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相关论文: A Comparison of Model-Free Phase I Dose Escalation…

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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

Nowadays, more and more clinical trials choose combinational agents as the intervention to achieve better therapeutic responses. However, dose-finding for combinational agents is much more complicated than single agent as the full order of…

应用统计 · 统计学 2022-08-05 Shu Wang , Ji-Hyun Lee

In parametric Bayesian designs of early phase cancer clinical trials with drug combinations exploring a discrete set of partially ordered doses, several authors claimed that there is no added value in including an interaction term to model…

统计方法学 · 统计学 2022-08-12 Mourad Tighiouart , José L. Jiménez , Marcio A. Diniz , André Rogatko

Phase I-II cancer clinical trial designs are intended to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, it is common to divide the study in two stages: i) a first stage in which dose is…

统计方法学 · 统计学 2022-12-13 José L. Jiménez , Mourad Tighiouart

Phase 1-2 designs provide a methodological advance over phase 1 designs for dose finding by using both clinical response and toxicity. A phase 1-2 trial still may fail to select a truly optimal dose. because early response is not a perfect…

应用统计 · 统计学 2024-04-03 Cheng-Han Yang , Peter F. Thall , Ruitao Lin

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

We consider a modified Ci3+3 (MCi3+3) design for dual-agent dose-finding trials in which both agents are tested on multiple doses. This usually happens when the agents are novel therapies. The MCi3+3 design offers a two-stage or three-stage…

应用统计 · 统计学 2024-09-05 Jiaxin Liu , Shijie Yuan , Qiqi Deng , Yuan Ji

An objective of phase I dose-finding trials is to find the maximum tolerated dose; the dose with a particular risk of toxicity. Frequently, this risk is assessed across the first cycle of therapy. However, in oncology, a course of treatment…

应用统计 · 统计学 2021-05-03 Helen Barnett , Oliver Boix , Dimintris Kontos , Thomas Jaki

For many years Phase I and Phase II clinical trials were conducted separately, but there was a recent shift to combine these Phases. While a variety of Phase~I/II model-based designs for cytotoxic agents were proposed in the literature,…

统计方法学 · 统计学 2018-06-19 Pavel Mozgunov , Thomas Jaki

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

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

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

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

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

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

This paper proposes a novel criterion for the allocation of patients in Phase~I dose-escalation clinical trials aiming to find the maximum tolerated dose (MTD). Conventionally, using a model-based approach the next patient is allocated to…

统计方法学 · 统计学 2018-07-17 Pavel Mozgunov , Thomas Jaki

The traditional more-is-better dose selection paradigm, developed based on cytotoxic chemotherapeutics, is often problematic When applied to the development of novel molecularly targeted agents (e.g., kinase inhibitors, monoclonal…

统计方法学 · 统计学 2022-11-04 Liyun Jiang , Ying Yuan

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

Two useful strategies to speed up drug development are to increase the patient accrual rate and use novel adaptive designs. Unfortunately, these two strategies often conflict when the evaluation of the outcome cannot keep pace with the…

统计方法学 · 统计学 2018-07-24 Ruitao Lin , Ying Yuan

An unprecedented number of new cancer targets are in development, and most are being developed in combination therapies. Early oncology development is strategically challenged in choosing the best combinations to move forward to late stage…

应用统计 · 统计学 2021-01-08 Linda Z. Sun , Cai , Wu , Xiaoyun , Li , Cong Chen , Emmett V. Schmidt
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