bluuebunny/crossref_metadata_2025_split huggingface dataset:
Latent Period
Authors: Tan Wai‐yuan , Chen Chao W. 
Published: July 2005

AbstractDifferent definitions of latency used in the biological and statistical literature have been reviewed and illustrated by examples. In particular, a distinction has been made between the biological latent period and the epidemiological latent period. This article also discusses the latency of infectious diseases, such as the incubation period of HIV and the time to tumors in cancers. Some stochastic models of the time period to cancer tumors and its applications have been discussed in some detail.
Propensity Score
Authors: Rosenbaum Paul R. 
Published: July 2005

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: July 2005

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.
Target Population
Authors: Greenland Sander 
Published: July 2005

Abstract

The concept of a
target population
is an informal one, sometimes defined as “the population about which information is wanted” or the “totality of elements which are under discussion and about which information is desired”. Often, the word “population” refers to this concept; see, for example, Kendall & Stuart or Freedman et al. The word “target” emphasizes, however, that this population is not necessarily the same as the one that we end up sampling. The latter population is sometimes called the
sampled population
or (in epidemiology) the
source population
.
Confidentiality in Epidemiology
Authors: Leufkens Hubert G. 
Published: July 2005

AbstractThis entry describes the need for confidentiality in conducting clinical and epidemiologic research, particularly with respect to informational privacy of individuals. Ethical principles (including beneficence, nonmalfeasance, autonomy, and justice) are reviewed, and mention is made of the legal framework and guidelines to prevent the violation of confidentiality.
Drug Utilization Patterns
Authors: Abrahamowicz Michal , Tamblyn Robyn 
Published: July 2005

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.
Diagnostic Tests, Likelihood Ratio
Authors: Roberts Robin S. 
Published: July 2005

AbstractThis article concerns the use of diagnostic test information in medical diagnosis. Traditionally, the association between a diagnostic test and the true state of disease has been quantified in terms of sensitivity and specificity. The application of Bayes Theorem allows the formal incorporation of the diagnostic test information as a post‐test probability of disease. The positive and negative likelihood ratios (LRs) are alternative expressions of the informational properties of the diagnostic test, which are computed from sensitivity and specificity. LRs offer several advantages including (a) simplifying the calculation of post‐test probability (via odds), (b) providing a more direct indication of the diagnostic value of the test, and (c) avoiding the need to dichotomize a quantitative test. Given enough data, one could estimate a continuous LR curve and thus compute post‐test probability for any test value.
Meta‐Analysis of Diagnostic Tests
Authors: Macaskill Petra , Glasziou Paul , Irwig Les 
Published: July 2005

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.
Number Needed to Treat (NNT)
Authors: Bender Ralf 
Published: July 2005

AbstractThe number needed to treat (NNT) has gained much attention in the past years as a useful way of reporting the results of randomized clinical trials. Defined as the reciprocal of the absolute risk reduction (ARR), NNT is the average number of patients needed to be treated to prevent an adverse outcome in one additional patient. This measure has been applied to a number of other research areas involving the development of more sophisticated techniques to calculate and interpret NNTs in medical research. The most important application areas are screening studies, public health research, epidemiology (case–control and cohort studies), crossover studies, studies measuring continuous and survival time data, risk–benefit analyses, and systematic reviews. An overview of the recent developments regarding NNT is given as well as a discussion of the advantages, drawbacks, and usefulness of the NNT statistic as a reporting tool.
Prevalence of Disease, Estimation from Screening Data
Authors: Pickles Andrew , Dunn Graham 
Published: July 2005

AbstractSimple estimation of prevalence from sample survey data may be biased because of imperfections in the measurement instrument. An alternative approach is to use two‐phase sampling in which a subsample of screened individuals is investigated more intensively with a gold standard measure to reveal their true disease status. A variant design is to use a calibration or validation sample of individuals who are assessed using both the screen and gold standard methods. The analysis of two‐phase samples is described, including the use of simple conditional probabilities, inverse probability weights, and a more general framework in which the EM algorithm is used. Bayesian approaches are also mentioned briefly. Statistical software for the analysis of multiphase data is mentioned, together with an outline of certain design considerations.
Receiver Operating Characteristic (ROC) Curves
Authors: Hanley J. A. 
Published: July 2005

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.
Secondary Attack Rate
Authors: Halloran M. Elizabeth 
Published: July 2005

AbstractThe secondary attack rate is defined as the probability that infection occurs among susceptible persons within a reasonable incubation period following known contact with an infectious person or an infectious source. It is a key epidemiologic parameter in infectious diseases that are transmitted by contact. It can be estimated using a variety of epidemiologic study designs, models, and methods of estimation. Inference needs to take into account the correlation of susceptibles exposed to the same infectious source.
Screening, Sojourn Time
Authors: Duffy Stephen W. 
Published: July 2005

