bluuebunny/crossref_metadata_2025_split huggingface dataset:
M
oran,
P
atrick
A
lfred
P
ierce
Authors: Wilson Susan R. 
Published: July 2005

Abstract
Pat Moran was an Australian statistical scientist with an enormous breadth and depth to his research interests. During his prolific career, Moran made substantial contributions to population genetics, medical statistics (particularly psychiatry), geometric probability, mathematical statistics, time series, and applied probability. He received numerous scientific honors, and now is commemorated by two Australian awards for young research statisticians.
National Institutes of Health (
NIH
)
Authors: Ellenberg Jonas H. , Dixon Dennis O. 
Published: July 2005

Abstract
The United States National Institutes of Health (NIH) has contributed in several ways to the development of biostatistics as a discipline. From its establishment soon after World War II, NIH leaders recognized the contributions that biostatisticians would make in fulfilling its research mission. NIH biostatisticians, some in positions of leadership beyond biostatistics, have influenced theoretical developments and applications for more than 50 years. NIH funds have supported development of methods at many universities, as well as graduated training of many of those currently working at universities, companies, and government agencies.
Office for National Statistics (
ONS
) (Formerly
OPCS
)
Authors: Fox John , Dunnell Karen , Coleman Michael 
Published: July 2005

Abstract
The Office for National Statistics (ONS) lies at the center of the United Kingdom Government Statistical Service. In partnership with other government departments, ONS aims to provide “the main statistical advisory service for policy formulation, resource allocation, planning, and research on the number, characteristics and health of the population. It collects and analyses data from a wide variety of sources, and publishes and interprets the statistics. Major customers are government, business, researchers and the general public.”
P
earl,
R
aymond
Authors: Dixon Dennis O. 
Published: July 2005

Abstract
Raymond Pearl (1879–1940) worked in Michigan as a biologist until 1905. After a year's study in London with Karl Pearson, he returned to the US and increasingly worked in areas of human biology. In 1918, he became the first Professor of Biometry and Vital Statistics in the Johns Hopkins School of Hygiene and Public Health, Baltimore, and started a prolific period of research in a wide range of human biology, including public health, eugenics and heredity. A major line of research was human population growth.
Pearson,Karl
Authors: Magnello M. E. 
Published: July 2005

AbstractKarl Pearson (1857–1936) was the founder of biometrics andBiometrikaand a principal architect of the mathematical theory of statistics. He was a polymath, with a wide interest in intellectual and social pursuits, and first taught applied mathematics at University College London. He started to teach statistics and to develop new methods during the 1890s. He developed measures of correlation and discovered the chi‐ square distribution and used it for tests of goodness of fit and association in contingency tables. He established and ran four laboratories and established the first degree course in mathematical statistics in Britain. He worked closely with Galton and Weldon, and eugenics and heredity remained central interests throughout his life.
P
etty,
W
illiam
Authors: Armitage Peter 
Published: July 2005

Abstract
A man of many accomplishments, including professorships in Anatomy and Music in Oxford, Petty was influential in the founding of the Royal Society and advocated the setting up of a national statistical office to conduct censuses, organize tax collection, and improve public health. He organized the complete mapping of Ireland. He was a friend of John Graunt, wrote on vital statistics and demography, and invented the term “political arithmetic”.
Salk Vaccine
Authors: Meier Paul , Smith Rebecca Pringle 
Published: July 2005

Abstract
The Salk vaccine was a killed‐virus vaccine against poliomyelitis, an uncommon but dangerous disease during the early twentieth century. In 1954, over one million children in the United States took part in a study of the effectiveness of the vaccine. Over 700 000 entered a randomized double‐blind study. Over a million were in a study in which second‐grade children were offered the vaccine and first‐ and third‐ grade children were observed as controls. In both forms of study, the incidence was reduced by about half in the groups that received the vaccine. The problems of design, execution, and interpretation are described in some detail.
Schneiderman, Marvin Arthur
Authors: Levin David L. 
Published: July 2005

