Relative risk models for assessing the joint effects of multiple factors
作者:
Jay H. Lubin,
William Gaffey,
期刊:
American Journal of Industrial Medicine
(WILEY Available online 1988)
卷期:
Volume 13,
issue 1
页码: 149-167
ISSN:0271-3586
年代: 1988
DOI:10.1002/ajim.4700130110
出版商: Wiley Subscription Services, Inc., A Wiley Company
关键词: biostatistics;cohort studies;epidemiology
数据来源: WILEY
摘要:
AbstractA goal of analyses of occupational cohort data is the specification of how covariate information relates to age‐specific disease risks. In describing this relationship, certain assumptions or models must be defined. For example, the usual standardized mortality ratio assumes a constant multiplicative increase in the age and calendar period disease rates of an exposed cohort over rates in a unexposed referent group. For analyzing several exposures, some of which may be continuous, such as cumulative dose, dose rate, duration of employment, and smoking patterns, or for analyzing complex associations between disease rate and covariates, flexible regression procedures are required. Using a crossclassification of the data and a Poisson probability model, relative risk regression methods are outlined. Breslow and Storer [1985], Guerrero and Johnson [1982], and Thomas [1981] propose general models for the relative risk as alternatives to, but which include, the usual exponential form. We review these models, discuss some limitations (in particular when there is more than one covariate) and present alternatives. Methods and models are illustrated by examining the joint effects of radon exposure and tobacco use on lung cancer mortality among a group of uranium miner
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