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.
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Review Meta Analysis
Bias in observational studies of prevalent users: lessons for comparative effectiveness research from a meta-analysis of statins.
Randomized clinical trials (RCTs) are usually the preferred strategy with which to generate evidence of comparative effectiveness, but conducting an RCT is not always feasible. Though observational studies and RCTs often provide comparable estimates, the questioning of observational analyses has recently intensified because of randomized-observational discrepancies regarding the effect of postmenopausal hormone replacement therapy on coronary heart disease. Reanalyses of observational data that excluded prevalent users of hormone replacement therapy led to attenuated discrepancies, which begs the question of whether exclusion of prevalent users should be generally recommended. ⋯ The pooled, multivariate-adjusted mortality hazard ratio for statin use was 0.77 (95% confidence interval (CI): 0.65, 0.91) in 4 studies that compared incident users with nonusers, 0.70 (95% CI: 0.64, 0.78) in 13 studies that compared a combination of prevalent and incident users with nonusers, and 0.54 (95% CI: 0.45, 0.66) in 13 studies that compared prevalent users with nonusers. The corresponding hazard ratio from 18 RCTs was 0.84 (95% CI: 0.77, 0.91). It appears that the greater the proportion of prevalent statin users in observational studies, the larger the discrepancy between observational and randomized estimates.
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The association between parental smoking and risk of childhood acute lymphoblastic leukemia (ALL) was investigated in an Australian population-based case-control study that included 388 cases and 868 controls aged <15 years, recruited from 2003 to 2006. Both of the child's parents provided information about their smoking habits for each year from age 15 years to the child's birth. Data were analyzed by logistic regression. ⋯ Meta-analyses of paternal smoking, including results from the Australian Study of Causes of Acute Lymphoblastic Leukemia in Children and those of previous studies, produced summary odds ratios of 1.15 (95% confidence interval: 1.06, 1.24) for any paternal smoking around the time of the child's conception and 1.44 (95% confidence interval: 1.24, 1.68) for smoking ≥20 cigarettes per day at that time. Study results suggest that heavier paternal smoking around the time of conception is a risk factor for childhood ALL. Men should be strongly encouraged to cease smoking, particularly when planning to start a family.
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In this article, the authors demonstrate a time-series analysis based on a hierarchical Bayesian model of a Poisson outcome with an excessive number of zeroes. The motivating example for this analysis comes from the intensive care unit (ICU) of an urban university teaching hospital (New Haven, Connecticut, 2002-2004). Studies of medication use among older patients in the ICU are complicated by statistical factors such as an excessive number of zero doses, periodicity, and within-person autocorrelation. ⋯ By applying elements of time-series analysis within both frequentist and Bayesian frameworks, the authors evaluate differences in shift-based dosing of medication in a medical ICU. From a small sample and with adjustment for excess zeroes, linear trend, autocorrelation, and clinical covariates, both frequentist and Bayesian models provide evidence of a significant association between a specific nursing shift and dosing level of a sedative medication. Furthermore, the posterior distributions from a Bayesian random-effects Poisson model permit posterior predictive simulations of related results that are potentially difficult to model.