Literature DB >> 30883859

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

Ellen C Caniglia1,2, James M Robins1,3, Lauren E Cain1, Caroline Sabin4, Roger Logan1, Sophie Abgrall5, Michael J Mugavero6, Sonia Hernández-Díaz1, Laurence Meyer7, Remonie Seng7, Daniel R Drozd8, George R Seage Iii1, Fabrice Bonnet9, Fabien Le Marec9, Richard D Moore10, Peter Reiss11, Ard van Sighem11, William C Mathews12, Inma Jarrín13, Belén Alejos13, Steven G Deeks14, Roberto Muga15, Stephen L Boswell16, Elena Ferrer17, Joseph J Eron18, John Gill19, Antonio Pacheco20, Beatriz Grinsztejn20, Sonia Napravnik18, Sophie Jose4, Andrew Phillips4, Amy Justice21, Janet Tate21, Heiner C Bucher22, Matthias Egger23, Hansjakob Furrer24, Jose M Miro25, Jordi Casabona26, Kholoud Porter4, Giota Touloumi27, Heidi Crane8, Dominique Costagliola28, Michael Saag29, Miguel A Hernán1,3,30.   

Abstract

Decisions about when to start or switch a therapy often depend on the frequency with which individuals are monitored or tested. For example, the optimal time to switch antiretroviral therapy depends on the frequency with which HIV-positive individuals have HIV RNA measured. This paper describes an approach to use observational data for the comparison of joint monitoring and treatment strategies and applies the method to a clinically relevant question in HIV research: when can monitoring frequency be decreased and when should individuals switch from a first-line treatment regimen to a new regimen? We outline the target trial that would compare the dynamic strategies of interest and then describe how to emulate it using data from HIV-positive individuals included in the HIV-CAUSAL Collaboration and the Centers for AIDS Research Network of Integrated Clinical Systems. When, as in our example, few individuals follow the dynamic strategies of interest over long periods of follow-up, we describe how to leverage an additional assumption: no direct effect of monitoring on the outcome of interest. We compare our results with and without the "no direct effect" assumption. We found little differences on survival and AIDS-free survival between strategies where monitoring frequency was decreased at a CD4 threshold of 350 cells/μl compared with 500 cells/μl and where treatment was switched at an HIV-RNA threshold of 1000 copies/ml compared with 200 copies/ml. The "no direct effect" assumption resulted in efficiency improvements for the risk difference estimates ranging from an 7- to 53-fold increase in the effective sample size.
© 2019 John Wiley & Sons, Ltd.

Entities:  

Keywords:  causal inference; dynamic regime; joint treatment strategies; marginal structural model; no direct effect

Mesh:

Substances:

Year:  2019        PMID: 30883859      PMCID: PMC6499640          DOI: 10.1002/sim.8120

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


  18 in total

1.  Estimating the causal effect of zidovudine on CD4 count with a marginal structural model for repeated measures.

Authors:  Miguel A Hernán; Babette A Brumback; James M Robins
Journal:  Stat Med       Date:  2002-06-30       Impact factor: 2.373

2.  A structural approach to selection bias.

Authors:  Miguel A Hernán; Sonia Hernández-Díaz; James M Robins
Journal:  Epidemiology       Date:  2004-09       Impact factor: 4.822

3.  British HIV Association guidelines for the routine investigation and monitoring of adult HIV-1-infected individuals 2011.

Authors:  D Asboe; C Aitken; M Boffito; C Booth; P Cane; A Fakoya; A M Geretti; P Kelleher; N Mackie; D Muir; G Murphy; C Orkin; F Post; G Rooney; C Sabin; L Sherr; E Smit; W Tong; A Ustianowski; M Valappil; J Walsh; M Williams; D Yirrell
Journal:  HIV Med       Date:  2012-01       Impact factor: 3.180

4.  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

Review 5.  Comparison of dynamic treatment regimes via inverse probability weighting.