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.
Standard Gamble Technique
Authors: Gafni Amiram 
Published: July 2005

Abstract
In recent years, the concept of utility has been introduced to both clinical decision‐making and program evaluation. The standard gamble (SG) technique is a classic method of measuring an individual utility (i.e. preferences) under uncertainty. Its most prominent use is in generating the preference weights to be used in the quality adjusted life year (QALY) calculation. In this section, we briefly describe the technique and its underlying assumptions.
Time Trade‐Off Technique
Authors: Gafni Amiram 
Published: July 2005

AbstractDecisions about medical treatments and the setting of health programs involve both technical and value judgments. An important one is evaluating potential tradeoffs between quality of life and length of life or between different domains of quality of life resulting from different treatment options. The time trade‐off TTO) technique is one of the methods used for that purpose. Its most prominent use is in generating the preference weights to be used in the QALY (quality adjusted life years) calculation. In this section, we briefly describe the technique and its underlying assumptions.
Admixture in Human Populations
Authors: Majumder Partha P. 
Published: July 2005

Abstract
When parental populations give rise to a hybrid population, it is possible to estimate the proportions of admixture from each parental population on the basis of known marker allele frequencies if there has been no genetic drift. Methods include generalized least squares and maximum likelihood. It is also possible to use a measure of genetic similarity. All these methods assume that the admixture is a static, one‐time phenomenon, whereas in reality there is usually a continued, long‐term exchange of genes among populations.
Multilocus (Gene × Gene Interaction)
Authors: Cordell Heather J. 
Published: July 2005

AbstractAn important topic in molecular and quantitative genetics is epistasis, or the interaction between genes. There has been much confusion in the literature owing to differing definitions and interpretations of gene–gene interaction. This article reviews some of the most popular definitions used in the literature and notes that the correspondence between statistical and biological interaction effects may be more limited than is sometimes assumed.
Paternity Testing
Authors: Baur Max P. 
Published: July 2005

Abstract
The use of genetic marker systems for the analysis of questionable paternity situations has been introduced in 1920 to prove in a deterministic way, situations of nonpaternity. Statistical evaluation of positive results (nonexclusion) was first performed by Essen–Möller in 1938. The general idea, which holds for any question of possible relationship, is the formulation of two alternative hypotheses regarding the postulated relationships of the persons involved. Subsequently, the likelihoods of the marker‐typing results conditional on these hypotheses are calculated using population frequencies estimated from large population samples and Mendelian proportions for transmission from parents to children. The complete genetic information is contained in the likelihood ratio of these two conditional likelihoods and serves as the basis for the decision between the two alternative hypotheses. Introduction of prior probabilities for the two hypotheses allows the calculation of Bayesian posterior probabilities using these conditional likelihoods.
Penetrance
Authors: Wijsman Ellen M. 
Published: July 2005

Abstract
Penetrance is a probability model that relates, for an individual, the observed phenotype to the underlying genotype. Penetrance functions may be continuous or discrete, may include measured covariates, and may involve more than one locus. All variables not explicitly in the model are assumed to be randomly distributed among individuals, leading to variance in estimates of penetrance, in part because of variability in unmeasured covariates and genetic background among populations.
Bioinformatics in Functional Genomics
Authors: Kohane Isaac S. , Butte Atul 
Published: July 2005

AbstractDatabases of DNA sequences, physical maps, genetic maps, gene polymorphisms, protein structures, and gene expression have produced a need for systematic quantitative analysis that goes by the name of bioinformatics. To manage these large datasets in the domain of functional genomics requires algorithmic implementation on computers. Functional genomics will continue to be hypothesis‐driven and hypothesis‐generating biological research. Because the datasets are of high dimensionality, involving relatively small numbers of cases, a large number of solutions can explain the data; the computational techniques to unravel these involve both supervised and unsupervised learning.
Animal Screening Systems
Authors: Haseman Joseph K. 
Published: July 2005

AbstractAnimal screening systems are experimental protocols designed primarily to screen substances in laboratory animals for possible adverse effects in humans. These experiments include long‐term rodent toxicity/carcinogenicity bioassays and shorter duration studies that focus on reproductive and developmental toxicology endpoints. These experiments are the first step of a broader risk assessment process to determine the magnitude of human risk associated with exposure to a test substance. Statistical issues that arise in these studies include (i) selection of the number, magnitude, and spacing of doses; (ii) use of statistical methodology that adjusts for potentially confounding factors such as survival, body weight, and litter effects; (iii) multiple comparison adjustments for the large number of variables evaluated; (iv) use of historical control data; and (v) development of appropriate statistical methodology to permit extrapolation of experimental results from high to low doses and from species to species.
Biological Assay, Overview
Authors: Sen Pranab K. 
Published: July 2005

