Use of agent-based simulations to design and interpret HIV clinical trials

Diego F. Cuadros, Laith J. Abu-Raddad, Susanne F. Awad, Gisela García-Ramos

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In this study, we illustrate the utility of an agent-based simulation to inform a trial design and how this supports outcome interpretation of randomized controlled trials (RCTs). We developed agent-based Monte Carlo models to simulate existing landmark HIV RCTs, such as the Partners in Prevention HSV/HIV Transmission Study. We simulated a variation of this study using valacyclovir therapy as the intervention, and we used a male circumcision RCT based on the Rakai Male Circumcision Trial. Our results indicate that a small fraction (20%) of the simulated Partners in Prevention HSV/HIV Transmission Study realizations rejected the null hypothesis, which was no effect from the intervention. Our results also suggest that an RCT designed to evaluate the effectiveness of a more potent drug regimen for HSV-2 suppression (valacyclovir therapy) is more likely to identify the efficacy of the intervention. For the male circumcision RCT simulation, the greater biological effect of the male circumcision yielded a major fraction (81%) of RCT realizations' that rejects the null hypothesis, which was no effect from the intervention. Our study highlights how agent-based simulations synthesize individual variation in the epidemiological context of the RCT. This methodology will be particularly useful for designing RCTs aimed at evaluating combination prevention interventions in community-based RCTs, wherein an intervention[U+05F3]s effectiveness is challenging to predict.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalComputers in Biology and Medicine
Volume50
DOIs
Publication statusPublished - 1 Jul 2014

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Keywords

  • Agent-based simulation
  • Clinical trial
  • HIV
  • HIV treatment and prevention
  • Mathematical modeling

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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