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相关论文: Pharmacokinetic Measurements in Dose Finding Model…

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Phase I oncology trials aim to identify a safe dose - often the maximum tolerated dose (MTD) - for subsequent studies. Conventional designs focus on population-level toxicity modeling, with recent attention on leveraging pharmacokinetic…

应用统计 · 统计学 2026-01-13 Kyong Ju Lee , Yuan Ji

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…

Dose-finding clinical trials in oncology aim to determine the maximum tolerated dose (MTD) of a new drug, generally defined by the proportion of patients with short-term dose-limiting toxicities (DLTs). Model-based approaches for such phase…

统计方法学 · 统计学 2020-12-08 Moreno Ursino , Lucie Biard , Sylvie Chevret

Phase I dose-escalation trials must be guided by a safety model in order to avoid exposing patients to unacceptably high risk of toxicities. Traditionally, these trials are based on one type of schedule. In more recent practice, however,…

统计方法学 · 统计学 2020-08-18 Burak Kürsad Günhan , Sebastian Weber , Tim Friede

Phase I dose-escalation trials constitute the first step in investigating the safety of potentially promising drugs in humans. Conventional methods for phase I dose-escalation trials are based on a single treatment schedule only. More…

应用统计 · 统计学 2021-03-04 Burak Kürsad Günhan , Sebastian Weber , Abdelkader Seroutou , Tim Friede

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

The primary objective of Phase I oncology trials is to assess the safety and tolerability of novel therapeutics. Conventional dose escalation methods identify the maximum tolerated dose (MTD) based on dose-limiting toxicity (DLT). However,…

统计方法学 · 统计学 2024-09-02 Yunlong Yang , Ying Yuan

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

We propose an adaptive design for early phase drug combination cancer trials with the goal of estimating the maximum tolerated dose (MTD). A nonparametric Bayesian model, using beta priors truncated to the set of partially ordered dose…

应用统计 · 统计学 2019-10-22 Zahra S. Razaee , Galen Wien-Cook , Mourad Tighiouart

In Oncology, trials evaluating drug combinations are becoming more common. While combination therapies bring the potential for greater efficacy, they also create unique challenges for ensuring drug safety. In Phase-I dose escalation trials…

应用统计 · 统计学 2023-02-23 Lukas A. Widmer , Andrew Bean , David Ohlssen , Sebastian Weber

Phase I early-phase clinical studies aim at investigating the safety and the underlying dose-toxicity relationship of a drug or combination. While little may still be known about the compound's properties, it is crucial to consider…

统计方法学 · 统计学 2022-09-13 Christian Röver , Moreno Ursino , Tim Friede , Sarah Zohar

Dose-finding studies in oncology often include an up-and-down dose transition rule that assigns a dose to each cohort of patients based on accumulating data on dose-limiting toxicity (DLT) events. In making a dose transition decision, a key…

统计方法学 · 统计学 2025-01-30 Zhiwei Zhang

The primary objective of phase I cancer clinical trials is to evaluate the safety of a new experimental treatment and to find the maximum tolerated dose (MTD). We show that the MTD estimation problem can be regarded as a level set…

机器学习 · 统计学 2025-04-15 Keiichiro Seno , Kota Matsui , Shogo Iwazaki , Yu Inatsu , Shion Takeno , Shigeyuki Matsui

Model-assisted designs have garnered significant attention in recent years due to their high accuracy in identifying the maximum tolerated dose (MTD) and their operational simplicity. To identify the MTD, they employ estimated dose limiting…

应用统计 · 统计学 2025-08-19 Rentaro Wakayama , Tomotaka Momozaki , Shuji Ando

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

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

In the development of new cancer treatment, an essential step is to determine the maximum tolerated dose (MTD) via phase I clinical trials. Generally speaking, phase I trial designs can be classified as either model-based or algorithm-based…

应用统计 · 统计学 2022-03-02 Huaqing Jin , Wenbin Du , Guosheng Yin

An accurately identified maximum tolerated dose (MTD) serves as the cornerstone of successful subsequent phases in oncology drug development. Bayesian logistic regression model (BLRM) is a popular and versatile model-based dose-finding…

统计方法学 · 统计学 2021-05-17 Hongtao Zhang , Alan Y Chiang , Jixian Wang

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

Drug combination trials are increasingly common nowadays in clinical research. However, very few methods have been developed to consider toxicity attributions in the dose escalation process. We are motivated by a trial in which the…

统计方法学 · 统计学 2018-08-23 Jose L. Jimenez , Mourad Tighiouart , Mauro Gasparini
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