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Evaluating Joint Effects of Induction-Salvage Treatment Regimes on Overall Survival in Acute Leukemia.评估诱导挽救治疗方案对急性白血病总生存期的联合影响。
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Experimental design and primary data analysis methods for comparing adaptive interventions.比较适应性干预的实验设计和主要数据分析方法。
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Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer.晚期前列腺癌序贯随机试验中可行动态治疗方案的评估
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癌症研究中的SMART设计:过去、现在与未来。

SMART designs in cancer research: Past, present, and future.

作者信息

Kidwell Kelley M

机构信息

Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

出版信息

Clin Trials. 2014 Aug;11(4):445-456. doi: 10.1177/1740774514525691. Epub 2014 Apr 14.

DOI:10.1177/1740774514525691
PMID:24733671
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4431956/
Abstract

BACKGROUND

Cancer affects millions of people worldwide each year. Patients require sequences of treatment based on their response to previous treatments to combat cancer and fight metastases. Physicians provide treatment based on clinical characteristics, changing over time. Guidelines for these individualized sequences of treatments are known as dynamic treatment regimens (DTRs) where the initial treatment and subsequent modifications depend on the response to previous treatments, disease progression, and other patient characteristics or behaviors. To provide evidence-based DTRs, the Sequential Multiple Assignment Randomized Trial (SMART) has emerged over the past few decades.

PURPOSE

To examine and learn from past SMARTs investigating cancer treatment options, to discuss potential limitations preventing the widespread use of SMARTs in cancer research, and to describe courses of action to increase the implementation of SMARTs and collaboration between statisticians and clinicians.

CONCLUSION

There have been SMARTs investigating treatment questions in areas of cancer, but the novelty and perceived complexity has limited its use. By building bridges between statisticians and clinicians, clarifying research objectives, and furthering methods work, there should be an increase in SMARTs addressing relevant cancer treatment questions. Within any area of cancer, SMARTs develop DTRs that can guide treatment decisions over the disease history and improve patient outcomes.

摘要

背景

癌症每年影响着全球数百万人。患者需要根据其对先前治疗的反应来制定治疗方案,以对抗癌症和转移。医生根据临床特征提供治疗,且治疗会随时间变化。这些个体化治疗方案的指南被称为动态治疗方案(DTR),其中初始治疗和后续调整取决于对先前治疗的反应、疾病进展以及其他患者特征或行为。为了提供基于证据的DTR,在过去几十年中出现了序贯多重分配随机试验(SMART)。

目的

审视并借鉴过去研究癌症治疗方案的SMART,讨论阻碍SMART在癌症研究中广泛应用的潜在局限性,并描述增加SMART实施以及统计学家与临床医生之间合作的行动方案。

结论

已有针对癌症领域治疗问题的SMART,但新颖性和感知到的复杂性限制了其应用。通过在统计学家和临床医生之间架起桥梁、明确研究目标以及推进方法研究,应该会有更多解决相关癌症治疗问题的SMART出现。在任何癌症领域内,SMART都能制定出可在疾病进程中指导治疗决策并改善患者预后的DTR。