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Improving on effective antiretroviral therapy: how good will a cure have to be?
  1. Kenneth A Freedberg1,2,
  2. Paul E Sax3
  1. 1Medical Practice Evaluation Center and Divisions of General Internal Medicine and Infectious Diseases, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
  2. 2Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
  3. 3Division of Infectious Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
  1. Correspondence to Dr Kenneth A Freedberg, MD, MSc, Medical Practice Evaluation Center, Massachusetts General Hospital, 50 Staniford St, Suite 901, Boston, MA, 02114, USA; KFREEDBERG{at}mgh.harvard.edu

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Introduction

Over the past two decades we have seen dramatic improvements in the efficacy, safety and availibity of antiretroviral therapy (ART). In the USA and Europe, life expectancy in people living with HIV disease approaches that of the HIV-uninfected.1 Even in regions hardest hit by the HIV epidemic, effective HIV therapy has reversed more than a decade of HIV-related decreased survival.

Despite these advances in ART, motivations to pursue HIV cure remain strong due to the toxicity, adherence challenges, cost and access to care issues associated with HIV therapy, as well as the persistant stigma associated with having HIV infection. Further and renewed motivation comes from a single case of HIV cure after stem cell transplantation for acute leukaemia.

Given the tremendous improvements in ART, it is important to assess how effective and safe an HIV cure would need to be in order to be a viable option compared to ART. We argue that this should be done prior to the availability of a cure to provide realistic goals for researchers and policy makers.

The value of mathematical and cost-effectiveness models

One methodology that has been increasingly popular over the past decade is the use of mathematical simulation modelling to assess the clinical impact, cost-effectiveness and clinical role of different treatment strategies in HIV disease, as well as in medicine broadly.2 Such models provide a framework and approach for using the best available data at any point in time, and assessing the potential impact of emerging innovations. Further, unlike individual trials focused on a single endpoint, these models can integrate multiple data sources to project beyond the timeline or outcomes of individual studies and project long-term outcomes in many domains, including virologic, immunologic, clinical and cost.3

Modelling uncertainty and ‘what if’ analyses

Mathematical models have particular value in their ability to assess uncertainty in clinical care. Examples include parameters determined in …

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