中文
相关论文

相关论文: BOP2-TE: Bayesian Optimal Phase 2 Design for Joint…

200 篇论文

Immunotherapy has transformed cancer treatment, yet its delayed therapeutic effects often lead to non-proportional hazards, rendering many conventional phase II designs underpowered and prone to type I error inflation. To address this…

统计方法学 · 统计学 2025-09-03 Zhongheng Cai , Haitao Pan

The conventional phase II trial design paradigm is to make the go/no-go decision based on the hypothesis testing framework. Statistical significance itself alone, however, may not be sufficient to establish that the drug is clinically…

统计方法学 · 统计学 2021-12-22 Yujie Zhao , Daniel Li , Rong Liu , Ying Yuan

The primary goal of a two-stage Phase I/II trial is to identify the optimal dose for the following large-scale Phase III trial. Recently, Phase I dose-finding designs have shifted from identifying the maximum tolerated dose (MTD) to the…

统计方法学 · 统计学 2025-01-16 Hao Sun , Jerry Li

The Bayesian Optimal Phase II (BOP2) framework is a flexible trial design that can naturally facilitate complex adaptations due to its Bayesian setting. BOP2 uses equal randomisation and equally placed interim analyses in its design, but it…

应用统计 · 统计学 2025-11-19 Connor Fitchett , Ayon Mukherjee , Sofía S. Villar , David S. Robertson

In Phase I/II dose-finding trials, the objective is to find the Optimal Biological Dose (OBD), a dose that is both safe and efficacious that maximises some optimality criterion based on safety and efficacy. This is further complicated when…

应用统计 · 统计学 2022-03-31 Helen Barnett , Oliver Boix , Dimitris Kontos , Thomas Jaki

The use of drug combinations in clinical trials is increasingly common during the last years since a more favorable therapeutic response may be obtained by combining drugs. In phase I clinical trials, most of the existing methodology…

统计方法学 · 统计学 2020-02-17 José L. Jiménez , Sungjin Kim , Mourad Tighiouart

In this article, we propose a phase I-II design in two stages for the combination of molecularly targeted therapies. The design is motivated by a published case study that combines a MEK and a PIK3CA inhibitors; a setting in which higher…

统计方法学 · 统计学 2025-05-21 José L. Jiménez , Mourad Tighiouart

Combination of several anti-cancer treatments has typically been presumed to have enhanced drug activity. Motivated by a real clinical trial, this paper considers phase I-II dose finding designs for dual-agent combinations, where one main…

统计方法学 · 统计学 2023-05-09 José L. Jiménez , Haiyan Zheng

Immunotherapies have revolutionized cancer treatment. Unlike chemotherapies, immune agents often take longer time to show benefit, and the complex and unique mechanism of action of these agents renders the use of multiple endpoints more…

统计方法学 · 统计学 2018-10-02 Ruitao Lin , Robert L Coleman , Ying Yuan

With the development of novel therapies such as molecularly targeted agents and immunotherapy, the maximum tolerated dose paradigm that "more is better" does not necessarily hold anymore. In this context, doses and schedules of novel…

统计方法学 · 统计学 2025-11-24 Anaïs Andrillon , Sandrine Micallef , Moreno Ursino , Pavel Mozgunov , Marie-Karelle Riviere

We consider a dose-optimization design for first-in-human oncology trial that aims to identify a suitable dose for late-phase drug development. The proposed approach, called the Pharmacometrics-Enabled DOse OPtimization (PEDOOP) design,…

应用统计 · 统计学 2024-06-19 Shijie Yuan , Zhanbo Huang , Jiaxin Liu , Yuan Ji

Project Optimus, an initiative by the FDA's Oncology Center of Excellence, seeks to reform the dose-optimization and dose-selection paradigm in oncology. We propose a dose-optimization design that considers plateau efficacy profiles,…

应用统计 · 统计学 2025-04-22 Rebecca B. Silva , Bin Cheng , Shing M. Lee

We propose a new integrated phase I/II trial design to identify the most efficacious dose combination that also satisfies certain safety requirements for drug-combination trials. We first take a Bayesian copula-type model for dose finding…

应用统计 · 统计学 2011-08-09 Ying Yuan , Guosheng Yin

We consider a Bayesian framework based on "probability of decision" for dose-finding trial designs. The proposed PoD-BIN design evaluates the posterior predictive probabilities of up-and-down decisions. In PoD-BIN, multiple grades of…

统计方法学 · 统计学 2021-03-12 Meizi Liu , Yuan Ji , Ji Lin

Dual agent dose-finding trials study the effect of a combination of more than one agent, where the objective is to find the Maximum Tolerated Dose Combination (MTC), the combination of doses of the two agents that is associated with a…

应用统计 · 统计学 2025-02-10 Helen Barnett , Oliver Boix , Dimitris Kontos , Thomas Jaki

Nonlinear regression models addressing both efficacy and toxicity outcomes are increasingly used in dose-finding trials, such as in pharmaceutical drug development. However, research on related experimental design problems for corresponding…

统计方法学 · 统计学 2016-01-06 Holger Dette , Katrin Kettelhake , Kirsten Schorning , Weng Kee Wong , Frank Bretz

Interval designs are a class of phase I trial designs for which the decision of dose assignment is determined by comparing the observed toxicity rate at the current dose with a prespecified (toxicity tolerance) interval. If the observed…

统计方法学 · 统计学 2013-09-20 Suyu Liu , Ying Yuan

Phase I dose-finding trials in oncology seek to find the maximum tolerated dose (MTD) of a drug under a specific schedule. Evaluating drug-schedules aims at improving treatment safety while maintaining efficacy. However, while we can…

Traditional phase I dose finding cancer clinical trial designs aim to determine the maximum tolerated dose (MTD) of the investigational cytotoxic agent based on a single toxicity outcome, assuming a monotone dose-response relationship.…

统计方法学 · 统计学 2024-11-14 Hao Sun , Hsin-Yu Lin , Jieqi Tu , Revathi Ananthakrishnan , Eunhee Kim

In phase I dose escalation studies for dual-agent combinations, at least one drug often has an established monotherapy dose. Consequently, substantial prior clinical safety data often exist for one or more monotherapies, allowing the study…

统计方法学 · 统计学 2026-05-07 Yuxuan Chen , Haiming Zhou , Keiko Nakajima , Philip He
‹ 上一页 1 2 3 10 下一页 ›