AbstractM.A. Schneiderman (1918–1997) was a statistician and scientific administrator at the US National Cancer Institute from 1948 to 1980. His research covered topics in dose–response curve analysis, clinical trials in cancer, multistage biological screening programs, and sequential methods. After 1980, he was actively engaged in the monitoring and development of legislation for environmental and toxicologic issues.
S
mith,
C
edric
A
usten
B
ardell
Authors: Armitage Peter 
Published: July 2005

Abstract
C.A.B. Smith (1917–2002) was a leading exponent in the United Kingdom of the application of statistics to human genetics, working at the Galton Laboratory, University College London. His principal work was in the detection and measurement of human linkage, and he was an influential exponent of likelihood and Bayesian methods.
Smoking and Health
Authors: Holland W. W. 
Published: July 2005

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.
S
nedecor,
G
eorge
W
addel
Authors: Cox D. F. 
Published: July 2005

Abstract
G.W. Snedecor (1881–1974) worked at Iowa State University from 1913 to 1958, directing the new Statistical Laboratory from 1933 to 1947. He introduced a course in statistics in 1915 and, with H.A. Wallace, wrote a book on correlation and machine calculation in 1925. He promoted the methods of R.A. Fisher and in 1937 wrote his influential book on statistical methods, the fifth and subsequent editions of which were coauthored with W.G. Cochran.
S
piegelman,
M
ortimer
Authors: Pollack Earl 
Published: July 2005

Abstract

Mortimer Spiegelman was an important contributor to biostatistics, particularly in the areas of
demography
and public health. His major contribution to the field of public health and epidemiology came toward the end of his career when he conceived of, coordinated, edited, and carried to a successful conclusion the publication of a series of monographs sponsored by the
American Public Health Association
(APHA) and published by the Harvard University Press. Each monograph pertained to a specific set of diseases in which the 1960
Census
was used in a standard way as the denominator for
rates
of disease. In his role as editor of this series, he used his considerable powers of persuasion with the authors of the monographs to ensure comparability among them and to make certain that the work on each was completed. Sixteen monographs resulted from this effort, covering a wide range of topics.
Statistical Consulting
Authors: Stinnett Sandra S. , Derr Janice A. , Gehan Edmund A. 
Published: July 2005

AbstractThe provision of advice and/or services in the application of statistical methods may be quite brief or may constitute full collaboration of the statistician as an investigator. The role of the biostatistician is discussed in the separate contexts of a medical school, a pharmaceutical company, a dental school, a government agency, and an academic department. Attention is drawn to the characteristics of a successful consultant, the challenges of consulting, especially with physicians, and the role of a consultant as ambassador and teacher.
Teaching Medical Statistics to Statisticians
Authors: Newell David , Simpson Judy 
Published: July 2005

Abstract
Biostatistics teaching to statisticians is mainly through graduate and continuing education courses. An important component is exposure to consulting and collaborative research done by teachers. Three aspects of the subject that need emphasis are ethical issues, the consequences of human fallibility (e.g. in diagnostic errors), and problems of causation. Considerations arising in observational studies and randomized trials are discussed, as are more general issues about the course format.
Tukey,JohnWilder
Authors: Kafadar Karen 
Published: July 2005

AbstractJohn Tukey (1915–2000), is best known to statisticians for founding the field of exploratory data analysis, for introducing the jackknife as a tool for characterizing the uncertainty in a statistic, and for guiding and contributing to research in robust methods. His work in other fields included Tukey's lemma; fast fourier transform; and coining the word “bit” for computer science.
W
ald,
A
braham
Authors: Chernoff H. 
Published: July 2005

Abstract
Wald (1902–1950) was born in Rumania and emigrated to the United States in 1938. From 1938, he worked at Columbia University. In 1939, he published the first account of statistical decision theory, and in 1943, he developed the probability ratio test, the essential feature of sequential analysis. He often used Bayesian arguments to derive frequentist results. He worked on a wide range of topics in mathematical statistics and econometrics.
Y
ule,
G
eorge
U
dny
Authors: Edwards A. W. F. 
Published: July 2005

