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Creators/Authors contains: "Zhu, Hongjian"

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  1. ; ; ; ; ; ; ; ; ; ; et al (, IEEE)
    Free, publicly-accessible full text available May 23, 2026
  2. ; ; ; ; ; ; ; ; ; ; et al (, IEEE 41st International Conference on Data Engineering (ICDE))
    Free, publicly-accessible full text available May 13, 2026
  3. ; ; ; ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available December 23, 2025
  4. ; ; ; ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available November 17, 2025
  5. ; ; ; (, The New England Journal of Statistics in Data Science)
    In addition to scientific questions, clinical trialists often explore or require other design features, such as increasing the power while controlling the type I error rate, minimizing unnecessary exposure to inferior treatments, and comparing multiple treatments in one clinical trial. We propose implementing adaptive seamless design (ASD) with response adaptive randomization (RAR) to satisfy various clinical trials’ design objectives. However, the combination of ASD and RAR poses a challenge in controlling the type I error rate. In this paper, we investigated how to utilize the advantages of the two adaptive methods and control the type I error rate. We offered the theoretical foundation for this procedure. Numerical studies demonstrated that our methods could achieve efficient and ethical objectives while controlling the type I error rate. 
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    Full Text Available 
  6. ; ; (, Contemporary Clinical Trials)
    Full Text Available 
  7. ; (, Biometrics)
    Abstract We consider theoretical and practical issues for innovatively using a large number of covariates in clinical trials to achieve various design objectives without model misspecification. Specifically, we propose a new family of semiparametric covariate‐adjusted response‐adaptive randomization (CARA) designs and we use the target maximum likelihood estimation (TMLE) to analyze the correlated data from CARA designs. Our approach can flexibly achieve multiple objectives and correctly incorporate the effect of a large number of covariates on the responses without model misspecification. We also obtain the consistency and asymptotic normality of the target parameters, allocation probabilities, and allocation proportions. Numerical studies demonstrate that our approach has advantages over existing approaches, even when the data‐generating distribution is complicated. 
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