AbstractBiological essay (or “bioassay”), in its classical form, is concerned with the measurement of the concentration of an active ingredient in a test preparation, by estimation of its potency relative to that of a standard preparation. Assays, usually by animal experimentation, form an essential tool of biological standardization. Standard designs and methods of analysis are described here, including stochastic approximation, radioimmunoassays, semiparametric and nonparametric methods, and bioequivalence studies. In a broader sense, Bioassay refers to experiments, with biological units, to detect possible dose–response (or toxicity‐adverse effect) relationships. Underlying toxicodynamics (and/or pharmacodynamics) and toxicokinetics (and/or pharmacokinetics), possibly in conjunction with toxicogenomics (and/or pharmacogenomics) may render such studies statistically quite different from conventional biometric studies. Some relevant approaches are considered here.
Median Effective Dose
Authors: Liu Jen‐pei , Chow Shein‐chung 
Published: July 2005

AbstractThe median effective dose (MED) is the median of the tolerance distribution of the dose levels for a stimulus that generates, on average, predefined responses in 50% of experimental units. The median effective dose is usually denoted as ED50. An overview of the design and analysis for inference about ED50is provided. First, two commonly employed tolerance distributions—normal and logistic distributions—are introduced to characterize the relationship between the dose and response. Under a location/scale model for the tolerance distribution, estimation procedures for ED50are reviewed. Interval estimation methods include delta method, the Fieller's theorem, likelihood approach, and Bayesian procedures. The difficulties associated with interval estimation by the Fieller's theorem and likelihood method are addressed. Inference procedures for ED50are also summarized for the situations where (a) the underlying population consists of a mixture of homogeneous subpopulation, (b) the responses of individual experimental units are dependent, and (c) responses are observed at some prespecified time points. Nonparametric methods such as Spearman–Kärber estimators as well as other robust procedures are reviewed. An account of different nonsequential designs for MED under various optimality conditions is given. Finally, a brief summary of sequential procedures and designs is provided.
Stochastic Approximation
Authors: Gross Alan J. , McLean David C. 
Published: July 2005

AbstractStochastic approximation is a method for approximating the root of the equationM(θ) = m0, when the functionM(.) is not directly observable but can be cast as the expectation of a suitably defined regression function. The method was introduced by Robbins and Munro in 1951 and has since been extensively refined and extended. It has found application in many areas of biostatistics and in the application of neural networks.
Compartment Models
Authors: Matis James H. , Wehrly Thomas E. , Kiffe Thomas R. 
Published: July 2005

Abstract
Compartment modeling is used to describe system kinetics in various branches of biomedical science, and is especially important in pharmacokinetics. The standard model defines constant flow rates into and out of each of several compartments, with linear dependence on compartment contents. More complex models permit time‐varying flow rates, lags in flow, and birth and nonlinear features. Stochastic models provide further insight.
Point Processes
Authors: Milne Robin K. 
Published: July 2005

AbstractThis article is a review of the theory of point processes that aims to introduce the reader to the wider literature. It provides a relatively nontechnical summary of foundational ideas, an introduction to the rich variety of point process, and stochastic process models that can be built from more basic point processes such as Poisson and renewal processes, and discusses some aspects of statistical inference for point process data. An annotated bibliography offers guidance to the literature.
Semi‐
M
arkov Processes
Authors: Mode Charles J. 
Published: July 2005

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.
Y
ule Process
Authors: Chiang Chin Long 
Published: July 2005

Abstract

The Yule process is a birth process with a constant birth rate. The number of births in time interval (0,
t
),
Y
(
t
), has a negative binomial distribution, which is the same as the distribution of the number of new species of a genus produced during (0,
t
) in Yule's study of evolution.

The numbers of species produced in a sequence of time intervals,
Y
(1), …,
Y
(
r
), have a chain of negative binomial distributions.
Communicable Diseases
Authors: Farrington C. P. 
Published: July 2005

AbstractMany of the statistical methods used in communicable diseases are similar to those used in other areas of epidemiology. Clinical trials, risk‐based epidemiological studies, and survival analysis for example account for much of the infectious disease statistician's toolkit. In addition, however, some more specialized techniques are required. This article aims to give a flavor of the range of statistical methods used in the field of infectious disease epidemiology. Only the simpler versions of the techniques are covered, with references to more complex methods. For common childhood infections, survival models are reinterpreted in the context of epidemic transmission models to estimate hazards of infection, and to derive epidemiological parameters such as the basic reproduction number. Basic back‐calculation methods are described for estimating the incidence of infections with long incubation periods such as AIDS. Simple stochastic models such as branching processes and chain binomial models for use with outbreak data are outlined. Methods are described for estimating latency and infectious periods. Cluster detection methods are also reviewed in the context of identifying infectiousness and detecting outbreaks. Statistical methods for evaluating vaccine efficacy and safety are discussed. Finally, some statistical topics relating to diagnostic testing for infectious diseases using laboratory techniques are touched upon.