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A platform trial with a master protocol provides an infrastructure to ethically and efficiently evaluate multiple treatment options in multiple diseases. Given that certain study drugs can enter or exit a platform trial, the randomization…

Methodology · Statistics 2025-07-15 Tianyu Zhan , Jane Zhang , Lei Shu , Yihua Gu

In this paper, a Bayesian approach is developed for simultaneously comparing multiple experimental treatments with a common control treatment in an exploratory clinical trial. The sample size is set to ensure that, at the end of the study,…

Statistics Theory · Mathematics 2019-11-14 John Whitehead , Faye Cleary , Amanda Turner

Suppression of disability progression is an important goal in the treatment of multiple sclerosis (MS). Randomized clinical trials in MS frequently use the time to the first confirmed disability progression (CDP) on the ordinal Expanded…

Applications · Statistics 2021-11-04 Alexandra Būhler

Purpose: The 3+3 design has been shown to be less likely to achieve the objectives of phase I dose-finding trials when compared with more advanced model-based designs. One major criticism of the 3+3 design is that it is based on simple…

Methodology · Statistics 2019-04-30 Meizi Liu , Sue-Jane Wang , Yuan Ji

Matching in causal inference from observational data aims to construct treatment and control groups with similar distributions of covariates, thereby reducing confounding and ensuring an unbiased estimation of treatment effects. This…

Artificial Intelligence · Computer Science 2025-04-15 Sahil Shikalgar , Md. Noor-E-Alam

Post-randomization events, also known as intercurrent events, such as treatment noncompliance and censoring due to a terminal event, are common in clinical trials. Principal stratification is a framework for causal inference in the presence…

Methodology · Statistics 2023-01-19 Bo Liu , Lisa Wruck , Fan Li

In clinical trials, an experimental treatment is sometimes added on to a standard of care or control therapy in multiple treatment phases (e.g., concomitant and maintenance phases) to improve patient outcomes. When the new regimen provides…

Methodology · Statistics 2020-11-19 Sudipta Bhattacharya , Jyotirmoy Dey

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…

Clinical trials are essential to drug development but time-consuming, costly, and prone to failure. Accurate trial outcome prediction based on historical trial data promises better trial investment decisions and more trial success. Existing…

Machine Learning · Computer Science 2023-04-12 Zifeng Wang , Cao Xiao , Jimeng Sun

While it is well known that high levels of prenatal alcohol exposure (PAE) result in significant cognitive deficits in children, the exact nature of the dose response is less well understood. In particular, there is a pressing need to…

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…

Methodology · Statistics 2026-01-16 Paramahansa Pramanik , Arnab Kumar Maity , Anjan Mandal , Haley Kate Robinson

In recent years, precision treatment strategy have gained significant attention in medical research, particularly for patient care. We propose a novel framework for estimating conditional average treatment effects (CATE) in time-to-event…

Methodology · Statistics 2024-07-29 Runjia Li , Victor B. Talisa , Chung-Chou H. Chang

We consider an input-to-response (ItR) system characterized by (1) parameterized input with a known probability distribution and (2) stochastic ItR function with heteroscedastic randomness. Our purpose is to efficiently quantify the extreme…

Methodology · Statistics 2022-05-18 Xianliang Gong , Yulin Pan

Health-related data is noisy and stochastic in implying the true physiological states of patients, limiting information contained in single-moment observations for sequential clinical decision making. We model patient-clinician interactions…

Artificial Intelligence · Computer Science 2019-05-21 Luchen Li , Matthieu Komorowski , Aldo A. Faisal

Most clinical prediction studies are developed from retrospective cohorts and reported as if all patient information were observed at once. In practice, clinicians face a more consequential question: \emph{when is there already enough…

Methodology · Statistics 2026-04-27 Hui-Mean Foo , Yuan-chin Ivan Chang

Multipurpose batch processes become increasingly popular in manufacturing industries since they adapt to low-volume, high-value products and shifting demands. These processes often operate in a dynamic environment, which faces disturbances…

Machine Learning · Computer Science 2025-12-02 Taicheng Zheng , Dan Li , Jie Li

Exploratory cancer drug studies test multiple tumor cell lines against multiple candidate drugs. The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug…

Machine Learning · Statistics 2021-03-23 Wesley Tansey , Christopher Tosh , David M. Blei

Phase Ib/II oncology trials, despite their small sample sizes, aim to provide information for optimal internal company decision-making concerning novel drug development. Hybrid controls (a combination of the current control arm and controls…

This review discusses the current applications, advantages, and limitations of PBPK and PopPK models in radiopharmaceutical therapy (RPT). PBPK models simulate radiopharmaceutical kinetics by integrating prior physiological and drug…

Heterogeneous treatment effects (HTEs) are commonly identified during randomized controlled trials (RCTs). Identifying subgroups of patients with similar treatment effects is of high interest in clinical research to advance precision…

Machine Learning · Computer Science 2022-12-06 Peniel N. Argaw , Elizabeth Healey , Isaac S. Kohane