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Interim Monitoring and Sample Size Adjustment in Sequential Multiple Assignment Randomized Trials

Wu, Liwen (2021) Interim Monitoring and Sample Size Adjustment in Sequential Multiple Assignment Randomized Trials. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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A sequential multiple assignment randomized trial (SMART) facilitates comparison of multiple adaptive treatment strategies (ATSs) simultaneously. Such design becomes increasingly popular in the management of chronic diseases, such as mental health disorders. However, SMARTs are generally more resource-intensive than classical clinical trials due to the sequential nature of treatment randomization in multiple stages. Thus, it would be beneficial to add interim analyses allowing for early stop if overwhelming efficacy is observed. In the first part of this dissertation, we introduce group sequential methods to SMARTs to facilitate interim monitoring based on multivariate chi-square distribution. Simulation study demonstrates that the proposed interim monitoring in SMART (IM-SMART) maintains desired type I error and power with reduced expected sample size. Lastly, we illustrate our method by reanalyzing a SMART assessing the effects of cognitive behavioral and physical therapies in patients with knee osteoarthritis and comorbid subsyndromal depressive symptoms.

Clinical trials are often designed based on limited information about effect sizes and variances at the planning stage. Sample size adjustment adds flexibility by re-estimating sample size during the trial to ensure adequate power. Although this adaptation is popular, no method is available for the SMART setting. In the second part of the dissertation, we propose a sample size adjustment procedure for SMARTs. Sample sizes are re-calculated at the interim analysis based on conditional power derived from bivariate non-central chi-square distribution. We demonstrate through simulation studies that even with an under-powered initial sample size due to miss-specified parameters, the proposed method can maintain desirable power, and additional resources are only invested in trials that show promising conditional power at the interim.

Public health significance: Our proposed methods will increase the efficiency of testing treatment strategies for depression, addiction, and many other chronic diseases that rely on continued treatments. This research is the first of its kind to allow interim monitoring and sample size adjustment based on a global test of treatment effects in the SMARTs. The proposed designs are easy to implement and enhances the practical efficiency of the current SMART design. We believe this work will facilitate designing more SMARTs in the future.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Wu, Liwenliw88@pitt.eduliw880000-0001-9329-3794
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWahed, Abdus S.WahedA@edc.pitt.eduwaheda
Committee MemberCheng, Yuyucheng@pitt.eduyucheng
Committee MemberDing, Yingyingding@pitt.eduyingding
Committee MemberKang, Chaeryoncrkang@pitt.educrkang
Date: 27 August 2021
Date Type: Publication
Defense Date: 9 July 2021
Approval Date: 27 August 2021
Submission Date: 5 August 2021
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 77
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 treatment strategy; Dynamic treatment regime; Group sequential analysis; Interim monitoring; Sample size adjustment; Sequential multiple assignment randomized trial
Date Deposited: 27 Aug 2021 18:08
Last Modified: 27 Aug 2022 05:15


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