• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
TSNP: A two-stage nonparametric phase I/II clinical trial design for immunotherapy.TSNP:一种免疫治疗的两阶段非参数 I/II 期临床试验设计。
Pharm Stat. 2021 Mar;20(2):282-296. doi: 10.1002/pst.2075. Epub 2020 Oct 6.
2
A utility-based Bayesian optimal interval (U-BOIN) phase I/II design to identify the optimal biological dose for targeted and immune therapies.一种基于效用的贝叶斯最优区间(U-BOIN)I/II期设计,用于确定靶向治疗和免疫治疗的最佳生物学剂量。
Stat Med. 2019 Dec 10;38(28):5299-5316. doi: 10.1002/sim.8361. Epub 2019 Oct 17.
3
TITE-BOIN12: A Bayesian phase I/II trial design to find the optimal biological dose with late-onset toxicity and efficacy.TITE-BOIN12:一种贝叶斯 I/II 期临床试验设计,旨在寻找具有迟发性毒性和疗效的最佳生物学剂量。
Stat Med. 2022 May 20;41(11):1918-1931. doi: 10.1002/sim.9337. Epub 2022 Jan 31.
4
SPIRIT: A seamless phase I/II randomized design for immunotherapy trials.SPIRIT:一种用于免疫治疗试验的无缝I/II期随机设计。
Pharm Stat. 2018 Sep;17(5):527-540. doi: 10.1002/pst.1869. Epub 2018 Jun 7.
5
SCI: A Bayesian adaptive phase I/II dose-finding design accounting for semi-competing risks outcomes for immunotherapy trials.SCI:一种贝叶斯自适应 I/II 期剂量探索设计,用于考虑免疫疗法试验中半竞争风险结局。
Pharm Stat. 2022 Sep;21(5):960-973. doi: 10.1002/pst.2209. Epub 2022 Mar 24.
6
Modified isotonic regression based phase I/II clinical trial design identifying optimal biological dose.基于改良等渗回归的 I/II 期临床试验设计,确定最佳生物学剂量。
Contemp Clin Trials. 2023 Apr;127:107139. doi: 10.1016/j.cct.2023.107139. Epub 2023 Mar 2.
7
Bayesian optimization design for dose-finding based on toxicity and efficacy outcomes in phase I/II clinical trials.基于I/II期临床试验中毒性和疗效结果的剂量探索的贝叶斯优化设计。
Pharm Stat. 2021 May;20(3):422-439. doi: 10.1002/pst.2085. Epub 2020 Nov 30.
8
A robust two-stage design identifying the optimal biological dose for phase I/II clinical trials.一种用于确定I/II期临床试验最佳生物学剂量的稳健两阶段设计。
Stat Med. 2017 Jan 15;36(1):27-42. doi: 10.1002/sim.7082. Epub 2016 Aug 18.
9
A robust Bayesian dose-finding design for phase I/II clinical trials.一种用于I/II期临床试验的稳健贝叶斯剂量探索设计。
Biostatistics. 2016 Apr;17(2):249-63. doi: 10.1093/biostatistics/kxv040. Epub 2015 Oct 20.
10
Bayesian adaptive model selection design for optimal biological dose finding in phase I/II clinical trials.贝叶斯自适应模型选择设计在 I/II 期临床试验中的最优生物剂量探索。
Biostatistics. 2023 Apr 14;24(2):277-294. doi: 10.1093/biostatistics/kxab028.

引用本文的文献

1
Ten challenges and opportunities in computational immuno-oncology.计算免疫肿瘤学的十大挑战与机遇。
J Immunother Cancer. 2024 Oct 26;12(10):e009721. doi: 10.1136/jitc-2024-009721.
2
Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies.适用于靶向药物和免疫疗法的最佳生物剂量的适应性 I- II 期临床试验设计。
Clin Trials. 2024 Jun;21(3):298-307. doi: 10.1177/17407745231220661. Epub 2024 Jan 11.
3
SCI: A Bayesian adaptive phase I/II dose-finding design accounting for semi-competing risks outcomes for immunotherapy trials.SCI:一种贝叶斯自适应 I/II 期剂量探索设计,用于考虑免疫疗法试验中半竞争风险结局。
Pharm Stat. 2022 Sep;21(5):960-973. doi: 10.1002/pst.2209. Epub 2022 Mar 24.
4
BIPSE: A biomarker-based phase I/II design for immunotherapy trials with progression-free survival endpoint.BIPSE:一种基于生物标志物的 I/II 期设计,用于无进展生存期终点的免疫疗法试验。
Stat Med. 2022 Mar 30;41(7):1205-1224. doi: 10.1002/sim.9265. Epub 2021 Nov 25.

