scholarly journals Comparison of adaptive treatment strategies based on longitudinal outcomes in sequential multiple assignment randomized trials

2016 ◽  
Vol 36 (3) ◽  
pp. 403-415 ◽  
Author(s):  
Zhiguo Li
Keyword(s):  
Randomized Trials ◽  
Treatment Strategies ◽  
Longitudinal Outcomes ◽  
Adaptive Treatment ◽  
Adaptive Treatment Strategies ◽  
Multiple Assignment

Adaptive treatment strategies for children and adolescents with Obsessive-Compulsive Disorder: A sequential multiple assignment randomized trial

2018 ◽  
Vol 58 ◽  
pp. 42-50 ◽  
Author(s):  
Daniel Fatori ◽  
Carlos Alberto de Bragança Pereira ◽  
Fernando R. Asbahr ◽  
Guaraci Requena ◽  
Pedro G. Alvarenga ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Children And Adolescents ◽  
Obsessive Compulsive Disorder ◽  
Treatment Strategies ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Adaptive Treatment ◽  
Adaptive Treatment Strategies ◽  
Multiple Assignment

scholarly journals Developing Adaptive Treatment Strategies to Address Suicidal Risk in College Students: A Pilot Sequential, Multiple Assignment, Randomized Trial (SMART)

2018 ◽  
Vol 22 (4) ◽  
pp. 644-664 ◽  
Author(s):  
Jacqueline Pistorello ◽  
David A. Jobes ◽  
Scott N. Compton ◽  
Nadia Samad Locey ◽  
Joseph C. Walloch ◽  
...  
Keyword(s):  
College Students ◽  
Randomized Trial ◽  
Treatment Strategies ◽  
Suicidal Risk ◽  
Adaptive Treatment ◽  
Adaptive Treatment Strategies ◽  
Multiple Assignment

Improving the efficiency of estimation in randomized trials of adaptive treatment strategies

2007 ◽  
Vol 4 (4) ◽  
pp. 297-308 ◽  
Author(s):  
Philip W Lavori ◽  
Ree Dawson
Keyword(s):  
Randomized Trials ◽  
Treatment Strategies ◽  
Adaptive Treatment ◽  
Adaptive Treatment Strategies

scholarly journals SMARTAR: an R package for designing and analyzing Sequential Multiple Assignment Randomized Trials

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10559
Author(s):  
Xiaobo Zhong ◽  
Bin Cheng ◽  
Xinru Wang ◽  
Ying Kuen Cheung
Keyword(s):  
Randomized Trial ◽  
Treatment Strategies ◽  
Secondary Data ◽  
Real Data ◽  
R Package ◽  
Adaptive Treatment ◽  
Adaptive Treatment Strategies ◽  
Chronic Disorder ◽  
Multiple Assignment ◽  
Adaptive Treatment Strategy

This article introduces an R package, SMARTAR (Sequential Multiple Assignment Randomized Trial with Adaptive Randomization), by which clinical investigators can design and analyze a sequential multiple assignment randomized trial (SMART) for comparing adaptive treatment strategies. Adaptive treatment strategies are commonly used in clinical practice to personalize healthcare in chronic disorder management. SMART is an efficient clinical design for selecting the best adaptive treatment strategy from a family of candidates. Although some R packages can help in adaptive treatment strategies research, they mainly focus on secondary data analysis for observational studies, instead of clinical trials. SMARTAR is the first R package provides functions that can support clinical investigators and data analysts at every step of the statistical work pipeline in clinical trial practice. In this article, we demonstrate how to use this package, using a real data example.

scholarly journals Design of sequentially randomized trials for testing adaptive treatment strategies

2015 ◽  
Vol 35 (6) ◽  
pp. 840-858 ◽  
Author(s):  
Semhar B. Ogbagaber ◽  
Jordan Karp ◽  
Abdus S. Wahed
Keyword(s):  
Randomized Trials ◽  
Treatment Strategies ◽  
Adaptive Treatment ◽  
Adaptive Treatment Strategies

scholarly journals Sequential causal inference: Application to randomized trials of adaptive treatment strategies

2008 ◽  
Vol 27 (10) ◽  
pp. 1626-1645 ◽  
Author(s):  
Ree Dawson ◽  
Philip W. Lavori
Keyword(s):  
Causal Inference ◽  
Randomized Trials ◽  
Treatment Strategies ◽  
Adaptive Treatment ◽  
Adaptive Treatment Strategies

scholarly journals A global logrank test for adaptive treatment strategies based on observational studies

2013 ◽  
Vol 33 (5) ◽  
pp. 760-771 ◽  
Author(s):  
Zhiguo Li ◽  
Marcia Valenstein ◽  
Paul Pfeiffer ◽  
Dara Ganoczy
Keyword(s):  
Observational Studies ◽  
Treatment Strategies ◽  
Logrank Test ◽  
Adaptive Treatment ◽  
Adaptive Treatment Strategies

scholarly journals Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies

Biometrics ◽  
2020 ◽  
Author(s):  
Noémi Kreif ◽  
Oleg Sofrygin ◽  
Julie A. Schmittdiel ◽  
Alyce S. Adams ◽  
Richard W. Grant ◽  
...  
Keyword(s):  
Treatment Strategies ◽  
Causal Effects ◽  
Adaptive Treatment ◽  
Adaptive Treatment Strategies

Personalizing medicine: a review of adaptive treatment strategies

2014 ◽  
Vol 23 (6) ◽  
pp. 580-585 ◽  
Author(s):  
Michael P. Wallace ◽  
Erica E. M. Moodie
Keyword(s):  
Treatment Strategies ◽  
Adaptive Treatment ◽  
Adaptive Treatment Strategies

scholarly journals Tools for the Precision Medicine Era: How to Develop Highly Personalized Treatment Recommendations From Cohort and Registry Data Using Q-Learning

2017 ◽  
Vol 186 (2) ◽  
pp. 160-172 ◽  
Author(s):  
Elizabeth F Krakow ◽  
Michael Hemmer ◽  
Tao Wang ◽  
Brent Logan ◽  
Mukta Arora ◽  
...  
Keyword(s):  
Graft Versus Host Disease ◽  
Controlled Trial ◽  
Treatment Strategies ◽  
Marrow Transplant ◽  
Host Disease ◽  
Q Learning ◽  
Graft Versus Host ◽  
Adaptive Treatment ◽  
Blood And Marrow Transplant ◽  
Multiple Assignment

Abstract Q-learning is a method of reinforcement learning that employs backwards stagewise estimation to identify sequences of actions that maximize some long-term reward. The method can be applied to sequential multiple-assignment randomized trials to develop personalized adaptive treatment strategies (ATSs)—longitudinal practice guidelines highly tailored to time-varying attributes of individual patients. Sometimes, the basis for choosing which ATSs to include in a sequential multiple-assignment randomized trial (or randomized controlled trial) may be inadequate. Nonrandomized data sources may inform the initial design of ATSs, which could later be prospectively validated. In this paper, we illustrate challenges involved in using nonrandomized data for this purpose with a case study from the Center for International Blood and Marrow Transplant Research registry (1995–2007) aimed at 1) determining whether the sequence of therapeutic classes used in graft-versus-host disease prophylaxis and in refractory graft-versus-host disease is associated with improved survival and 2) identifying donor and patient factors with which to guide individualized immunosuppressant selections over time. We discuss how to communicate the potential benefit derived from following an ATS at the population and subgroup levels and how to evaluate its robustness to modeling assumptions. This worked example may serve as a model for developing ATSs from registries and cohorts in oncology and other fields requiring sequential treatment decisions.

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