Browsing by Author "Li, Lingling"

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    Inverse probability weighting for covariate adjustment in randomized studies
    (Wiley, 2014-02) Shen, Changyu; Li, Xiaochun; Li, Lingling; Biostatistics, School of Medicine
    Covariate adjustment in randomized clinical trials has the potential benefit of precision gain. It also has the potential pitfall of reduced objectivity as it opens the possibility of selecting a 'favorable' model that yields strong treatment benefit estimate. Although there is a large volume of statistical literature targeting on the first aspect, realistic solutions to enforce objective inference and improve precision are rare. As a typical randomized trial needs to accommodate many implementation issues beyond statistical considerations, maintaining the objectivity is at least as important as precision gain if not more, particularly from the perspective of the regulatory agencies. In this article, we propose a two-stage estimation procedure based on inverse probability weighting to achieve better precision without compromising objectivity. The procedure is designed in a way such that the covariate adjustment is performed before seeing the outcome, effectively reducing the possibility of selecting a 'favorable' model that yields a strong intervention effect. Both theoretical and numerical properties of the estimation procedure are presented. Application of the proposed method to a real data example is presented.
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    New statistical methods for the evaluation of effectivenss and safety of a medical intervention in using observational data
    (2016-12-05) Zhan, Jia; Shen, Changyu; Li, Xiaochun; Li, Lingling; Xu, Huiping; Wessel, Jennifer
    Observational studies offer unique advantages over randomized clinical trials (RCTs) in many situations where RCTs are not feasible or suffer from major limitations such as insufficient sample sizes and narrowly focused populations. Because observational data are relatively easy and inexpensive to access, and contain rich and comprehensive demographic and medical information on large and representative populations, they have played a major role in the assessment of the effectiveness and safety of medical interventions. However, observational data also have the challenges of higher rates of missing data and the confounding effect. My proposal is on the development of three statistical methods to address these challenges. The first method is on the refinement and extension of a multiply robust (MR) estimation procedure that simultaneously accounts for the confounding effect and missing covariate process, where we derived the asymptotic variance estimator and extended the method to the scenario where the missing covariate is continuous. The second method focuses on the improvement of estimation precision in an RCT by a historical control cohort. This was achieved through augmenting the conventional effect estimator with an extra mean zero (approximately) term correlated with the conventional effect estimator. In the third method, we calibrated the hidden database bias of an electronic medical records database and utilized an empirical Bayes method to improve the accuracy of the estimation of the risk of acute myocardial infarction associated with a drug by borrowing information from other drugs.