Using logistic regression to estimate the adjusted attributable risk of low birthweight in an unmatched case-control study.
Other authors have shown how to estimate attributable risk based on stratification. In this paper, we show how to estimate adjusted attributable risks, standard errors, and confidence intervals from an unmatched case-control study that has population-based controls and uses the logistic regression model to estimate relative risk. We apply the method to data from a case-control study of low birthweight. The method is conceptually simple, has no assumptions beyond those of the logistic model, makes use of computer-intensive statistical techniques (the bootstrap), and extends to interactions. A Fortran computer program to carry out the computations is available from the authors upon request.
Kooperberg
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Department of Statistics, University of California, Berkeley.
Petitti
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DB
D016022:Case-Control Studies; D016001:Confidence Intervals; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D016015:Logistic Models; D008433:Mathematics; D012306:Risk; D012984:Software