Abstract

G.U. Yule (1871–1951) worked from 1892 at University College London under Karl Pearson, and from 1912 to 1931 at Cambridge. He published
An Introduction to the Theory of Statistics
in 1911, the 11th and subsequent editions of which were coauthored by M.G. Kendall. He contributed to the theory of regression, correlation, genetics, epidemiology, and time series, and developed a coefficient of association for 2 × 2 tables. He described what is now usually known as Simpson's paradox for association in a 2 × 2 × 2 tables. He introduced the simple birth process in the study of evolution.
F
leiss,
J
oseph
L
.
Authors: Shrout Patrick E. , Begg Melissa D. 
Published: July 2005

Abstract
Joseph L. Fleiss was a biostatistician who made important contributions to the analysis of diagnostic reliability, clinical trials, and categorical data. He was born on November 13, 1937, in Brooklyn, New York, and he died on June 12, 2003, in Ridgewood, New Jersey. This encyclopedia entry describes his impact on research in psychiatry, dentistry, and cardiology and traces his career as a researcher, professor, and department chair at Columbia University in New York City. It highlights Fleiss's impact on clinical trials and as an author of more than 200 scientific papers and of accessible biostatistics texts on rates and proportions, and on clinical trials.
Clinical Trials
Authors: Goodman Steven N. 
Published: July 2005

Abstract

Clinical Trials: Journal of the Society for Clinical Trials
is a peer‐reviewed, bimonthly journal, initiated in 2004, as the official journal of the Society for Clinical Trials (SCT), succeeding
Controlled Clinical Trials
. As with its predecessor, it is an interdisciplinary journal whose primary focus is clinical trial‐related statistical methodology, but which also covers a broad range of methods and issues from other domains that relate to clinical trials. Its current structure, procedures, and focus are described.
Barnard,GeorgeAlfred
Authors: Lindley Dennis V. 
Published: July 2005

AbstractG.A. Barnard (1915–2002) was a prominent British statistician, holding professorships at Imperial College London and the University of Essex. He was a strong proponent of the use of likelihood as a central feature of statistical inference, leading to the likelihood principle, and stressed the importance of conjugate distributions in Bayesian theory. Another interest was industrial inspection and quality control.
Analytic Hierarchy Process
Authors: Saaty Thomas L. 
Published: July 2005

Abstract
The Analytic Hierarchy Process (AHP) is a theory of relative measurement of intangible criteria. With this approach to relative measurement, a scale of priorities is derived from pairwise comparison measurements only after the elements to be measured are known. The ability to do pairwise comparisons is our biological heritage and we need it to cope with a world where everything is relative and constantly changing and thus, there are no fixed standards to measure things on. In traditional measurement, one has a scale that one applies to measure any element that comes along that has the property the scale is for, and the elements are measured one by one, not by comparing them with each other. In the AHP, paired comparisons are made with judgments using numerical values taken from the AHP absolute fundamental scale of 1 to 9. A scale of relative values is derived from all these paired comparisons and it also belongs to an absolute scale that is invariant under the identity transformation like the system of real numbers. The AHP is useful for making multicriteria decisions involving benefits, opportunities, costs, and risks. The ideas are developed in stages and illustrated with examples of real‐life decisions. The subject is transparent and easy to understand why it is done the way it is along the lines discussed here. The AHP has a generalization to dependence and feedback; the Analytic Network Process (ANP) is not discussed here.
Health Care Utilization and Behavior, Models of
Authors: Aday Lu Ann , Andersen Ronald M. 
Published: July 2005

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: July 2005

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 Economics
Authors: Conrad Douglas A. , Sullivan Sean D. 
Published: July 2005

