Browsing by Author "Zhang, Zhiwei"

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    In utero exposure to cigarette smoking, environmental tobacco smoke and reproductive hormones in US girls approaching puberty
    (Karger, 2015) Gollenberg, Audra L.; Addo, O. Yaw; Zhang, Zhiwei; Hediger, Mary L.; Himes, John H.; Lee, Peter A.; Department of Pediatrics, IU School of Medicine
    BACKGROUND/AIMS: Evidence is unclear whether prenatal smoking affects age at menarche and pubertal development, and its impact upon hormones has not been well studied. We aim to identify potential pathways through which prenatal smoking and environmental tobacco smoke (ETS) affect reproductive hormones in girls approaching puberty. METHODS: We examined the association between prenatal smoking, current ETS and luteinizing hormone (LH) and inhibin B (InB) in 6- to 11-year-old girls in the 3rd National Health and Nutrition Examination Survey, 1988-1994. Parents/guardians completed interviewer-assisted questionnaires on health and demographics at the time of physical examination. Residual blood samples were analyzed for reproductive hormones in 2008. RESULTS: Of 660 girls, 19 and 39% were exposed to prenatal smoke and current ETS, respectively. Accounting for multiple pathways in structural equation models, prenatally exposed girls had significantly lower LH (β = -0.205 log-mIU/ml, p < 0.0001) and InB (β = -0.162, log-pg/ml, p < 0.0001). Prenatal smoking also influenced LH positively and InB negatively indirectly through BMI-for-age. ETS was positively associated with LH, but not with InB. CONCLUSION: Exposure to maternal smoking may disrupt reproductive development manifesting in altered hormone levels near puberty.
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    Subgroup selection in adaptive signature designs of confirmatory clinical trials
    (Wiley, 2017-02) Zhang, Zhiwei; Li, Meijuan; Lin, Min; Soon, Guoxing; Greene, Tom; Shen, Changyu; Department of Medicine, IU School of Medicine
    The increasing awareness of treatment effect heterogeneity has motivated flexible designs of confirmatory clinical trials that prospectively allow investigators to test for treatment efficacy for a subpopulation of patients in addition to the entire population. If a target subpopulation is not well characterized in the design stage, it can be developed at the end of a broad eligibility trial under an adaptive signature design. The paper proposes new procedures for subgroup selection and treatment effect estimation (for the selected subgroup) under an adaptive signature design. We first provide a simple and general characterization of the optimal subgroup that maximizes the power for demonstrating treatment efficacy or the expected gain based on a specified utility function. This characterization motivates a procedure for subgroup selection that involves prediction modelling, augmented inverse probability weighting and low dimensional maximization. A cross-validation procedure can be used to remove or reduce any resubstitution bias that may result from subgroup selection, and a bootstrap procedure can be used to make inference about the treatment effect in the subgroup selected. The approach proposed is evaluated in simulation studies and illustrated with real examples.