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Statistics for Epidemiology
Statistical ideas have been integral to the development of epidemiology and continue to provide the tools needed to interpret epidemiological studies. Although epidemiologists do not need a highly mathematical background in statistical theory to conduct and interpret such studies, they do need more than an encyclopedia of "recipes." Statistics for Epidemiology achieves just the right balance between the two approaches, building an intuitive understanding of the methods most important to practitioners and the skills to use them effectively. It develops the techniques for analyzing simple risk factors and disease data, with step-by-step extensions that include the use of binary regression. It ...
Causal Inference in Statistics
Many of the concepts and terminology surrounding modern causal inference can be quite intimidating to the novice. Judea Pearl presents a book ideal for beginners in statistics, providing a comprehensive introduction to the field of causality. Examples from classical statistics are presented throughout to demonstrate the need for causality in resolving decision-making dilemmas posed by data. Causal methods are also compared to traditional statistical methods, whilst questions are provided at the end of each section to aid student learning.
Analysis of Longitudinal Studies in Epidemiology
Aimed at a nontechnical audience, with intuitive explanations instead of mathematical derivations, Analysis of Longitudinal Studies in Epidemiology covers a wide range of topics that include Poisson regression, survival analysis, repeated measure, clustered data, longitudinal observations, and generalized linear models such as logistic regression. The book illustrates examples through extensive case studies, placing particular emphasis on the extension of simple cross-sectional methods and exploring how they can be adapted to longitudinal settings. It also explains the importance of time-dependent confounding and introduces methods including inverse probability weighted estimators.
Lifetime Data: Models in Reliability and Survival Analysis
Statistical models and methods for lifetime and other time-to-event data are widely used in many fields, including medicine, the environmental sciences, actuarial science, engineering, economics, management, and the social sciences. For example, closely related statistical methods have been applied to the study of the incubation period of diseases such as AIDS, the remission time of cancers, life tables, the time-to-failure of engineering systems, employment duration, and the length of marriages. This volume contains a selection of papers based on the 1994 International Research Conference on Lifetime Data Models in Reliability and Survival Analysis, held at Harvard University. The conference brought together a varied group of researchers and practitioners to advance and promote statistical science in the many fields that deal with lifetime and other time-to-event-data. The volume illustrates the depth and diversity of the field. A few of the authors have published their conference presentations in the new journal Lifetime Data Analysis (Kluwer Academic Publishers).
AIDS Epidemiology
In 1974, the Societal Institute of the Mathematical Sciences (SIMS) initiated a series of five-day Research Application Conferences (RAC's) at Alta, Utah, for the purpose of probing in depth societal fields in light of their receptivity to mathematical and statistical analysis. The first eleven conferences addressed ecosystems, epidemiology, energy, environmental health, time series and ecological processes, energy and health, energy conversion and fluid mechanics, environmental epidemiology: risk assessment, atomic bomb survival data: utilization and analysis, modem statistical methods in chronic disease epidemiology and scientific issues in quantitative cancer risk assess ment. These Proce...
Causal Inference in Statistics
CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of sit...
Shopping Malls and Public Space in Modern China
China’s rise as an economic superpower has been inescapable. Statistical hyperbole has been accompanied by a plethora of highly publicized architectural forms that brand the regeneration of its increasingly globalized urban centres. Despite the sizeable body of literature that has accompanied China’s modernization, the essence and trajectory of its contemporary cityscape remains difficult to grasp. This volume addresses a less explored aspect of China’s urban rejuvenation - the prominence of the shopping mall as a keystone of its public spaces. Here, the presence of the built form most representative of Western capitalism’s excess is one that makes explicit the tensions between China...
Methodological Issues in AIDS Behavioral Research
We are pleased to introduce a new series, Aids Prevention and Mental Health, with the publication of this volume on methodology issues in AIDS mental health research. The objective of the series is to publish high-quality and up-to-date volumes that HIV prevention and mental health researchers, clinicians, policymakers, and educators will find useful and that will thus contribute significantly to their work in these important areas.
Counting Civilian Casualties
Counting Civilian Casualties aims to promote open scientific dialogue by high lighting the strengths and weaknesses of the most commonly used casualty recording and estimation techniques in an understandable format.
