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相关论文: Bayesian Set of Best Dynamic Treatment Regimes and…

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Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen (DTR) is a sequence of pre-specified decision rules which can be used to guide the delivery of a sequence of…

Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by…

A dynamic treatment regimen (DTR) is a pre-specified sequence of decision rules which maps baseline or time-varying measurements on an individual to a recommended intervention or set of interventions. Sequential multiple assignment…

统计方法学 · 统计学 2019-10-23 Brook Luers , Min Qian , Inbal Nahum-Shani , Connie Kasari , Daniel Almirall

In a sequential multiple-assignment randomized trial (SMART), a sequence of treatments is given to a patient over multiple stages. In each stage, randomization may be done to allocate patients to different treatment groups. Even though…

统计方法学 · 统计学 2024-01-09 Rik Ghosh , Bibhas Chakraborty , Inbal Nahum-Shani , Megan E. Patrick , Palash Ghosh

Accurate models of clinical actions and their impacts on disease progression are critical for estimating personalized optimal dynamic treatment regimes (DTRs) in medical/health research, especially in managing chronic conditions.…

统计方法学 · 统计学 2021-02-19 William Hua , Hongyuan Mei , Sarah Zohar , Magali Giral , Yanxun Xu

In precision medicine, Dynamic Treatment Regimes (DTRs) are treatment protocols that adapt over time in response to a patient's observed characteristics. A DTR is a set of decision functions that takes an individual patient's information as…

统计方法学 · 统计学 2022-03-17 Cong Jiang , Michael Wallace , Mary Thompson

Cluster-level dynamic treatment regimens can be used to guide sequential, intervention or treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level DTR, the…

统计方法学 · 统计学 2016-07-15 Timothy NeCamp , Amy Kilbourne , Daniel Almirall

Sequential Multiple Assignment Randomized Trials (SMARTs) are considered the gold standard for estimation and evaluation of treatment regimes. SMARTs are typically sized to ensure sufficient power for a simple comparison, e.g., the…

Sequential, multiple assignment, randomized trial (SMART) designs have become increasingly popular in the field of precision medicine by providing a means for comparing sequences of treatments tailored to the individual patient, i.e.,…

应用统计 · 统计学 2018-04-13 William J. Artman , Inbal Nahum-Shani , Tianshuang Wu , James R. McKay , Ashkan Ertefaie

Personalized intervention strategies, in particular those that modify treatment based on a participant's own response, are a core component of precision medicine approaches. Sequential Multiple Assignment Randomized Trials (SMARTs) are…

Sequential multiple assignment randomized trials (SMARTs) are used to construct data-driven optimal intervention strategies for subjects based on their intervention and covariate histories in different branches of health and behavioral…

统计方法学 · 统计学 2022-04-28 Palash Ghosh , Xiaoxi Yan , Bibhas Chakraborty

Dynamic treatment regimes (DTRs) are sequences of decision rules designed to tailor treatment based on patients' treatment history and evolving disease status. Ordinal outcomes frequently serve as primary endpoints in clinical trials and…

统计方法学 · 统计学 2025-03-11 Xinru Wang , Tanujit Chakraborty , Bibhas Chakraborty

Dynamic treatment regimes (DTRs) are sequences of decision rules that recommend treatments based on patients' time-varying clinical conditions. The sequential multiple assignment randomized trial (SMART) is an experimental design that can…

统计方法学 · 统计学 2024-05-14 Xinru Wang , Nina Deliu , Yusuke Narita , Bibhas Chakraborty

To achieve the goal of providing the best possible care to each patient, physicians need to customize treatments for patients with the same diagnosis, especially when treating diseases that can progress further and require additional…

统计方法学 · 统计学 2022-10-25 Xiao Li , Brent R Logan , S M Ferdous Hossain , Erica E M Moodie

Dynamic treatment regimes (DTRs) are sequences of decision rules to guide treatment assignments in response to a patient's evolving, time-varying disease status. Sequential multiple assignment randomized trials (SMARTs) are considered the…

统计方法学 · 统计学 2026-04-29 Xinru Wang , Meghna Bose , Bibhas Chakraborty , Robert Mahar

The optimal prophylaxis, and treatment if the prophylaxis fails, for a disease may be best evaluated using a sequential multiple assignment randomised trial (SMART). A SMART is a multi-stage study that randomises a participant to an initial…

统计方法学 · 统计学 2022-03-25 Robert K. Mahar , Katherine J. Lee , Bibhas Chakraborty , Agus Salim , Julie A. Simpson

The cyclical and heterogeneous nature of many substance use disorders highlights the need to adapt the type or the dose of treatment to accommodate the specific and changing needs of individuals. The Adaptive Treatment for Alcohol and…

统计方法学 · 统计学 2020-05-22 William J. Artman , Ashkan Ertefaie , Kevin G. Lynch , James R. McKay , Brent A. Johnson

There has been significant attention given to developing data-driven methods for tailoring patient care based on individual patient characteristics. Dynamic treatment regimes formalize this through a sequence of decision rules that map…

统计方法学 · 统计学 2022-02-22 Eric J. Rose , Erica E. M. Moodie , Susan Shortreed

The sequential multiple assignment randomized trial (SMART) is the ideal study design for the evaluation of multistage treatment regimes, which comprise sequential decision rules that recommend treatments for a patient at each of a series…

统计方法学 · 统计学 2024-05-15 Anastasios A. Tsiatis , Marie Davidian

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…

统计方法学 · 统计学 2025-02-06 Larry Dong , Eleanor Pullenayegum , Rodolphe Thiébaut , Olli Saarela
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