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An optimal dynamic treatment regime (DTR) is a sequence of decision rules aimed at providing the best course of treatments individualized to patients. While conventional DTR estimation uses longitudinal data, such data can also be…

Methodology · Statistics 2025-02-06 Larry Dong , Eleanor Pullenayegum , Rodolphe Thiébaut , Olli Saarela

The treatment assignment mechanism in a randomized clinical trial can be optimized for statistical efficiency within a specified class of randomization mechanisms. Optimal designs of this type have been characterized in terms of the…

Methodology · Statistics 2025-09-03 Wei Zhang , Zhiwei Zhang , Aiyi Liu

The Regression Discontinuity (RD) design is a quasi-experimental design which emulates a randomised study by exploiting situations where treatment is assigned according to a continuous variable as is common in many drug treatment…

Methodology · Statistics 2016-07-28 Sara Geneletti , Federico Ricciardi , Aidan O'Keeffe , Gianluca Baio

Randomized controlled trials (RCTs) can be used to generate guarantees on treatment effects. However, RCTs often spend unnecessary resources exploring sub-optimal treatments, which can reduce the power of treatment guarantees. To address…

Computers and Society · Computer Science 2024-10-16 Santiago Cortes-Gomez , Naveen Raman , Aarti Singh , Bryan Wilder

This paper presents a robust version of the stratified sampling method when multiple uncertain input models are considered for stochastic simulation. Various variance reduction techniques have demonstrated their superior performance in…

Optimization and Control · Mathematics 2023-06-16 Seung Min Baik , Eunshin Byon , Young Myoung Ko

Recently, methodology was presented to facilitate the incorporation of interim analyses in stepped-wedge (SW) cluster randomised trials (CRTs). Here, we extend this previous discussion. We detail how the stopping boundaries, allocation…

Methodology · Statistics 2018-03-28 Michael Grayling , David Robertson , James Wason , Adrian Mander

Multi-arm bandit experimental designs are increasingly being adopted over standard randomized trials due to their potential to improve outcomes for study participants, enable faster identification of the best-performing options, and/or…

Methodology · Statistics 2025-06-04 Brian M Cho , Aurélien Bibaut , Nathan Kallus

Adaptive experiments are used extensively in online platforms, healthcare and biotechnology, and a variety of other settings. In many of these applications, the main goal is not to precisely estimate a treatment effect, but to demonstrate…

Statistics Theory · Mathematics 2026-03-10 Guido Imbens , Lorenzo Masoero , Alexander Rakhlin , Thomas S. Richardson , Suhas Vijaykumar

Bayesian adaptive designs enable flexible clinical trials by adapting features based on accumulating data. Among these, Bayesian Response-Adaptive Randomization (BRAR) skews patient allocation towards more promising treatments based on…

Methodology · Statistics 2025-12-05 Daniel Kaddaj , Stef Baas , Edwin Y. N. Tang , David S. Robertson , Lukas Pin , Sofía S. Villar

Bayesian adaptive designs have gained popularity in all phases of clinical trials with numerous new developments in the past few decades. During the COVID-19 pandemic, the need to establish evidence for the effectiveness of vaccines,…

Methodology · Statistics 2022-03-08 Shirin Golchi

To use control charts in practice, the in-control state usually has to be estimated. This estimation has a detrimental effect on the performance of control charts, which is often measured for example by the false alarm probability or the…

Methodology · Statistics 2013-07-30 Axel Gandy , Jan Terje Kvaløy

This paper presents novel method for distribution-free robust trajectory optimization and control of discrete-time, nonlinear, and non-Gaussian stochastic systems, with closed-loop guarantees on chance constraint satisfaction. Our framework…

Systems and Control · Electrical Eng. & Systems 2026-03-10 Rihan Aaron D'Silva , Hiroyasu Tsukamoto

We show that, when the double bootstrap is used to improve performance of bootstrap methods for bias correction, techniques based on using a single double-bootstrap sample for each single-bootstrap sample can be particularly effective. In…

Statistics Theory · Mathematics 2015-11-12 Jinyuan Chang , Peter Hall

Response-adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates.…

Applications · Statistics 2017-03-14 Sofia S. Villar , Jack Bowden , James Wason

Interval designs are a class of phase I trial designs for which the decision of dose assignment is determined by comparing the observed toxicity rate at the current dose with a prespecified (toxicity tolerance) interval. If the observed…

Methodology · Statistics 2013-09-20 Suyu Liu , Ying Yuan

The Bootstrap method application in simulation supposes that value of random variables are not generated during the simulation process but extracted from available sample populations. In the case of Hierarchical Bootstrap the function of…

Artificial Intelligence · Computer Science 2013-03-29 A. Andronov , M. Fioshin

Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online decision-making. Consequently, there is an increased desire…

Methodology · Statistics 2023-03-01 Dae Woong Ham , Iavor Bojinov , Michael Lindon , Martin Tingley

Platform trials have become increasingly popular for drug development programs, attracting interest from statisticians, clinicians and regulatory agencies. Many statistical questions related to designing platform trials - such as the impact…

Randomized clinical trials (RCTs) are widely considered the gold standard for evaluating the effectiveness of new treatments or interventions in drug development. Still, they may not be feasible in certain cases, such as with rare diseases…

Methodology · Statistics 2025-08-05 Di Ran , Fanni Zhang , Sima Shahsavari , Kristine Broglio , Alasdair Henderson , Binbing Yu

Inference for functional linear models in the presence of heteroscedastic errors has received insufficient attention given its practical importance; in fact, even a central limit theorem has not been studied in this case. At issue,…

Statistics Theory · Mathematics 2024-05-27 Hyemin Yeon , Xiongtao Dai , Daniel John Nordman