Browsing by Author "Sun, Wei"

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    Gender and age interact to affect early outcome after intracerebral hemorrhage.
    (PLoS One, 2013) Umeano, Odera; Phillips-Bute, Barbara; Hailey, Claire E; Sun, Wei; Gray, Marisa C; Roulhac-Wilson, Briana; McDonagh, David L; Kranz, Peter G; Laskowitz, Daniel T; James, Michael L
    BACKGROUND: Intracerebral hemorrhage (ICH) is a common and devastating form of cerebrovascular disease. In ICH, gender differences in outcomes remain relatively understudied but have been examined in other neurological emergencies. Further, a potential effect of age and gender on outcomes after ICH has not been explored. This study was designed to test the hypothesis that age and gender interact to modify neurological outcomes after ICH. METHODS: Adult patients admitted with spontaneous primary supratentorial ICH from July 2007 through April 2010 were assessed via retrospective analysis of an existing stroke database at Duke University. Univariate analysis of collected variables was used to compare gender and outcome. Unfavorable outcome was defined as discharge to hospice or death. Using multivariate regression, the combined effect of age and gender on outcome after ICH was analyzed. RESULTS: In this study population, women were younger (61.1+14.5 versus 65.8+17.3 years, p=0.03) and more likely to have a history of substance abuse (35% versus 8.9%, p<0.0001) compared to men. Multivariable models demonstrated that advancing age had a greater effect on predicting discharge outcome in women compared to men (p=0.02). For younger patients, female sex was protective; however, at ages greater than 60 years, female sex was a risk factor for discharge to hospice or death. CONCLUSION: While independently associated with discharge to hospice or death after ICH, the interaction effect between gender and age demonstrated significantly stronger correlation with early outcome after ICH in a single center cohort. Prospective study is required to verify these findings.
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    PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs.
    (Biometrics, 2016-03) Ha, Min Jin; Sun, Wei; Xie, Jichun
    Estimation of the skeleton of a directed acyclic graph (DAG) is of great importance for understanding the underlying DAG and causal effects can be assessed from the skeleton when the DAG is not identifiable. We propose a novel method named PenPC to estimate the skeleton of a high-dimensional DAG by a two-step approach. We first estimate the nonzero entries of a concentration matrix using penalized regression, and then fix the difference between the concentration matrix and the skeleton by evaluating a set of conditional independence hypotheses. For high-dimensional problems where the number of vertices p is in polynomial or exponential scale of sample size n, we study the asymptotic property of PenPC on two types of graphs: traditional random graphs where all the vertices have the same expected number of neighbors, and scale-free graphs where a few vertices may have a large number of neighbors. As illustrated by extensive simulations and applications on gene expression data of cancer patients, PenPC has higher sensitivity and specificity than the state-of-the-art method, the PC-stable algorithm.