Addressing sequential and concurrent treatment regimens in a small n sequential, multiple assignment, randomized trial (snSMART) in the MISTIC study

被引:0
作者
Cheng, Yuwei [1 ]
Tremoulet, Adriana [2 ]
Burns, Jane [2 ]
Jain, Sonia [1 ,3 ]
机构
[1] Univ Calif San Diego, Herbert Wertheim Sch Publ Hlth & Human Longev Sci, La Jolla, CA 92093 USA
[2] Univ Calif San Diego, Dept Pediat, La Jolla, CA USA
[3] Univ Calif San Diego, Biostat Res Ctr BRC, La Jolla, CA USA
关键词
Clinical trial design; SMART design; Bayesian joint stage model; rare disease; DESIGN;
D O I
10.1080/10543406.2023.2292206
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Multisystem Inflammatory Syndrome in children (MIS-C) is a rare and novel pediatric complication linked to COVID-19 exposure, which was first identified in April 2020. A small n, Sequential, Multiple Assignment, Randomized Trial (snSMART) was applied to the Multisystem Inflammatory Syndrome Therapies in Children Comparative Effectiveness Study (MISTIC) to efficiently evaluate multiple competing treatments. In the MISTIC snSMART study, participants are randomized to one of three interventions (steroids, infliximab or anakinra), and potentially re-randomized to the remaining two treatments depending on their response to the first randomized treatment. However, given the novelty and urgency of the MIS-C disease, in addition to patient welfare concerns, treatments were not always administered sequentially, but allowed to be administered concurrently if deemed medically necessary. We propose a pragmatic modification to the original snSMART design to address the analysis of concurrent versus sequential treatments in the MISTIC study. A modified Bayesian joint stage model is developed that can distinguish a concurrent treatment effect from a sequential treatment effect. A simulation study is conducted to demonstrate the improved accuracy and efficiency of the primary aim to estimate the first stage treatments' response rates and the secondary aim to estimate the combined first and second stage treatments' responses in the proposed model compared to the standard snSMART Bayesian joint stage model. We observed that the modified model has improved efficiency in terms of bias and rMSE under large sample size settings.
引用
收藏
页码:106 / 124
页数:19
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