Modern statistical methods in chronic disease epidemiology : proceedings of a conference
著者
書誌事項
Modern statistical methods in chronic disease epidemiology : proceedings of a conference
（A Wiley-Interscience publication）
Wiley, c1986
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注記
Conference held in 1985 in Alta, Utah
Includes bibliographies
内容説明・目次
内容説明
A tremendous amount of activity surrounding statistical methodology in chronic disease epidemiology has taken place since Mantel and Haenszel's pioneering paper in 1959. Areas of increased interest have centered around environmental and genetic risk assessment and risk extrapolation. The eleventh Research Application Conference held under the direction of SIMS brought together leading experts to discuss the theory and applications of statistical methods in chronic disease epidemiology. This book represents the proceedings of that conference. Focusing on relative risk regression, the book, as a result, includes chapters dealing with a range of issues, such as matching and covariate adjustment, choice of primary time variate and evolutionary covariates, design and analysis of prevention trials, problems involving auxiliary and incomplete covariate data, confidence region and model criticism, absolute and relative risk methods, methods in genetic epidemiology, models for carcinogenesis and cancer progression, and multivariate failure time methods.
目次
- ASPECTS OF THE VALIDITY AND OF THE DESIGN OF EPIDEMIOLOGIC STUDIES: Adjusting for Covariates that have the Same Distribution in Exposed and Unexposed Cohorts
- Regression Methods for Data with Incomplete Covariates
- Partial and Marginal Matching in Case-Control Studies
- Design Options for Sampling within a Cohort
- TOPICS IN RELATIVE RISK REGRESSION ANALYSIS OF EPIDEMIOLOGIC DATA: Stanford Heart Transplantation Data Revisited: A Real-Time Approach
- Time-Dependent Covariates and Markov Processes
- Confidence Regions for Case-Control and Survival Studies with General Relative Risk Functions
- Relative Risk Regression Diagnostics
- An Example of Dependencies among Variables in a Conditional Logistic Regression
- ON THE ANALYSIS OF CORRELATED DISEASE OCCURRENCE DATA: A Model for Bivariate Survival Data.
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