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Int J Epidemiol. 2016 Dec 1;45(6):2038-2049. doi: 10.1093/ije/dyv295.

Using observational data to emulate a randomized trial of dynamic treatment-switching strategies: an application to antiretroviral therapy.

Author information

1
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
2
Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
3
Divisions of Biostatistics and Epidemiology, University of California, Berkeley, School of Public Health, Berkeley, CA, USA.
4
School of Social and Community Medicine, University of Bristol, Bristol, UK.
5
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
6
Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'épidémiologie et de Santé Publique (IPLESP UMRS 1136), F75013, Paris, France.
7
Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpital Antoine Béclère, Service de Médecine Interne, Clamart, France.
8
Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
9
Positive Health Program, San Francisco General Hospital, San Francisco, CA, USA.
10
Division of Infectious Diseases, University of Calgary, Calgary, AB, Canada.
11
Department of Hygiene, Epidemiology and Medical Statistics, Athens University Medical School, Athens, Greece.
12
INSERM U897, Centre Inserm Epidémiologie et Biostatistique, Université de Bordeaux, and Bordeaux University Hospital, Department of Internal Medicine, Bordeaux, France.
13
Stichting HIV Monitoring, Amsterdam, Netherlands.
14
Academic Medical Center, Department of Global Health and Division of Infectious Diseases, University of Amsterdam, and Amsterdam Institute for Global Health and Development, Amsterdam, Netherlands.
15
Epidemiology and Population Health Program, BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada.
16
Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada.
17
1st Department of Internal Medicine, University of Cologne, D-50937 Cologne, Germany.
18
University College London, London, UK.
19
National Centre of Epidemiology, Instituto de Salud Carlos III, Madrid, Spain.
20
Consorcio de Investigación Biomédica de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
21
Ramón y Cajal Hospital, IRYCIS, Madrid, Spain.
22
University of Alcalá de Henares, Madrid, Spain.
23
Division of Infectious Disease, Case Western Reserve University, Cleveland, OH, USA.
24
Department of Infection and Population Health; Division of Population Health, University College London, London, UK.
25
Fenway Health, Boston, MA, USA.
26
HIV Center, Department of Infectious Diseases, University Hospital, Frankfurt, Germany.
27
Vall d'Hebron Research Institute, Barcelona, Spain.
28
Rollins School of Public Health at Emory University, Atlanta, GA, USA.
29
Emory University School of Medicine, Atlanta, GA, USA.
30
Atlanta Veterans Affairs Medical Center, Decatur, GA, USA.
31
Clinic of Infectious Diseases and Tropical Medicine, Department of Health Sciences, San Paolo Hospital, University of Milan, Milan, Italy.
32
Istituto Nazionale per le Malattie Infettive Lazzaro Spallanzani IRCCS, Rome, Italy.
33
School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
34
Center for Epidemiological Studies on HIV/AIDS and STI of Catalonia (CEEISCAT), Agència Salut Pública de Catalunya (ASPC), Generalitat de Catalunya, Badalona, 08916 Catalonia, Spain.
35
Department of Paediatrics, Obstetrics, Gynaecology and Preventive Medicine, Universitat Autònoma de Barcelona, Bellaterra, 08193 Catalonia, Spain.
36
Université Paris Sud, INSERM CESP U1018, and AP-HP, Hôpital de Bicêtre, Service de Santé Publique, le Kremlin Bicêtre, France.
37
Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Basel, Switzerland.
38
Centre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
39
University of Bern, Institute for Social and Preventive Medicine, Bern, Switzerland.
40
University of Alabama at Birmingham, Birmingham, AL, USA.
41
University of California San Diego, CA, USA (Currently Gilead Sciences, Foster City, CA, USA).
42
Division of HIV/AIDS, Department of Medicine, University of California, San Francisco, CA, USA.
43
Medical Research Council Clinical Trials Unit, University College London, London, UK.
44
Division of Infectious Diseases, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
45
Department of Medicine, University of Washington, Seattle, WA, USA.
46
Unit of Infectious Diseases, Hospital Sierrallana, Torrelavega, Spain.
47
Yale School of Medicine, New Haven, CT, USA.
48
VA Connecticut Healthcare System, West Haven, CT, USA.
49
Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA.

Abstract

Background:

When a clinical treatment fails or shows suboptimal results, the question of when to switch to another treatment arises. Treatment switching strategies are often dynamic because the time of switching depends on the evolution of an individual's time-varying covariates. Dynamic strategies can be directly compared in randomized trials. For example, HIV-infected individuals receiving antiretroviral therapy could be randomized to switching therapy within 90 days of HIV-1 RNA crossing above a threshold of either 400 copies/ml (tight-control strategy) or 1000 copies/ml (loose-control strategy).

Methods:

We review an approach to emulate a randomized trial of dynamic switching strategies using observational data from the Antiretroviral Therapy Cohort Collaboration, the Centers for AIDS Research Network of Integrated Clinical Systems and the HIV-CAUSAL Collaboration. We estimated the comparative effect of tight-control vs. loose-control strategies on death and AIDS or death via inverse-probability weighting.

Results:

Of 43 803 individuals who initiated an eligible antiretroviral therapy regimen in 2002 or later, 2001 met the baseline inclusion criteria for the mortality analysis and 1641 for the AIDS or death analysis. There were 21 deaths and 33 AIDS or death events in the tight-control group, and 28 deaths and 41 AIDS or death events in the loose-control group. Compared with tight control, the adjusted hazard ratios (95% confidence interval) for loose control were 1.10 (0.73, 1.66) for death, and 1.04 (0.86, 1.27) for AIDS or death.

Conclusions:

Although our effective sample sizes were small and our estimates imprecise, the described methodological approach can serve as an example for future analyses.

KEYWORDS:

HIV; antiretroviral therapy; dynamic strategies; inverse-probability weighting; mortality; observational studies

PMID:
26721599
PMCID:
PMC5841611
DOI:
10.1093/ije/dyv295
[Indexed for MEDLINE]
Free PMC Article

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