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| 1 | Efficient randomized-adaptive designs 显示文摘 | Feifang HU Zhang LX Xuming HE | 2009 | The Annals of Statistics2009，37，5： | 1 |
| 2 | Markov Chain Marginal Bootstrap 显示文摘 | Xuming HE Feifang Hu | 2002 | American Statistical Association2002，97，459： | 1 |
| 3 | Bayesian doubly adaptive randomization in clinical trials显示文摘Bayesian adaptive randomization has attracted increasingly attention in the literature and has been implemented in many phase II clinical trials. Doubly adaptive biased coin design(DBCD) is a superior choice in response-adaptive designs owing to its promising properties. In this paper, we propose a randomized design by combining Bayesian adaptive randomization with doubly adaptive biased coin design. By selecting a fixed tuning parameter, the proposed randomization procedure can target an explicit allocation proportion, and assign more patients to the better treatment simultaneously. Moreover, the proposed randomization is efficient to detect treatment differences. We illustrate the proposed design by its applications to both discrete and continuous responses, and evaluate its operating features through simulation studies. | XIAO YiKe LIU ZhongQiang HU FeiFang | 2017 | Science China Mathematics2017，60，12： | 1 |
| 4 | Efficiently Determine the Starting Sample Size for Progressive Sampling显示文摘 | Baohua Gu Bing Liu Feifang Hu Huan Liu | 2001 | Lecture Notes in Computer Scierce2001，2167，： | 1 |
| 5 | The Gaussian approximation for multi-color generalized Friedman’s urn model显示文摘The generalized Friedman’s urn model is a popular urn model which is widely used in many disciplines.In particular,it is extensively used in treatment allocation schemes in clinical trials.In this paper,we show that both the urn composition process and the allocation proportion process can be approximated by a multi-dimensional Gaussian process almost surely for a multi-color generalized Friedman’s urn model with both homogeneous and non-homogeneous generating matrices.The Gaussian process is a solution of a stochastic differential equation.This Gaussian approximation is important for the understanding of the behavior of the urn process and is also useful for statistical inferences.As an application,we obtain the asymptotic properties including the asymptotic normality and the law of the iterated logarithm for a multi-color generalized Friedman's urn model as well as the randomized-play-the-winner rule as a special case. | ZHANG LiXin HU FeiFang | 2009 | Science China Mathematics2009，52，6： | 1 |
| 6 | A Bootstrap Based on the Estimating Equations of the Linear Model显示文摘 | Feifang Hu | 1995 | Biometrika1995，，2： | 1 |
| 7 | Markov Chain Marginal Bootstrap显示文摘 | Xuming He Feifang Hu | 2002 | Journal of the American Statistical Association2002，，97： | 1 |
| 8 | Covariate-adaptive designs with missing covariates in clinical trials显示文摘Many covariate-adaptive randomization procedures have been proposed and implemented to balance important covariates in clinical trials. These methods are usually based on fully observed covariates. In practice,the covariates of a patient are often partially missing. We propose a novel covariate-adaptive design to deal with missing covariates and study its properties. For the proposed design, we show that as the number of patients increases, the overall imbalance, observed margin imbalance and fully observed stratum imbalance are bounded in probability. Under certain covariate-dependent missing mechanism, the proposed design can balance missing covariates as if the covariates are observed. Finally, we explore our methods and theoretical findings through simulations. | LIU ZhongQiang YIN JianXin HU FeiFang | 2015 | Science China Mathematics2015，58，6： | 0 |
| 9 | Comment on‘Inference after covariate-adaptive randomisation:aspects of methodology and theory’显示文摘In the past decade,significant progress has been made regarding inference under covariate-adaptive randomisation.We thank Prof.Shao for a timely review of the growing literature about the topic.The paper is focused on the most important and commonly used class of covariate-adaptive randomisation methods,i.e.,those balancing discrete covariates.The recent advances in robust inference are emphasised and discussed in detail.Several types of outcomes,such as continuous and time-to-event data,are covered.We here provide some additional recent results from the following five perspectives. | Wei Ma Li-Xin Zhang Feifang Hu | 2021 | Statistical Theory and Related Fields2021，5，3： | 0 |
| 10 | In honor of the 65th birthday of Professor Zhidong Bai显示文摘We are thankful to Science in China Series A: Mathematics for the publication of this special issue in honor of Professor Zhidong Bai of his 65th birthday. Professor Bai is a leading statis- | HU FeiFang & CHEN ZeHua | 2009 | Science China Mathematics2009，52，6： | 0 |
| 11 | Multi-arm covariate-adaptive randomization显示文摘Simultaneously investigating multiple treatments in a single study achieves considerable efficiency in contrast to the traditional two-arm trials.Balancing treatment allocation for influential covariates has become increasingly important in today’s clinical trials.The multi-arm covariate-adaptive randomized clinical trial is one of the most powerful tools to incorporate covariate information and multiple treatments in a single study.Pocock and Simon’s procedure has been extended to the multi-arm case.However,the theoretical properties of multi-arm covariate-adaptive randomization have remained largely elusive for decades.In this paper,we propose a general framework for multi-arm covariate-adaptive designs which also includes the two-arm case,and establish the corresponding theory under widely satisfied conditions.The theoretical results provide new insights into the balance properties of covariate-adaptive randomization procedures and make foundations for most existing statistical inferences under two-arm covariate-adaptive randomization.Furthermore,these open a door to study the theoretical properties of statistical inferences for clinical trials based on multi-arm covariateadaptive randomization procedures. | Feifang Hu Xiaoqing Ye Li-Xin Zhang | 2023 | Science China Mathematics2023，66，1： | 0 |
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