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Adaptive Randomization in Sequential Multiple Assignment Randomized Trials

Wang, Junyao (2021) Adaptive Randomization in Sequential Multiple Assignment Randomized Trials. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Sequential multiple assignment randomized trials (SMARTs) are systematic and efficient media for comparing dynamic treatment regimes (DTRs), where each patient is involved in multiple stages of treatment with the randomization at each stage depending on the patient's previous treatment history and outcomes. Generally, in SMARTs, patients are randomized equally to ethically acceptable treatment options regardless of how effective those treatments were during previous stages, which results in some undesirable consequences in practice, such as less retention and lower treatment adherence. In clinical trials, response adaptive randomization (RAR) has been proposed to alleviate such concerns. In this dissertation, we introduce the RAR into SMART to benefit more patients with more efficacy regimes, further to increase recruitment and retention.

In the first part of the dissertation, we propose a response adaptive SMART (RA-SMART) design where the randomization probabilities are imbalanced in favor of more promising treatments based on the accumulated information on treatment efficacy from previous patients and stages. The operating characteristics of the RA-SMART relative to SMART, including the consistency and efficiency of estimated response rate under each DTR, the power of identifying the optimal DTR, and the number of patients treated with the optimal and the worst DTRs, are assessed through extensive simulation studies. Finally, some practical suggestions are discussed.

In the second part of the dissertation, we propose an optimal response adaptive SMART (ORA-SMART) design for minimizing the number of failures in the trial without sacrificing the power for identifying the optimal DTR. We provide an algorithm to solve for optimal randomization probabilities at stage II, applying the Majorization-Minimization method. The proposed ORA-SMART is compared to the regular SMART with equal randomization probabilities at stage II under various scenarios through simulation. We conclude with a discussion on potential implications and future research.

Public health significance: Sequential multiple assignment randomized trials (SMARTs) have been widely used in clinical research of treatment sequencing in many disease areas such as behavioral and mental health and oncology. The designs proposed in this dissertation will help alleviate ethical concerns about treating patients with less effective treatments, and consequently, will improve public health.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Wang, Junyaojuw55@pitt.eduJUW55
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWahed, Abdus S.waheda@edc.pitt.eduwaheda
Committee MemberAnderson, Steward J.sja@pitt.edusja
Committee MemberChang, Chung-Chou H.changj@pitt.educhangj
Committee MemberCheng, Yuyucheng@pitt.eduyucheng
Committee MemberDing, Yingyingding@pitt.eduyingding
Date: 27 August 2021
Date Type: Publication
Defense Date: 13 July 2021
Approval Date: 27 August 2021
Submission Date: 1 August 2021
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 153
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: adaptive randomization, adaptive treatment strategies, clinical trial, dynamic treatment regimes, majorization-minimization, optimal design, sequential multiple assignment randomized trials.
Date Deposited: 27 Aug 2021 17:55
Last Modified: 27 Aug 2022 05:15


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