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Activity Number: 444
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract - #310167
Title: Bootstrap Estimation of Variance from ROC Curve Analysis of Complex NHANES Survey Data
Author(s): Rey DeCastro*+ and Yang Xia and Connie Sosnoff and Lee-Yang Wong
Companies: CDC/National Center for Environmental Health and CDC NCEH and CDC NCEH and CDC NCEH
Keywords: ROC curve ; cutpoint ; sensitivity and specificity ; bootstrap ; variance estimation ; survey data analysis
Abstract:

The secondary data analyst is fundamentally hindered from implementing bootstrap variance estimation for complex survey data because information for adjusting bootstrap replicate weights for post-stratification and non-response are usually not publicly available. Taking this is as a given, we ignored post-stratification and non-response weight adjustments in order to implement replicate adjustments described in Rust & Rao 1996, then proceeded with bootstrap estimation (1000 replicates) of a simple weighted statistic (the sum) for complex survey data (NHANES) on the tobacco smoke biomarker NNAL in urine for comparison with the estimate from Taylor series linearization for the full survey sample. The bootstrap and Taylor series estimates were found to be very close (< ±0.52 percent). We therefore proceeded to use the bootstrap to estimate variances for the optimal cutpoint and c-index from ROC curve analysis of NHANES urinary NNAL data to discriminate smokers from non-smokers. The optimal cutpoint was 19.92 NNAL ng/g Cr [bootstrap CI 19.77:20.08; CV=12.39%] and c-index (equivalent to the area under the ROC curve) was 0.98978 [bootstrap CI 0.98970:0.98985; CV=0.11%].


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