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相关论文: Using joint models in phase I dose-finding designs…

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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

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

The purpose of a phase I dose-finding clinical trial is to investigate the toxicity profiles of various doses for a new drug and identify the maximum tolerated dose. Over the past three decades, various dose-finding designs have been…

统计方法学 · 统计学 2021-11-25 Yunshan Duan , Shijie Yuan , Yuan Ji , Peter Mueller

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

This study examines the application of Bayesian approach in the context of clinical trials, emphasizing their increasing importance in contemporary biomedical research. While conventional frequentist approach provides a foundational basis…

统计方法学 · 统计学 2026-01-16 Paramahansa Pramanik , Arnab Kumar Maity , Anjan Mandal , Haley Kate Robinson

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

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 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

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

Joint modeling of longitudinal and survival data has become increasingly important in medical research, particularly for understanding disease progression in chronic conditions where both repeated biomarker measurements and time-to-event…

统计方法学 · 统计学 2025-12-30 Nithisha Suryadevara , Vivek Reddy Srigiri

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

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

Advances in data collecting technologies in genomics have significantly increased the need for tools designed to study the genetic basis of many diseases. Effective statistical methods should excel in both prediction accuracy and biomarker…

统计方法学 · 统计学 2025-11-13 Anthony-Alexander Christidis , Stefan Van Aelst , Ruben Zamar

Two-part joint models for a longitudinal semicontinuous biomarker and a terminal event have been recently introduced based on frequentist estimation. The biomarker distribution is decomposed into a probability of positive value and the…

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

Enrichment of predictive models with new biomolecular markers is an important task in high-dimensional omic applications. Increasingly, clinical studies include several sets of such omics markers available for each patient, measuring…

Statistical methodology for the design and analysis of clinical Phase II dose response studies, with related software implementation, are well developed for the case of a normally distributed, homoscedastic response considered for a single…

统计方法学 · 统计学 2014-05-09 José Pinheiro , Björn Bornkamp , Ekkehard Glimm , Frank Bretz

It is crucial to design Phase II cancer clinical trials that balance the efficiency of treatment selection with clinical practicality. Sargent and Goldberg proposed a frequentist design that allow decision-making even when the primary…

统计方法学 · 统计学 2025-05-15 Moka Komaki , Satoru Shinoda , Haiyan Zheng , Kouji Yamamoto

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

Dose optimization in oncology clinical trials has shifted from seeking the maximum tolerated dose to identifying the Optimal Biological Dose (OBD) that balances therapeutic benefits and risks across multiple clinical attributes. Existing…

统计方法学 · 统计学 2025-05-07 Fanni Zhang , Kristine Broglio , Michael Sweeting , Gina D'Angelo
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