Record Display for the EPA National Library Catalog

RECORD NUMBER: 14 OF 18

OLS Field Name OLS Field Data
Main Title Regression Models for Cohort Mortality Studies.
Author Whittemore, A. S. ;
CORP Author Stanford Univ., CA. Dept. of Family, Community and Preventive Medicine.;Health Effects Research Lab., Research Triangle Park, NC.;National Institutes of Health, Bethesda, MD.;SIAM Inst. for Mathematics and Society, Philadelphia, PA.;National Science Foundation, Washington, DC.
Publisher c1986
Year Published 1986
Report Number EPA/R-813495; EPA/600/J-86/526;
Stock Number PB90-232166
Additional Subjects Mortality ; Regression analysis ; Mathematical models ; Formulas(Mathematics) ; Reprints ; Cohort studies ; Occupational safety and health ; Health hazards ; Environmental exposure ; Survival analysis ; Proportional hazards models ; Cause of death
Holdings
Library Call Number Additional Info Location Last
Modified
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Status
NTIS  PB90-232166 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. NTIS 08/27/1990
Collation 19p
Abstract
Cohort studies evaluate suspect health hazards from occupational or environmental exposures by recording the facts and causes of deaths in the exposed group as they occur over an extended time period. The article reviews several methods for analyzing cohort mortality data and shows them to be special cases of a single procedure. The procedure represents death rates as the product of an age-specific baseline rate that applies in the absence of exposure, times a function of exposures. Maximum likelihood methods are used to estimate unknown regression parameters in the function of exposures. The log-likelihood kernel for the data is shown to be that of N independent Poisson variates, where N is the total number of person-units of mortality observation time in the study. The expected values of these variates depend on the exposures and regression parameters. The latter can be estimated using packaged software programs for Poisson regression on any microcomputer that supports ANSI Standard FORTRAN.