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Abstract

Background & Objectives: Despite the solid foundation on epidemiological evidence and basic science research, nearly 90% of the randomized controlled trials (RCT) designed to measure the efficacy of interventions on HIV incidence failed to measure a statistically significant efficacy against HIV incidence. Here, we propose the use of computer simulations to control trials as a useful tool to overcome the difficulty of effect size estimation and outcome interpretation derived from HIV intervention RCTs. Methods: We simulated the Partners in Prevention HSV/HIV transmission study recently conducted to test the efficacy of herpes simplex virus type 2 (HSV-2) suppressive therapy by acyclovir in reducing HIV transmission. We also simulated different variations of this trial, and the Rakai male circumcision trial. We developed individual-based Monte-Carlo models parameterized by the data of these RCTs and simulated the RCTs 1000 times. To measure the efficacy of the intervention, we conducted a log-rank survival analysis for each RCT realization and estimated the statistical power as the fraction of realizations that rejected the null hypothesis. Results: Our analyses indicated that the partners in prevention RCT had only 14% likelihood to observe a statistically significant efficacy for the intervention. In contrast, a different and more potent regimen for HSV-2 suppression had 87% chance of observing a statistically significant efficacy. For the Rakai male circumcision trial simulation, 94% of the RCT realizations showed statistically significant efficacy for the intervention. Conclusions: The simulations indicate that several unexpected odds have colluded to undermine the statistical power of the partners in prevention study, and therefore it would be premature to discredit the concept of acyclovir therapy for HIV prevention based on the outcome of the partners in prevention trial. Our study highlights how in silico simulations of RCTs can provide powerful tools for optimizing the design of RCTs and predicting their outcome. Observational and biological evidence summarized in computer simulations could help us on the estimation of effect size ranges and a better understanding of the system, which might provide a powerful tool to enhance the success of any HIV intervention RCT.

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/content/papers/10.5339/qfarf.2012.BMP58
2012-10-01
2020-01-26
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2012.BMP58
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