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相关论文: CUI-MET: Clinical Utility Index Dose Optimization …

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The initiation of dose optimization has driven a paradigm shift in oncology clinical trials to determine the optimal biological dose (OBD). Early-phase trials with randomized doses can facilitate additional investigation of the identified…

统计方法学 · 统计学 2025-02-27 Gina DAngelo , Guannan Chen , Di Ran

The FDA's Project Optimus initiative emphasizes patient-centered dose selection in oncology that balances efficacy and safety. We develop a framework for randomized dose optimization studies that uses clinically interpretable utility scores…

应用统计 · 统计学 2026-03-24 Xuemin Gu , Cong Xu , Lei Xu , Ying Yu

The primary objective of phase I oncology studies is to establish the safety profile of a new treatment and determine the maximum tolerated dose (MTD). This is motivated by the development of cytotoxic agents based on the underlying…

应用统计 · 统计学 2023-02-10 Yiding Zhang , Zhixing Xu , Hui Quan , Ji Lin

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

Phase I dose escalation trials in oncology generally aim to find the maximum tolerated dose (MTD). However, with the advent of molecular targeted therapies and antibody drug conjugates, dose limiting toxicities are less frequently observed,…

统计方法学 · 统计学 2025-08-19 Ayon Mukherjee , Jonathan L. Moscovici , Zheng Liu

Optimizing doses for multiple indications is challenging. The pooled approach of finding a single optimal biological dose (OBD) for all indications ignores that dose-response or dose-toxicity curves may differ between indications, resulting…

统计方法学 · 统计学 2024-10-07 Shuqi Wang , Peter F. Thall , Kentaro Takeda , Ying Yuan

The main objective of dose finding trials is to find an optimal dose amongst a candidate set for further research. The trial design in oncology proceeds in stages with a decision as to how to treat the next group of patients made at every…

统计方法学 · 统计学 2025-10-21 Andrew Hall , Duncan Wilson , Stuart Barber , Sarah R Brown

The conventional more-is-better dose selection paradigm, which targets the maximum tolerated dose (MTD), is not suitable for the development of targeted therapies and immunotherapies as the efficacy of these novel therapies may not increase…

统计方法学 · 统计学 2023-08-31 Peng Yang , Daniel Li , Ruitao Lin , Bo Huang , Ying Yuan

Traditional dose selection for oncology registration trials typically employs a one- or two-step single maximum tolerated dose (MTD) approach. However, this approach may not be appropriate for molecularly targeted therapy that tends to have…

统计方法学 · 统计学 2023-09-28 Jason J. Z. Liao , Ekaterine Asatiani , Qingyang Liu , Kevin Hou

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

Chemotherapy is one of the primary modalities of cancer treatment. Chemotherapy drug administration is a complex problem that often requires expensive clinical trials to evaluate potential regimens. One way to alleviate this burden and…

Dose-finding trials for oncology studies are traditionally designed to assess safety in the early stages of drug development. With the rise of molecularly targeted therapies and immuno-oncology compounds, biomarker-driven approaches have…

统计方法学 · 统计学 2025-09-15 Xijin Chen , Pavel Mozgunov , Richard D. Baird , Thomas Jaki

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

Identification of optimal dose combinations in early phase dose-finding trials is challenging, due to the trade-off between precisely estimating the many parameters required to flexibly model the possibly non-monotonic dose-response…

统计方法学 · 统计学 2024-02-13 James Willard , Shirin Golchi , Erica E. M. Moodie , Bruno Boulanger , Bradley P. Carlin

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

Traditionally, the major objective in phase I trials is to identify a working-dose for subsequent studies, whereas the major endpoint in phase II and III trials is treatment efficacy. The dose sought is typically referred to as the maximum…

统计方法学 · 统计学 2016-08-14 Mourad Tighiouart , André Rogatko

We consider a formal statistical design that allows simultaneous enrollment of a main cohort and a backfill cohort of patients in a dose-finding trial. The goal is to accumulate more information at various doses to facilitate dose…

应用统计 · 统计学 2024-04-03 Jiaxin Liu , Shijie Yuan , B. Nebiyou Bekele , Yuan Ji

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

Introduction In analysis of time-to-event outcomes, a mixture cure (MC) model is preferred over a standard survival model when the sample includes individuals who will never experience the event of interest. Motivated by a cohort study of…

应用统计 · 统计学 2026-04-02 Changchang Xu , Laurent Briollais , Irene L Andrulis , Shelley B Bull

Objective: The objective of this study is to develop a machine learning (ML)-based framework for early risk stratification of clinical trials (CTs) according to their likelihood of exhibiting a high rate of dosing errors, using information…

机器学习 · 计算机科学 2026-02-27 Félicien Hêche , Sohrab Ferdowsi , Anthony Yazdani , Sara Sansaloni-Pastor , Douglas Teodoro
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