American journal of epidemiology
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Targeted screening remains an important approach to human immunodeficiency virus (HIV) testing. The authors aimed to derive and validate an instrument to accurately identify patients at risk for HIV infection, using patient data from a metropolitan sexually transmitted disease clinic in Denver, Colorado (1996-2008). With multivariable logistic regression, they developed a risk score from 48 candidate variables using newly identified HIV infection as the outcome. ⋯ The final score included age, gender, race/ethnicity, sex with a male, vaginal intercourse, receptive anal intercourse, injection drug use, and past HIV testing, and values ranged from -14 to +81. For persons with scores of <20, 20-29, 30-39, 40-49, and ≥50, HIV prevalences were 0.31% (95% confidence interval (CI): 0.20, 0.45) (n = 27/8,782), 0.41% (95% CI: 0.29, 0.57) (n = 36/8,677), 0.99% (95% CI: 0.63, 1.47) (n = 24/2,431), 1.59% (95% CI: 1.02, 2.36) (n = 24/1,505), and 3.59% (95% CI: 2.73, 4.63) (n = 57/1,588), respectively. The risk score accurately categorizes patients into groups with increasing probabilities of HIV infection.
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Case-cohort and nested case-control designs are often used to select an appropriate subsample of individuals from prospective cohort studies. Despite the great attention that has been given to the calculation of association estimators, no formal methods have been described for estimating risk prediction measures from these 2 sampling designs. Using real data from the Swedish Twin Registry (2004-2009), the authors sampled unstratified and stratified (matched) case-cohort and nested case-control subsamples and compared them with the full cohort (as "gold standard"). ⋯ Overall, stratification improved efficiency, with stratified case-cohort designs being comparable to matched nested case-control designs. Individual risks and prediction measures calculated by using case-cohort and nested case-control designs after appropriate reweighting could be assessed with good efficiency, except for the finely matched nested case-control design, where matching variables could not be included in the individual risk estimation. In conclusion, the authors have shown that case-cohort and nested case-control designs can be used in settings where the research aim is to evaluate the prediction ability of new markers and that matching strategies for nested case-control designs may lead to biased prediction measures.