A simple approach for fitting linear relative rate models in SAS.
D. Richardson. American journal of epidemiology, 168 (11):
1333-8(December 2008)4872<m:linebreak></m:linebreak>GR: K01-OH008635/OH/NIOSH CDC HHS/United States; JID: 7910653; 2008/10/25 aheadofprint; ppublish;.
DOI: 10.1093/aje/kwn278
Abstract
The linear relative rate model has been employed in epidemiologic analyses of a variety of environmental and occupational exposures. In contrast to an exponential rate model, the linear relative rate model implies that the excess relative rate of disease changes in an additive fashion with exposure. The linear relative rate model may be fitted using EPICURE (HiroSoft International Corporation, Seattle, Washington), a specialized statistical software package widely used for such analyses. In this paper, the author presents a simple approach to fitting the linear relative rate model to epidemiologic data using PROC NLMIXED in the SAS statistical software package (SAS Institute Inc., Cary, North Carolina). This approach is illustrated via analyses of data from a study of mortality in a cohort of South Carolina asbestos textile workers (1940-2001).
%0 Journal Article
%1 Richardson2008
%A Richardson, David B
%D 2008
%J American journal of epidemiology
%K Algorithms Asbestosis Asbestosis:etiology Asbestosis:mortality CohortStudies Female Humans LinearModels Male MiddleAged Models NorthCarolina NorthCarolina:epidemiology OccupationalExposure OccupationalExposure:adverseeffects PoissonDistribution Software Statistical TextileIndustry TextileIndustry:statistics&numericaldata TimeFactors
%N 11
%P 1333-8
%R 10.1093/aje/kwn278
%T A simple approach for fitting linear relative rate models in SAS.
%U http://www.ncbi.nlm.nih.gov/pubmed/18953061
%V 168
%X The linear relative rate model has been employed in epidemiologic analyses of a variety of environmental and occupational exposures. In contrast to an exponential rate model, the linear relative rate model implies that the excess relative rate of disease changes in an additive fashion with exposure. The linear relative rate model may be fitted using EPICURE (HiroSoft International Corporation, Seattle, Washington), a specialized statistical software package widely used for such analyses. In this paper, the author presents a simple approach to fitting the linear relative rate model to epidemiologic data using PROC NLMIXED in the SAS statistical software package (SAS Institute Inc., Cary, North Carolina). This approach is illustrated via analyses of data from a study of mortality in a cohort of South Carolina asbestos textile workers (1940-2001).
%@ 1476-6256
@article{Richardson2008,
abstract = {The linear relative rate model has been employed in epidemiologic analyses of a variety of environmental and occupational exposures. In contrast to an exponential rate model, the linear relative rate model implies that the excess relative rate of disease changes in an additive fashion with exposure. The linear relative rate model may be fitted using EPICURE (HiroSoft International Corporation, Seattle, Washington), a specialized statistical software package widely used for such analyses. In this paper, the author presents a simple approach to fitting the linear relative rate model to epidemiologic data using PROC NLMIXED in the SAS statistical software package (SAS Institute Inc., Cary, North Carolina). This approach is illustrated via analyses of data from a study of mortality in a cohort of South Carolina asbestos textile workers (1940-2001).},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Richardson, David B},
biburl = {https://www.bibsonomy.org/bibtex/27bedd5e886e6ae1296d42283e5228f90/jepcastel},
city = {Department of Epidemiology, CB 7435, School of Public Health, University of North Carolina, Chapel Hill, NC 27599, USA. david.richardson@unc.edu},
doi = {10.1093/aje/kwn278},
interhash = {b6322b307174f195afe6c6cda65ecab1},
intrahash = {7bedd5e886e6ae1296d42283e5228f90},
isbn = {1476-6256},
issn = {1476-6256},
journal = {American journal of epidemiology},
keywords = {Algorithms Asbestosis Asbestosis:etiology Asbestosis:mortality CohortStudies Female Humans LinearModels Male MiddleAged Models NorthCarolina NorthCarolina:epidemiology OccupationalExposure OccupationalExposure:adverseeffects PoissonDistribution Software Statistical TextileIndustry TextileIndustry:statistics&numericaldata TimeFactors},
month = {12},
note = {4872<m:linebreak></m:linebreak>GR: K01-OH008635/OH/NIOSH CDC HHS/United States; JID: 7910653; 2008/10/25 [aheadofprint]; ppublish;},
number = 11,
pages = {1333-8},
pmid = {18953061},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {A simple approach for fitting linear relative rate models in SAS.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18953061},
volume = 168,
year = 2008
}