Model-based Estimation of Population Attributable Risk under Cross-sectional Sampling
| Autor: | J. R. Landis, Srabashi Basu |
|---|---|
| Rok vydání: | 1995 |
| Předmět: |
Adult
Models Statistical Adolescent National Health and Nutrition Examination Survey Epidemiology business.industry Linear model Sampling (statistics) Logistic regression Cohort Studies Cross-Sectional Studies Risk Factors Hypertension Attributable risk Covariate Sampling design Statistics Prevalence Humans Medicine Female Risk factor Epidemiologic Methods business Retrospective Studies |
| Zdroj: | American Journal of Epidemiology. 142:1338-1343 |
| ISSN: | 1476-6256 0002-9262 |
| Popis: | The covariate-adjusted population attributable risk (PAR) measures the proportionate reduction in disease prevalence in the target population when the putative risk factor is removed, after adjusting for covariate effects. This paper extends the model-based approach developed for retrospective and cohort studies to the cross-sectional sampling design. An appropriate logit linear model is utilized to estimate the covariate-adjusted attributable risk. The asymptotic variance of this complex ratio estimate is obtained using Taylor series expansions which incorporate the sampling variation of the estimated model parameters and the appropriate estimates of risk factor prevalence. These methods are illustrated with cardiovascular disease risk factor data from the second National Health and Nutrition Examination Survey (NHANES II). |
| Databáze: | OpenAIRE |
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