Authors:  Miguel A Hernán; Emilie Lanoy; Dominique Costagliola; James M Robins
Journal:  Basic Clin Pharmacol Toxicol       Date:  2006-03       Impact factor: 4.080

6.  Estimation and extrapolation of optimal treatment and testing strategies.

Authors:  James Robins; Liliana Orellana; Andrea Rotnitzky
Journal:  Stat Med       Date:  2008-10-15       Impact factor: 2.373

7.  The hazards of hazard ratios.

Authors:  Miguel A Hernán
Journal:  Epidemiology       Date:  2010-01       Impact factor: 4.822

8.  The effect of combined antiretroviral therapy on the overall mortality of HIV-infected individuals.

Authors:  Maile Ray; Roger Logan; Jonathan A C Sterne; Sonia Hernández-Díaz; James M Robins; Caroline Sabin; Loveleen Bansi; Ard van Sighem; Frank de Wolf; Dominique Costagliola; Emilie Lanoy; Heiner C Bucher; Viktor von Wyl; Anna Esteve; Jordi Casbona; Julia del Amo; Santiago Moreno; Amy Justice; Joseph Goulet; Sara Lodi; Andrew Phillips; Rémonie Seng; Laurence Meyer; Santiago Pérez-Hoyos; Patricia García de Olalla; Miguel A Hernán
Journal:  AIDS       Date:  2010-01-02       Impact factor: 4.177

9.  Cohort profile: the Centers for AIDS Research Network of Integrated Clinical Systems.

Authors:  Mari M Kitahata; Benigno Rodriguez; Richard Haubrich; Stephen Boswell; W Christopher Mathews; Michael M Lederman; William B Lober; Stephen E Van Rompaey; Heidi M Crane; Richard D Moore; Michael Bertram; James O Kahn; Michael S Saag
Journal:  Int J Epidemiol       Date:  2008-02-08       Impact factor: 7.196

10.  When to initiate combined antiretroviral therapy to reduce mortality and AIDS-defining illness in HIV-infected persons in developed countries: an observational study.

Authors:  Lauren E Cain; Roger Logan; James M Robins; Jonathan A C Sterne; Caroline Sabin; Loveleen Bansi; Amy Justice; Joseph Goulet; Ard van Sighem; Frank de Wolf; Heiner C Bucher; Viktor von Wyl; Anna Esteve; Jordi Casabona; Julia del Amo; Santiago Moreno; Remonie Seng; Laurence Meyer; Santiago Perez-Hoyos; Roberto Muga; Sara Lodi; Emilie Lanoy; Dominique Costagliola; Miguel A Hernan
Journal:  Ann Intern Med       Date:  2011-04-19       Impact factor: 25.391

View more
  4 in total

1.  Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data.

Authors:  Camille Maringe; Sara Benitez Majano; Aimilia Exarchakou; Matthew Smith; Bernard Rachet; Aurélien Belot; Clémence Leyrat
Journal:  Int J Epidemiol       Date:  2020-10-01       Impact factor: 7.196

2.  Estimating the effect of nutritional interventions using observational data: the American Heart Association's 2020 Dietary Goals and mortality.

Authors:  Yu-Han Chiu; Jorge E Chavarro; Barbra A Dickerman; JoAnn E Manson; Kenneth J Mukamal; Kathryn M Rexrode; Eric B Rimm; Miguel A Hernán
Journal:  Am J Clin Nutr       Date:  2021-08-02       Impact factor: 8.472

3.  Withholding Primary Pneumocystis Pneumonia Prophylaxis in Virologically Suppressed Patients With Human Immunodeficiency Virus: An Emulation of a Pragmatic Trial in COHERE.

Authors:  Andrew Atkinson; Marcel Zwahlen; Diana Barger; Antonella d'Arminio Monforte; Stephane De Wit; Jade Ghosn; Enrico Girardi; Veronica Svedhem; Philippe Morlat; Cristina Mussini; Antoni Noguera-Julian; Christoph Stephan; Giota Touloumi; Ole Kirk; Amanda Mocroft; Peter Reiss; Jose M Miro; James R Carpenter; Hansjakob Furrer
Journal:  Clin Infect Dis       Date:  2021-07-15       Impact factor: 9.079

4.  Aim for Clinical Utility, Not Just Predictive Accuracy.

Authors:  Michael C Sachs; Arvid Sjölander; Erin E Gabriel
Journal:  Epidemiology       Date:  2020-05       Impact factor: 4.860

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.