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研究生： 茆玉麟
Mau, Yu-Lin
論文名稱： 利用複合性指標對重複事件和終點事件臨床試驗進行依反應變數調整隨機化的方法研究
Response-Adaptive Randomization Procedure for Recurrent Events and Terminal Event Data with a Composite Endpoint
指導教授： 蘇佩芳
Su, Pei-Fang
學位類別： 博士
Doctor
系所名稱： 管理學院 - 統計學系
Department of Statistics
論文出版年： 2023
畢業學年度： 112
語文別： 英文
論文頁數： 66
中文關鍵詞： 分配規則 、平衡隨機分派 、雙重自適應有偏硬幣設計 、Fisher 信息矩陣
外文關鍵詞： Allocation rule, Balanced randomization, Doubly adaptive biased coin design, Fisher information matrix
相關次數： 點閱：101  下載：20 
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    • 在臨床試驗或觀察性研究中，重複事件和終點事件數據是常見的。為了評估治療對這兩種事件的影響，通常使用複合性指標作為評估標準。在分析重複事件和終點事件數據時，通常採用聯合脆弱性模型來進行分析。在這項研究中，目標是基於聯合脆弱性模型開發基於目標的自適應隨機化策略，使用複合性指標作為評估標準。我們討論了平衡設計和反應自適應隨機化。平衡設計通常被使用，而反應自適應隨機化的分配方法可以讓更多受試者接受更有效的治療，這是一種理想和道德的方法。根據結果顯示，提出的方法在使用複合性指標時相對於平衡設計可以減少接受次優治療的試驗參與者人數，同時實現所需的分配目標。還提供了一個用於計算樣本大小和分配機率的 R Shiny 應用介面。最後，應用兩項臨床試驗用於所提出的方法。

    In clinical trials or observational studies, recurrent events and terminal event data are common. To evaluate the effect of treatment on both types of events, composite endpoints are often used as evaluation criteria. For the analysis of recurrent events and terminal event data, a joint frailty model is typically employed. In this study, the goal is to develop target-driven adaptive randomization strategies by using composite endpoints based on a joint frailty model. We discuss balanced designs and response-adaptive randomization. Balanced designs are commonly used, while the allocation method of the response-adaptive randomization can allow more participants to receive more effective treatment, which is a desirable and ethical approach. According to the results, the proposed procedure when using composite endpoints can reduce the number of trial participants receiving inferior treatment compared to that of the balanced designs while achieving the desired allocation target. An R Shiny application is also available for calculating sample sizes and allocation probabilities. Finally, two clinical trials are used to introduce the proposed procedure.

    摘要i Abstract ii 誌謝iii Table of Contents iv List of Tables vi List of Figures vii Chapter 1. Introduction 1 1.1 Clinical Trial1 1.1.1 Outcome 1 1.1.2 Composite Endpoint2 1.2 Randomized Controlled Trials. . 3 1.2.1 Complete Randomization4 1.2.2 Restricted Randomization4 1.3 Adaptive Designs.5 1.3.1 Introduction ICH. 5 1.3.2 ICH E20 5 1.3.3 Introduction of Adaptive Designs.6 1.4 Real Cases7 1.4.1 COAPT Trial.7 1.4.2 CUPID2 Trial. . 7 1.4.3 Research Goal. . 9 Chapter 2. Literature Review 10 2.1 Joint Frailty Model.10 2.2 Response Adaptive Randomization11 2.3 DBCD Based on Binary Outcome13 2.4 DBCD Based on Survival Outcome14 Chapter 3. Proposed Methods 16 3.1 Model Formulation.16 3.2 Hypothesis Testing.18 3.3 Treatment Allocation Schemes 20 3.4 Allocation Targets.22 3.4.1 Rule 0: Balanced Allocation22 3.4.2 Rule 1: Maximize the Test Power.22 3.4.3 Rule 2: Minimize the Total Number of Recurrent Events.23 3.4.4 Rule 3: Minimize the Total Hazard Rate of Terminal Events. . 23 3.5 Asymptotic Theory of the Allocation Rule.24 Chapter 4. Simulation Studies 26 4.1 Simulation for Parameter Estimation and Required Total Sample Size. 26 4.2 Simulation Performance for the RAR Procedure29 4.2.1 Scenario (A).31 4.2.2 Scenario (B).32 4.2.3 Scenario (C).33 4.2.4 Scenario (D).34 4.3 Sensitivity Analysis.37 4.3.1 Consider Different Frailty Distributions. 37 4.3.2 Consider the Baseline Function 38 Chapter 5. Redesign Clinical Trials 41 5.1 The COAPT Trial.41 5.2 The CUPID 2 Trial.42 Chapter 6. R Shiny Interface 44 6.1 Calculate Sample Size. . 44 6.2 Calculate Allocation Probability 45 Chapter 7. Conclusion 48 7.1 Summary 48 7.2 Future Work49 References 51 Appendix A. The Derivation of MLE 53 Appendix B. Six Conditions for the Allocation Function 57

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