Multivariate cohort analysis.
Modern methods of categorical and survival data analysis are usefully applied to the multivariate analysis of follow-up data that arise in epidemiologic cohort studies. They provide a formal basis for extending analyses based on the standardized mortality ratio into the multivariate domain so as to permit simultaneous consideration of such risk factors as age, duration, and intensity of exposure; age and calendar year of follow-up; and personal characteristics. Analogous methods are available that control for demographic variables internally, without reference to vital statistics or other standard rates. Various model structures allow for the effects of different variables to combine in an additive, multiplicative, or mixed (additive relative risks) fashion. Illustrative analyses are provided of the relationship between respiratory cancer mortality and arsenic exposure in a cohort of Montana smelter workers.
D001151:Arsenic / Q000009:adverse effects*; D004812:Epidemiologic Methods; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D009784:Occupational Diseases / Q000401:mortality*; D011446:Prospective Studies; D012044:Regression Analysis; D012142:Respiratory Tract Neoplasms / Q000401:mortality*; D013223:Statistics as Topic; D013997:Time Factors; D014481:United States
D001151:Arsenic