SHCS

Swiss HIV Cohort Study

& Swiss Mother and Child HIV Cohort Study

Caniglia et al., Emulating a trial monitoring treatment HIV

12th June, 2019

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

Caniglia et al. from the HIV-Causal collaboration performed a study emulating a clinical trial investigating the question whether monitoring frequency could be lowered.

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?

They 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 their example, few individuals follow the dynamic strategies of interest over long periods of follow-up, they describe how to leverage an additional assumption: no direct effect of monitoring on the outcome of interest. They compare their results with and without the “no direct effect” assumption.

They 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 a 7- to 53-fold increase in the effective sample size.

PubMed

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