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Relative Risk and Odds Ratio Regression

Relative Risk and Odds Ratio Regression Consider a cohort of nearly 20,000 residents of Hiroshima and Nagasaki who have undergone biennial clinical examinations for the past 20 years under the auspices of the Radiation Effects Research Foundation.Suppose that one would like to use a series of blood pressure measurements on an individual to assess the risk of a subsequent cardiovascular disease event.This type of assessment is complicated by a number of features: the "risk factors" of interest are evolving over time, risk factor data may be missing, there may be many other "confound­ ing" factors, and endpoint events may be censored due to competing risks or dropout. During the past 15 years a vast biostatistical literature has developed on the regression analysis of "failure" time data.This literature is relevant to a range of problems in medicine and public health as well as in other areas.For example "failure time" may refer to time-to-relapse in a clinical trial, or to the age of disease diagnosis in an epidemiologic cohort study or in a disease prevention trial. Often interest will center on the dependence of failure rate on individual characteristics, exposures, or treatment assignments, which are collectively referred to here as "covariates." In the above example the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annual Review of Public Health Annual Reviews

Relative Risk and Odds Ratio Regression

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Publisher
Annual Reviews
Copyright
Copyright 1986 Annual Reviews. All rights reserved
Subject
Review Articles
ISSN
0163-7525
eISSN
1545-2093
DOI
10.1146/annurev.pu.07.050186.000343
pmid
3521648
Publisher site
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Abstract

Consider a cohort of nearly 20,000 residents of Hiroshima and Nagasaki who have undergone biennial clinical examinations for the past 20 years under the auspices of the Radiation Effects Research Foundation.Suppose that one would like to use a series of blood pressure measurements on an individual to assess the risk of a subsequent cardiovascular disease event.This type of assessment is complicated by a number of features: the "risk factors" of interest are evolving over time, risk factor data may be missing, there may be many other "confound­ ing" factors, and endpoint events may be censored due to competing risks or dropout. During the past 15 years a vast biostatistical literature has developed on the regression analysis of "failure" time data.This literature is relevant to a range of problems in medicine and public health as well as in other areas.For example "failure time" may refer to time-to-relapse in a clinical trial, or to the age of disease diagnosis in an epidemiologic cohort study or in a disease prevention trial. Often interest will center on the dependence of failure rate on individual characteristics, exposures, or treatment assignments, which are collectively referred to here as "covariates." In the above example the

Journal

Annual Review of Public Health – Annual Reviews

Published: May 1, 1986

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