Literature DB >> 18646286

Estimation and extrapolation of optimal treatment and testing strategies.

James Robins1, Liliana Orellana, Andrea Rotnitzky.   

Abstract

We review recent developments in the estimation of an optimal treatment strategy or regime from longitudinal data collected in an observational study. We also propose novel methods for using the data obtained from an observational database in one health-care system to determine the optimal treatment regime for biologically similar subjects in a second health-care system when, for cultural, logistical, or financial reasons, the two health-care systems differ (and will continue to differ) in the frequency of, and reasons for, both laboratory tests and physician visits. Finally, we propose a novel method for estimating the optimal timing of expensive and/or painful diagnostic or prognostic tests. Diagnostic or prognostic tests are only useful in so far as they help a physician to determine the optimal dosing strategy, by providing information on both the current health state and the prognosis of a patient because, in contrast to drug therapies, these tests have no direct causal effect on disease progression. Our new method explicitly incorporates this no direct effect restriction.

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Mesh:

Year:  2008        PMID: 18646286     DOI: 10.1002/sim.3301

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  85 in total

1.  When to start treatment? A systematic approach to the comparison of dynamic regimes using observational data.

Authors:  Lauren E Cain; James M Robins; Emilie Lanoy; Roger Logan; Dominique Costagliola; Miguel A Hernán
Journal:  Int J Biostat       Date:  2010       Impact factor: 0.968

2.  Collaborative double robust targeted maximum likelihood estimation.

Authors:  Mark J van der Laan; Susan Gruber
Journal:  Int J Biostat       Date:  2010-05-17       Impact factor: 0.968

3.  Identifying a set that contains the best dynamic treatment regimes.

Authors:  Ashkan Ertefaie; Tianshuang Wu; Kevin G Lynch; Inbal Nahum-Shani
Journal:  Biostatistics       Date:  2015-08-03       Impact factor: 5.899

4.  A sequential multiple assignment randomized trial (SMART) protocol for empirically developing an adaptive preventive intervention for college student drinking reduction.

Authors:  Megan E Patrick; Jeffrey A Boatman; Nicole Morrell; Anna C Wagner; Grace R Lyden; Inbal Nahum-Shani; Cheryl A King; Erin E Bonar; Christine M Lee; Mary E Larimer; David M Vock; Daniel Almirall
Journal:  Contemp Clin Trials       Date:  2020-07-25       Impact factor: 2.226

5.  Diagnosing and responding to violations in the positivity assumption.

Authors:  Maya L Petersen; Kristin E Porter; Susan Gruber; Yue Wang; Mark J van der Laan
Journal:  Stat Methods Med Res       Date:  2010-10-28       Impact factor: 3.021

6.  Targeted maximum likelihood based causal inference: Part II.

Authors:  Mark J van der Laan
Journal:  Int J Biostat       Date:  2010-02-22       Impact factor: 0.968

7.  Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models.

Authors:  Maya Petersen; Joshua Schwab; Susan Gruber; Nello Blaser; Michael Schomaker; Mark van der Laan
Journal:  J Causal Inference       Date:  2014-06-18

8.  Emulating a trial of joint dynamic strategies: An application to monitoring and treatment of HIV-positive individuals.

Authors:  Ellen C Caniglia; James M Robins; Lauren E Cain; Caroline Sabin; Roger Logan; Sophie Abgrall; Michael J Mugavero; Sonia Hernández-Díaz; Laurence Meyer; Remonie Seng; Daniel R Drozd; George R Seage Iii; Fabrice Bonnet; Fabien Le Marec; Richard D Moore; Peter Reiss; Ard van Sighem; William C Mathews; Inma Jarrín; Belén Alejos; Steven G Deeks; Roberto Muga; Stephen L Boswell; Elena Ferrer; Joseph J Eron; John Gill; Antonio Pacheco; Beatriz Grinsztejn; Sonia Napravnik; Sophie Jose; Andrew Phillips; Amy Justice; Janet Tate; Heiner C Bucher; Matthias Egger; Hansjakob Furrer; Jose M Miro; Jordi Casabona; Kholoud Porter; Giota Touloumi; Heidi Crane; Dominique Costagliola; Michael Saag; Miguel A Hernán
Journal:  Stat Med       Date:  2019-03-18       Impact factor: 2.373

9.  Causal inference in epidemiological studies with strong confounding.

Authors:  Kelly L Moore; Romain Neugebauer; Mark J van der Laan; Ira B Tager
Journal:  Stat Med       Date:  2012-02-23       Impact factor: 2.373

10.  Marginal structural Cox models for estimating the association between β-interferon exposure and disease progression in a multiple sclerosis cohort.

Authors:  Mohammad Ehsanul Karim; Paul Gustafson; John Petkau; Yinshan Zhao; Afsaneh Shirani; Elaine Kingwell; Charity Evans; Mia van der Kop; Joel Oger; Helen Tremlett
Journal:  Am J Epidemiol       Date:  2014-06-17       Impact factor: 4.897

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