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).

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 Proceedings are a result of the twelfth conference on Statistical Methodology for Study of the AIDS Epidemic which was held in 1991 at the Mathematical Sciences Research Institute, Berkeley, California. For five days, 45 speakers and observers contributed their expertise in the relevant biology and statistics. The presentations were timely and the discussion was both enlightening and at times spirited. Members of the Program Committee for the Conference were Klaus Dietz (University of Tiibingen, Germany), Vernon T. Farewell (University of Waterloo, Ontario), and Nicholas P. Jewell (University of California, Berke ley) (Chair). The Conference was supported by a grant to SIMS from the National Institute of Drug Abuse. D. L. Thomsen, Jr."

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).

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 covers the logistic regression model in detail and contrasts it with the Cox model for time-to-incidence data. The author uses a few simple case studies to guide readers from elementary analyses to more complex regression modeling. Following these examples through several chapters makes it easy to compare the interpretations that emerge from varying approaches.

Written by one of the top biostatisticians in the field, Statistics for Epidemiology stands apart in its focus on interpretation and in the depth of understanding it provides. It lays the groundwork that all public health professionals, epidemiologists, and biostatisticians need to successfully design, conduct, and analyze epidemiological studies.