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Standard Errors for Attributable Risk for Simple and Complex Sample Designs
Author(s) -
Graubard Barry I.,
Fears Thomas R.
Publication year - 2005
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00355.x
Subject(s) - statistics , estimator , stratified sampling , national health and nutrition examination survey , cluster sampling , attributable risk , simple random sample , logistic regression , matching (statistics) , sample size determination , confounding , population , variance (accounting) , mathematics , propensity score matching , odds ratio , medicine , environmental health , accounting , business
Summary Adjusted attributable risk (AR) is the proportion of diseased individuals in a population that is due to an exposure. We consider estimates of adjusted AR based on odds ratios from logistic regression to adjust for confounding. Influence function methods used in survey sampling are applied to obtain simple and easily programmable expressions for estimating the variance of . These variance estimators can be applied to data from case–control, cross‐sectional, and cohort studies with or without frequency or individual matching and for sample designs with subject samples that range from simple random samples to (sample) weighted multistage stratified cluster samples like those used in national household surveys. The variance estimation of is illustrated with: (i) a weighted stratified multistage clustered cross‐sectional study of childhood asthma from the Third National Health and Examination Survey (NHANES III), and (ii) a frequency‐matched case–control study of melanoma skin cancer.