本文引用的文献

1
Bayesian Phase I/II Biomarker-based Dose Finding for Precision Medicine with Molecularly Targeted Agents.基于贝叶斯的 I/II 期生物标志物指导的剂量探索,用于分子靶向药物的精准医学。
J Am Stat Assoc. 2017;112(518):508-520. doi: 10.1080/01621459.2016.1228534. Epub 2017 Jul 13.
2
A Bayesian Phase I/II Trial Design for Immunotherapy.一种用于免疫疗法的贝叶斯 I/II 期试验设计。
J Am Stat Assoc. 2018;113(523):1016-1027. doi: 10.1080/01621459.2017.1383260. Epub 2018 Jun 28.
3
A utility-based Bayesian optimal interval (U-BOIN) phase I/II design to identify the optimal biological dose for targeted and immune therapies.一种基于效用的贝叶斯最优区间(U-BOIN)I/II期设计,用于确定靶向治疗和免疫治疗的最佳生物学剂量。
Stat Med. 2019 Dec 10;38(28):5299-5316. doi: 10.1002/sim.8361. Epub 2019 Oct 17.
4
De-novo and acquired resistance to immune checkpoint targeting.免疫检查点靶向治疗的获得性和新生耐药性。
Lancet Oncol. 2017 Dec;18(12):e731-e741. doi: 10.1016/S1470-2045(17)30607-1.
5
Robust treatment comparison based on utilities of semi-competing risks in non-small-cell lung cancer.基于非小细胞肺癌半竞争风险效用的稳健治疗比较
J Am Stat Assoc. 2017;112:11-23. doi: 10.1080/01621459.2016.1176926. Epub 2017 May 3.
6
A robust two-stage design identifying the optimal biological dose for phase I/II clinical trials.一种用于确定I/II期临床试验最佳生物学剂量的稳健两阶段设计。
Stat Med. 2017 Jan 15;36(1):27-42. doi: 10.1002/sim.7082. Epub 2016 Aug 18.
7
The future of cancer treatment: immunomodulation, CARs and combination immunotherapy.癌症治疗的未来:免疫调节、嵌合抗原受体(CAR)及联合免疫疗法。
Nat Rev Clin Oncol. 2016 May;13(5):273-90. doi: 10.1038/nrclinonc.2016.25. Epub 2016 Mar 15.
8
A robust Bayesian dose-finding design for phase I/II clinical trials.一种用于I/II期临床试验的稳健贝叶斯剂量探索设计。
Biostatistics. 2016 Apr;17(2):249-63. doi: 10.1093/biostatistics/kxv040. Epub 2015 Oct 20.
9
Cancer immunotherapy and breaking immune tolerance: new approaches to an old challenge.癌症免疫疗法与打破免疫耐受:应对旧挑战的新方法
Cancer Res. 2015 Jan 1;75(1):5-10. doi: 10.1158/0008-5472.CAN-14-2538. Epub 2014 Dec 18.
10
Using Data Augmentation to Facilitate Conduct of Phase I-II Clinical Trials with Delayed Outcomes.利用数据增强促进具有延迟结果的I-II期临床试验的开展。
J Am Stat Assoc. 2014;109(506):525-536. doi: 10.1080/01621459.2014.881740.

TSNP:一种免疫治疗的两阶段非参数 I/II 期临床试验设计。

TSNP: A two-stage nonparametric phase I/II clinical trial design for immunotherapy.

机构信息

Department of Biostatistics, Indiana University, Indianapolis, Indiana, USA.

Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, Indiana, USA.

出版信息

Pharm Stat. 2021 Mar;20(2):282-296. doi: 10.1002/pst.2075. Epub 2020 Oct 6.

DOI:10.1002/pst.2075
PMID:33025762
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9386730/
Abstract

We develop a transparent and efficient two-stage nonparametric (TSNP) phase I/II clinical trial design to identify the optimal biological dose (OBD) of immunotherapy. We propose a nonparametric approach to derive the closed-form estimates of the joint toxicity-efficacy response probabilities under the monotonic increasing constraint for the toxicity outcomes. These estimates are then used to measure the immunotherapy's toxicity-efficacy profiles at each dose and guide the dose finding. The first stage of the design aims to explore the toxicity profile. The second stage aims to find the OBD, which can achieve the optimal therapeutic effect by considering both the toxicity and efficacy outcomes through a utility function. The closed-form estimates and concise dose-finding algorithm make the TSNP design appealing in practice. The simulation results show that the TSNP design yields superior operating characteristics than the existing Bayesian parametric designs. User-friendly computational software is freely available to facilitate the application of the proposed design to real trials. We provide comprehensive illustrations and examples about implementing the proposed design with associated software.

摘要

我们开发了一种透明高效的两阶段非参数(TSNP)I/II 期临床试验设计,以确定免疫治疗的最佳生物学剂量(OBD)。我们提出了一种非参数方法,在毒性结果单调递增的约束下,推导出毒性-疗效联合反应概率的闭式估计。然后,这些估计用于测量每个剂量的免疫治疗的毒性-疗效特征,并指导剂量发现。设计的第一阶段旨在探索毒性特征。第二阶段旨在通过效用函数同时考虑毒性和疗效结果来找到 OBD,从而达到最佳的治疗效果。闭式估计和简洁的剂量发现算法使 TSNP 设计在实践中具有吸引力。模拟结果表明,TSNP 设计的运行特性优于现有的贝叶斯参数设计。我们提供了免费的用户友好型计算软件,以方便将建议的设计应用于真实试验。我们提供了全面的说明和示例,介绍了如何使用相关软件来实现所提出的设计。