Statistical Models in Epidemiology, the Environment, and Clinical Trials
By: edited by M. Elizabeth Halloran, Donald Berry
Contributors: Berry, Donald
Series: The IMA Volumes in Mathematics and its Applications -- 116; IMA volumes in mathematics and its applications -- 116.
Publisher: Springer New York
Published: 2000
Place of publication: New York, NY
Language: eng
Format: [electronic resource] /
Number of pages: 283
ISBN-13: 9781461270782; 9781461212843
Subjects: Mathematics; Global analysis (Mathematics); Statistics; Physiology
Description: This volume contains refereed papers by participants in the two weeks on Clinical Trials and one week on Epidemiology and the Environment held as part of the six weeks workshop on Statistics in the Health Sciences Applications at the Institute for Mathematics and its Applications (IMA) in the summer of 1997. Donald Berry was in charge of the weeks on clinical trials, and Elizabeth Halloran organized the week on epidemiology and the environment. The collection includes a major contribution from Jamie Robins, Andrea Rotnitzky, and Daniel Scharfstein on sensitivity analysis for selection bias and unmeasured confounding in missing data and causal and inference models. In another paper, Jamie Robins presents a new class of causal models called marginal structural models. Alan Hubbard, Mark van der Laan, and Jamie Robins present a methodology for consistent and efficient estimation of treatment-specific survival functions in observational settings. Brian Leroux, Xingye Lei, and Norman Breslow present a new mixed model for spatial dependence for estimating disease rates in small areas. Andrew Lawson and Allan Clark demonstrate Markov Chain Monte Carlo methods for clustering in spatial epidemiology. Colin Chen, David Chock, and Sandra Winkler present a simulation study examining confounding in estimation of the epidemiologic effect of air pollution. Dalene Stangl discusses issues in the use of reference priors and Bayes factors in analyzing clinical trials. Stephen George reviews the role of surrogate endpoints in cancer clinical trials.
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Table of contents: title: Foreword; title: Preface; title: Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models; title: Marginal structural models versus structural nested models as tools for causal inference; title: Nonparametric locally efficient estimation of the treatment specific survival distribution with right censored data and covariates in observational studies; title: Estimation of disease rates in small areas: A new mixed model for spatial dependence; title: Markov chain Monte Carlo methods for clustering in case event and count data in spatial epidemiology; title: A simulation study of the epidemiological impact of air pollution: Diagnostics of the confounding effects for generalized linear models; title: The use of reference priors and Bayes factors in the analysis of clinical trials; title: Surrogate endpoints in cancer clinical trials; title: List of Participants.