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相关论文: Sample Size Calculations for SMARTs

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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

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…

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

Sequential Multiple-Assignment Randomized Trials (SMARTs) play an increasingly important role in psychological and behavioral health research. This experimental approach enables researchers to answer scientific questions about how to…

统计方法学 · 统计学 2023-06-21 John J. Dziak , Daniel Almirall , Walter Dempsey , Catherine Stanger , Inbal Nahum-Shani

Response-adaptive randomization (RAR) has been studied extensively in conventional, single-stage clinical trials, where it has been shown to yield ethical and statistical benefits, especially in trials with many treatment arms. However, RAR…

统计方法学 · 统计学 2024-01-09 Peter Norwood , Marie Davidian , Eric Laber

The sequential multiple assignment randomized trial (SMART) is the gold standard trial design to generate data for the evaluation of multi-stage treatment regimes. As with conventional (single-stage) randomized clinical trials, interim…

统计方法学 · 统计学 2023-09-13 Cole Manschot , Eric Laber , Marie Davidian

Adaptive interventions (AIs) are increasingly becoming popular in medical and behavioral sciences. An AI is a sequence of individualized intervention options that specify for whom and under what conditions different intervention options…

应用统计 · 统计学 2018-12-18 Palash Ghosh , Inbal Nahum-Shani , Bonnie Spring , Bibhas Chakraborty

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

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…

In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have been growing in popularity as they offer a more individualized approach, and sequential…

统计方法学 · 统计学 2021-08-03 Armando Turchetta , Erica E. M. Moodie , David A. Stephens , Sylvie D. Lambert

A sequential multiple assignment randomized trial (SMART) facilitates comparison of multiple adaptive treatment strategies (ATSs) simultaneously. Previous studies have established a framework to test the homogeneity of multiple ATSs by a…

统计方法学 · 统计学 2022-11-04 Liwen Wu , Junyao Wang , Abdus S. Wahed

One of the main goals of sequential, multiple assignment, randomized trials (SMART) is to find the most efficacious design embedded dynamic treatment regimes. The analysis method known as multiple comparisons with the best (MCB) allows…

统计方法学 · 统计学 2020-08-07 William J. Artman , Ashkan Ertefaie , Kevin G. Lynch , James R. McKay

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

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) have grown in popularity in recent years, and many of their study protocols propose conducting a cost effectiveness analysis of the adaptive strategies embedded within them. The cost…

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

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

There is a growing interest in the implementation of platform trials, which provide the flexibility to incorporate new treatment arms during the trial and the ability to halt treatments early based on lack of benefit or observed…

统计方法学 · 统计学 2023-08-25 Peter Greenstreet , Thomas Jaki , Alun Bedding , Pavel Mozgunov

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

Micro-randomized trials (MRTs) are widely used to assess the marginal and moderated effect of mobile health (mHealth) treatments delivered via mobile devices. In many applications, the mHealth treatments are categorical with multiple levels…

统计方法学 · 统计学 2025-04-23 Jeremy Lin , Tianchen Qian
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