may not be between randomized groups of patients. The so‐called “ignorability of treatment” condition is discussed, relating to the requirement that treatment actually received should be independent of potential patient responses to treatment. Other issues relating to the observational comparisons in medicine and adjustment for confounders are reviewed, together with a brief mention of recent developments in structural equation modeling of treatment effects in this context.
Vaccine Studies
Authors: Halloran M. Elizabeth
Published: July 2005

This entry begins with a brief overview of the biological background for vaccine action, and then describes the various phases of vaccine development, including early phases of identification of candidate vaccines, establishment of safety and immunogenicity in animals, safety and immunogenicity in humans, field efficacy trials, and post licensure studies. Measures of vaccine efficacy are described, and certain potential study biases are identified.
Cost–Benefit Analysis, Willingness to Pay
Authors: O'Brien Bernie
Published: July 2005

Cost–Benefit Analysis (CBA) is a technique of economic evaluation, where the benefits of programs are valued in monetary terms to permit direct comparison with program costs. There are a number of measurement and statistical challenges that arise while attempting to assign money values to health program benefits such as reduction in the risks of morbidity and mortality. The basic measurement goal is to quantify the trade‐offs that consumers are willing to make between reduced health risk and money. Specifically, to identify the maximum willingness to pay (WTP) for health benefits. In this chapter, we review several approaches to both market‐based “revealed preference” and survey‐based “stated preference” WTP estimation. A prevalent method developed in environmental economics is to ask respondents to accept/reject questions for increasingly higher monetary amounts (“bids”) and then to use survival‐type methods to estimate a bid curve, with covariates, that can be used to solve mean or median WTP.