AbstractThis chapter examines the major research questions facing health economics today and describes the larger context for those questions in terms of allocative efficiency and technical efficiency. The theory underlying various decision‐making algorithms is also elucidated–cost–effectiveness, cost–benefit, and cost–utility analyses being the most prominent examples. The Pareto‐efficiency criterion is explicated, and the underlying assumptions of the different algorithms are presented. The chapter concludes with examples of the application of various decision rules and develops the concepts of risk, return, and the time value of resources devoted to health investments.
Health Services Research, Overview
Authors: Lohr Kathleen N. 
Published: July 2005

AbstractHealth services research studies how social factors, financing systems, organizational structures and processes, technology, and personal behaviors affect access to health care, the costs and quality of health care, and the health and well‐being of individuals and populations. HSR, a boundary‐crossing field, engages a very wide set of clinical and academic disciplines, investigates a broad array of questions relating to health care delivery and management, financing, clinical practice, personal health care decision‐making, and health policy, and employs virtually all quantitative and qualitative methods found in the social sciences, economics, outcomes measurement, management and administration, epidemiology and statistics, law, and policy analysis. It has one major professional organization, Academy Health, and is supported in the US in large measure by the federal Agency for Healthcare Research and Quality, certain other federal agencies, and philanthropic organizations; numerous journals exclusively or predominantly publish on HSR‐related topics.
Health Status Instruments, Measurement Properties of
Authors: SchÜnemann Holger , Juniper Elizabeth , Guyatt Gordon 
Published: July 2005

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.
Medicare Data
Authors: Ash Arlene S. , Virnig Beth 
Published: July 2005

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.
Operations Research
Authors: Steinwachs Donald 
Published: July 2005

Abstract
Operations Research (OR) uses quantitative methods to support decision‐making. OR methods have been used in health service delivery to improve efficiency and effectiveness. OR methods are designed to support a decision‐maker seeking to optimize the achievement of explicit goals within available resources and other constraints present. The range of OR decision tools is described and how they can be applied to different types of management and clinical decisions.
Outcomes Research
Authors: Lipscomb Joseph 
Published: July 2005

AbstractOutcomes research is a multidisciplinary field of study to understand and improve the end results of healthcare. These end results may include survival, health‐related quality of life, satisfaction with care, economic impact, and other outcomes important to a range of decision makers: patients, individuals at risk to disease, providers, public and private payers, regulators, and standards‐setting organizations. Outcomes research is substantially focused on assessing and enhancing the quality of health care. This requires continuing efforts to develop and apply valid, reliable, feasible, and appropriately responsive outcome measures; to evaluate the impact of interventions on these outcomes; and to translate findings into information useful to decision makers.
Quality of Care
Authors: Normand Sharon‐Lise T. 
Published: July 2005

AbstractQuality of care refers to the attributes or characteristics of the delivery and subsequent outcomes of mental and physical health care. Data are collected to assess quality in order to improve care, to provide accountability, and to quantify information for market choices. Analyses of health data, however, are challenging due to their nonexperimental nature and complex structure. These characteristics lead to potential problems with confounding when identifying factors that impact quality, with nonindependence of outcomes associated with individuals treated within health care units, as well as with clustering due to multiple mixed outcomes.
Quality of Life and Health Status
Authors: Patrick Donald L. 
Published: July 2005

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.
Operations Research, Simulation
Authors: Sharkey Phoebe D. 
Published: July 2005

Abstract
Simulation models have been indispensable tools for decision makers in many different public and private environments. The methodology of developing a simulation model involves the abstraction of a real‐life system into a symbolic model format. To discuss the multiple stages required in a Monte Carlo simulation study, including the (1) Problem Statement; (2) Information Gathering and Data Preparation; (3) Model Conceptualization; (4) Model Implementation; (5) Analysis and Interpretation; (6) Verification and Validation; and (7) Implementation and Documentation. Simulation models are created in order to provide valuable insight to decision makers by replicating real‐life decision scenarios and providing more complete information about the risks and uncertainties in those environments. Once a model has been verified and validated as accurately representing the real‐world environment, many “what if ” questions about the system can be posed. Illustrative examples are presented.
Small Area Variation Analysis
Authors: Diehr Paula 
Published: July 2005

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.