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

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, Fabrice Bonnet, Fabien Le Marec, Richard D. Moore, Peter Reiss, Ard van Sighem, William C. Mathews, Inma Jarrín, Belén AlejosSteven 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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2428-2446
Number of pages19
JournalStatistics in Medicine
Issue number13
StatePublished - Jun 15 2019


  • causal inference
  • dynamic regime
  • joint treatment strategies
  • marginal structural model
  • no direct effect

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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