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. 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

Lauren E Cain  1 Michael S Saag  2 Maya Petersen  3 Margaret T May  4 Suzanne M Ingle  4 Roger Logan  1 James M Robins  1   5 Sophie Abgrall  6   7 Bryan E Shepherd  8 Steven G Deeks  9 M John Gill  10 Giota Touloumi  11 Georgia Vourli  11 François Dabis  12 Marie-Anne Vandenhende  12 Peter Reiss  13   14 Ard van Sighem  13 Hasina Samji  15 Robert S Hogg  15   16 Jan Rybniker  17 Caroline A Sabin  18 Sophie Jose  18 Julia Del Amo  19   20 Santiago Moreno  21   22 Benigno Rodríguez  23 Alessandro Cozzi-Lepri  24 Stephen L Boswell  25 Christoph Stephan  26 Santiago Pérez-Hoyos  27 Inma Jarrin  19   20 Jodie L Guest  28   29   30 Antonella D'Arminio Monforte  31 Andrea Antinori  32 Richard Moore  33 Colin Nj Campbell  20   34 Jordi Casabona  20   34   35 Laurence Meyer  36 Rémonie Seng  36 Andrew N Phillips  18 Heiner C Bucher  37 Matthias Egger  38   39 Michael J Mugavero  40 Richard Haubrich  41 Elvin H Geng  42 Ashley Olson  43 Joseph J Eron  44 Sonia Napravnik  44 Mari M Kitahata  45 Stephen E Van Rompaey  45 Ramón Teira  46 Amy C Justice  47   48 Janet P Tate  47   48 Dominique Costagliola  6 Jonathan Ac Sterne  4 Miguel A Hernán  1   5   49 Antiretroviral Therapy Cohort Collaboration, the Centers for AIDS Research Network of Integrated Clinical Systems, and the HIV-CAUSAL Collaboration
Affiliations

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

Lauren E Cain et al. Int J Epidemiol. .

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.

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Figures

Figure 1
Figure 1
Modified CONSORT flow diagram for the mortality analysis in the ART-CC, the CNICS and the HIV-CAUSAL Collaboration, 2002–12.
Figure 2
Figure 2
Survival (left) and AIDS-free survival (right) under tight- and loose-control switching strategies in the ART-CC, the CNICS and the HIV-CAUSAL Collaboration, 2002–12. The curves are standardized by the baseline covariates and inverse-probability weighted by the time-varying covariates listed under Table 4.

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References

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