Literature DB >> 30351426

Sequential, Multiple-Assignment, Randomized Trials for COMparing Personalized Antibiotic StrategieS (SMART-COMPASS).

Scott R Evans1, Dean Follmann2, Ying Liu3, Thomas Holland4, Sarah B Doernberg5, Nadine Rouphael6, Toshimitsu Hamasaki7, Yunyun Jiang1, Judith J Lok8, Thuy Tien T Tran1, Anthony D Harris9, Vance G Fowler4, Helen Boucher10, Barry N Kreiswirth11, Robert A Bonomo12, David Van Duin13, David L Paterson14, Henry Chambers5.   

Abstract

Patient management is not based on a single decision. Rather, it is dynamic: based on a sequence of decisions, with therapeutic adjustments made over time. Adjustments are personalized: tailored to individual patients as new information becomes available. However, strategies allowing for such adjustments are infrequently studied. Traditional antibiotic trials are often nonpragmatic, comparing drugs for definitive therapy when drug susceptibilities are known. COMparing Personalized Antibiotic StrategieS (COMPASS) is a trial design that compares strategies consistent with clinical practice. Strategies are decision rules that guide empiric and definitive therapy decisions. Sequential, multiple-assignment, randomized (SMART) COMPASS allows evaluation when there are multiple, definitive therapy options. SMART COMPASS is pragmatic, mirroring clinical, antibiotic-treatment decision-making and addressing the most relevant issue for treating patients: identification of the patient-management strategy that optimizes the ultimate patient outcomes. SMART COMPASS is valuable in the setting of antibiotic resistance, when therapeutic adjustments may be necessary due to resistance.
© The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  pragmatic trial; sequential randomization; strategy

Mesh:

Substances:

Year:  2019        PMID: 30351426      PMCID: PMC6522685          DOI: 10.1093/cid/ciy912

Source DB:  PubMed          Journal:  Clin Infect Dis        ISSN: 1058-4838            Impact factor:   9.079


  10 in total

1.  Dynamic treatment regimes: practical design considerations.

Authors:  Philip W Lavori; Ree Dawson
Journal:  Clin Trials       Date:  2004-02       Impact factor: 2.486

2.  Traditional multiplicity adjustment methods in clinical trials.

Authors:  Alex Dmitrienko; Ralph D'Agostino
Journal:  Stat Med       Date:  2013-09-30       Impact factor: 2.373

Review 3.  Advanced multiplicity adjustment methods in clinical trials.

Authors:  Mohamed Alosh; Frank Bretz; Mohammad Huque
Journal:  Stat Med       Date:  2013-09-16       Impact factor: 2.373

Review 4.  Clinical management of Staphylococcus aureus bacteremia: a review.

Authors:  Thomas L Holland; Christopher Arnold; Vance G Fowler
Journal:  JAMA       Date:  2014-10-01       Impact factor: 56.272

5.  The Antibacterial Resistance Leadership Group: Progress Report and Work in Progress.

Authors:  Henry F Chip Chambers; Heather R Cross; Scott R Evans; Barry N Kreiswirth; Vance G Fowler
Journal:  Clin Infect Dis       Date:  2017-03-15       Impact factor: 9.079

6.  Designing a pilot sequential multiple assignment randomized trial for developing an adaptive treatment strategy.

Authors:  Daniel Almirall; Scott N Compton; Meredith Gunlicks-Stoessel; Naihua Duan; Susan A Murphy
Journal:  Stat Med       Date:  2012-03-22       Impact factor: 2.373

7.  Introduction to dynamic treatment strategies and sequential multiple assignment randomization.

Authors:  Philip W Lavori; Ree Dawson
Journal:  Clin Trials       Date:  2014-05-01       Impact factor: 2.486

8.  Desirability of Outcome Ranking (DOOR) and Response Adjusted for Duration of Antibiotic Risk (RADAR).

Authors:  Scott R Evans; Daniel Rubin; Dean Follmann; Gene Pennello; W Charles Huskins; John H Powers; David Schoenfeld; Christy Chuang-Stein; Sara E Cosgrove; Vance G Fowler; Ebbing Lautenbach; Henry F Chambers
Journal:  Clin Infect Dis       Date:  2015-06-25       Impact factor: 9.079

9.  Using Outcomes to Analyze Patients Rather than Patients to Analyze Outcomes: A Step toward Pragmatism in Benefit:risk Evaluation.

Authors:  Scott R Evans; Dean Follmann
Journal:  Stat Biopharm Res       Date:  2016-12-06       Impact factor: 1.452

10.  Characteristics of urinary tract infection pathogens and their in vitro susceptibility to antimicrobial agents in China: data from a multicenter study.

Authors:  Lu-Dong Qiao; Shan Chen; Yong Yang; Kai Zhang; Bo Zheng; Hong-Feng Guo; Bo Yang; Yuan-Jie Niu; Yi Wang; Ben-Kang Shi; Wei-Min Yang; Xiao-Kun Zhao; Xiao-Feng Gao; Ming Chen; Ye Tian
Journal:  BMJ Open       Date:  2013-12-13       Impact factor: 2.692

  10 in total
  1 in total

1.  Optimal allocation to treatments in a sequential multiple assignment randomized trial.

Authors:  Andrea Morciano; Mirjam Moerbeek
Journal:  Stat Methods Med Res       Date:  2021-09-23       Impact factor: 3.021

  1 in total

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