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Related papers: A Unified Decision Framework for Phase I Dose-Find…

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Adaptive designs have been proposed for clinical trials in which the nuisance parameters or alternative of interest are unknown or likely to be misspecified before the trial. Whereas most previous works on adaptive designs and mid-course…

Methodology · Statistics 2011-05-18 Jay Bartroff , Tze Leung Lai

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD…

Molecular Networks · Quantitative Biology 2023-09-27 Natalie M. Isenberg , Susan D. Mertins , Byung-Jun Yoon , Kristofer Reyes , Nathan M. Urban

Immunotherapies and targeted therapies have gained popularity due to their promising therapeutic effects across multiple treatment areas. The focus of early phase dose-finding clinical trials has shifted from finding the maximum tolerated…

Methodology · Statistics 2023-12-27 Hao Sun , Jieqi Tu

Many drugs used therapeutically or recreationally induce tolerance: the effect of the substance decreases with repeated use. This phenomenon may reduce the efficacy of the substance unless dosage is increased beyond what is healthy for the…

Dynamical Systems · Mathematics 2024-03-19 Golrokh Nouri , Paul Tupper

The question of selecting the "best" amongst different choices is a common problem in statistics. In drug development, our motivating setting, the question becomes, for example: what is the dose that gives me a pre-specified risk of…

Statistics Theory · Mathematics 2018-03-15 Pavel Mozgunov , Thomas Jaki

Recently, the U.S. Food and Drug Administration (FDA) released draft guidance \citep{FDA2026} signaling a paradigm shift that facilitates the use of Bayesian methodology as the primary analysis and decision framework for drug approval. The…

Methodology · Statistics 2026-03-23 Peng Yang , Li Wang , Ying Yuan

In the drug discovery process, where experiments can be costly and time-consuming, computational models that predict drug-target interactions are valuable tools to accelerate the development of new therapeutic agents. Estimating the…

Machine Learning · Computer Science 2024-07-22 Hannah Rosa Friesacher , Ola Engkvist , Lewis Mervin , Yves Moreau , Adam Arany

Clinical trials in the medical domain are constrained by budgets. The number of patients that can be recruited is therefore limited. When a patient population is heterogeneous, this creates difficulties in learning subgroup specific…

Machine Learning · Statistics 2020-02-25 Hyun-Suk Lee , Cong Shen , James Jordon , Mihaela van der Schaar

Oncology drug development starts with a dose escalation phase to find the maximal tolerable dose (MTD). Dose limiting toxicity (DLT) is the primary endpoint for dose escalation phase. Traditionally, model-based dose escalation trial designs…

Applications · Statistics 2024-04-18 Arnab Kumar Maity , Satrajit Roy Chowdhury , Ray Li , Lada Markovtsova , Roberto Bugarini

Diffusion models have emerged as a leading framework in generative modeling, poised to transform the traditionally slow and costly process of drug discovery. This review provides a systematic comparison of their application in designing two…

Machine Learning · Computer Science 2025-11-27 Yiquan Wang , Yahui Ma , Yuhan Chang , Jiayao Yan , Jialin Zhang , Minnuo Cai , Kai Wei

Hierarchical random effect models are used for different purposes in clinical research and other areas. In general, the main focus is on population parameters related to the expected treatment effects or group differences among all units of…

Applications · Statistics 2021-04-07 Maryna Prus , Norbert Benda , Rainer Schwabe

We propose a frequentist adaptive phase 2 trial design to evaluate the safety and efficacy of three treatment regimens (doses) compared to placebo for four types of helminth (worm) infections. This trial will be carried out in four…

The US Food and Drug Administration (FDA) launched Project Optimus and issued guidance to reform dose-finding and selection trials, shifting the paradigm from identifying the maximum tolerable dose (MTD) to determining the optimal…

Methodology · Statistics 2025-09-16 Kai Chen , Yixuan Zhao , Kentaro Takeda , Ying Yuan

Energy systems optimisation models are a leading tool for informing decisions in the energy transition. However, these models often remain opaque, and results are frequently presented without a clear discussion of their epistemic…

Successful pharmaceutical drug development requires finding correct doses that provide an optimum balance between efficacy and toxicity. Competing responses to dose such as efficacy and toxicity often will increase with dose, and it is…

Applications · Statistics 2024-01-26 A. Lawrence Gould

Recently there has been much work on early phase cancer designs that incorporate both toxicity and efficacy data, called Phase I-II designs because they combine elements of both phases. However, they do not explicitly address the Phase II…

Methodology · Statistics 2014-02-12 Jay Bartroff , Tze Leung Lai , Balasubramanian Narasimhan

We identify three properties of the standard oncology phase I trial design or 3 + 3 design. We show that the standard design implicitly uses isotonic regression to estimate a maximum tolerated dose. We next illustrate the relationship…

Applications · Statistics 2009-06-08 Gregory J. Hather , Howard Mackey

Breakthroughs in cancer biology have defined new research programs emphasizing the development of therapies that target specific pathways in tumor cells. Innovations in clinical trial design have followed with master protocols defined by…

Methodology · Statistics 2020-07-09 Alexander M. Kaizer , Joseph S. Koopmeiners , Nan Chen , Brian P. Hobbs

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…

Methodology · Statistics 2025-05-15 Moka Komaki , Satoru Shinoda , Haiyan Zheng , Kouji Yamamoto

In experimental design, we are given $n$ vectors in $d$ dimensions, and our goal is to select $k\ll n$ of them to perform expensive measurements, e.g., to obtain labels/responses, for a linear regression task. Many statistical criteria have…

Machine Learning · Computer Science 2019-06-11 Michał Dereziński , Feynman Liang , Michael W. Mahoney