Abstract
Applications such as public interventions on risk factors associated with the disease necessitate the inference of population attributable risk, especially when multiple risk levels with confounders appear in a cross-sectional data set. For case control studies, inference procedures for multiple exposure levels are available in the literature. However, corresponding procedures for survey-type data are not available. In this paper, we propose two estimation procedures. The first one is based on the Wald statistic and the second one is based on a logarithmic transformation. Simulation studies show that the confidence interval estimates of the attributable risk based on the Wald statistic perform equally well with the logarithmic transformation procedure. When the sample size is small, the large sample approximation is not plausible, so we discuss an exact test procedure. A data set regarding the impact of body mass index on diastolic blood pressure and the cardiovascular disease is included for illustration.
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