Comparison of Pocock and Simon's covariate-adaptive randomization procedures in clinical trials

被引:2
|
作者
Shan, Guogen [1 ]
Li, Yulin [1 ]
Lu, Xinlin [1 ]
Zhang, Yahui [1 ]
Wu, Samuel S. [1 ]
机构
[1] Univ Florida, Dept Biostat, Gainesville, FL 32610 USA
关键词
Additional covariates; Allocation predictability; Covariate adaptive randomization; Imbalance score; Pocock and Simon; Statistical power; PROGNOSTIC-FACTORS; ALLOCATION; DESIGN; MINIMIZATION; ADJUSTMENT; URN;
D O I
10.1186/s12874-024-02151-3
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
When multiple influential covariates need to be balanced during a clinical trial, stratified blocked randomization and covariate-adaptive randomization procedures are frequently used in trials to prevent bias and enhance the validity of data analysis results. The latter approach is increasingly used in practice for a study with multiple covariates and limited sample sizes. Among a group of these approaches, the covariate-adaptive procedures proposed by Pocock and Simon are straightforward to be utilized in practice. We aim to investigate the optimal design parameters for the patient treatment assignment probability of their developed three methods. In addition, we seek to answer the question related to the randomization performance when additional covariates are added to the existing randomization procedure. We conducted extensive simulation studies to address these practically important questions.
引用
下载
收藏
页数:12
相关论文
共 50 条
  • [21] Multi-arm covariate-adaptive randomization
    Hu, Feifang
    Ye, Xiaoqing
    Zhang, Li-Xin
    SCIENCE CHINA-MATHEMATICS, 2023, 66 (01) : 163 - 190
  • [22] Choosing a covariate-adaptive randomization procedure in practice
    Zagoraiou, Maroussa
    JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 2017, 27 (05) : 845 - 857
  • [23] Inference under covariate-adaptive randomization with multiple treatments
    Bugni, Federico A.
    Canay, Ivan A.
    Shaikh, Azeem M.
    QUANTITATIVE ECONOMICS, 2019, 10 (04) : 1747 - 1785
  • [24] Inference under covariate-adaptive randomization withimperfect compliance
    Bugni, Federico A.
    Gao, Mengsi
    JOURNAL OF ECONOMETRICS, 2023, 237 (01)
  • [25] Inference under covariate-adaptive randomization: A simulation study
    Callegaro, Andrea
    Shree, B. S. Harsha
    Karkada, Naveen
    STATISTICAL METHODS IN MEDICAL RESEARCH, 2021, 30 (04) : 1072 - 1080
  • [26] A theory for testing hypotheses under covariate-adaptive randomization
    Shao, Jun
    Yu, Xinxin
    Zhong, Bob
    BIOMETRIKA, 2010, 97 (02) : 347 - 360
  • [27] A unifying framework for standard and covariate-adaptive randomization procedures based on minimizing suitable imbalance functions
    Berger, Vance W.
    CONTEMPORARY CLINICAL TRIALS, 2013, 36 (02) : 527 - 530
  • [28] The impact of misclassification on covariate-adaptive randomized clinical trials with generalized linear models
    Wang, Tong
    Ma, Wei
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2025, 234
  • [29] General covariate-adaptive randomization targeting unequal allocation ratio
    Liu, Zhongqiang
    Ban, Tao
    Huang, Tao
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2017, 191 : 68 - 80
  • [30] The impact of covariate misclassification using generalized linear regression under covariate-adaptive randomization
    Fan, Liqiong
    Yeatts, Sharon D.
    Wolf, Bethany J.
    McClure, Leslie A.
    Selim, Magdy
    Palesch, Yuko Y.
    STATISTICAL METHODS IN MEDICAL RESEARCH, 2018, 27 (01) : 20 - 34