Recent advances in biomedical prevention intervention, e.g., Treatment as Prevention (TasP) and Pre-Exposure Prophylaxis (PrEP), have changed how HIV prevention is conceptualized and implemented, while effective HIV vaccine is yet to be developed. For a biological HIV prevention regimen, mostly likely in oral medicine, to be effective, users’ adherence to the prescribed regimen is critical. Imperfect adherence reduces the regimen’s effectiveness, and also makes it difficult to assess the regimen’s efficacy in clinical trial settings. If a regimen is shown to be effective to prevent new HIV infections, it is important to understand how the level of protection is associated with the level of adherence and who tend to adhere to the regimen, as the users’ adherence will ultimately determine the regimen’s population impact on the overall reduction of HIV infection.
In fact, for the past decade, we have led or collaborated on the design and analysis for several important HIV/AIDS prevention trials, including the studies, such as the HPTN 052 Study of TasP among sero-discordant couples, the HPTN 069 Study of PrEP using Maraviroc (MVC), Emtricitabine (FTC) and/or Tenofovir disoproxil fumarate (TDF) among at-risk MSM and women, and the Partners PrEP Study of oral TDF and TDF/FTC among the HIV- partners of sero-discordant couples. We have noted that, imperfect adherence has always been challenging to study design, conduct and monitoring, and interpretation of analysis results, due to the fact that there is no easy-to-implement and cost-effective gold standard for measuring adherence of oral medicine, mostly antiretroviral (ARV) drugs, particularly in their long-term use for HIV prevention.
There are various methods used in clinical practice for researchers trying to measure the adherence, e.g., by collecting data from pharmacy pill count, self-report, electronic drug monitoring, and/or drug detection using plasma samples. Since each of these methods has its limitations, when analyzing the adherence data collected by these methods, we have realized that there is a serious lack of proper statistical methods, subject to different data collection procedures, particularly to 1) define a consistent measure that can capture the longitudinal patterns of adherence, 2) use the adherence data collected by various measuring instruments to triangulate the true pattern of adherence, 3) investigate how the level of protection from HIV infection is associated with the adherence, and 4) predict the population impact of adherence in reducing the overall HIV infections.
To address these particular methodological challenges, we specifically aim to:
Aim 1. Develop consistent statistical measures that can capture different longitudinal adherence patterns.
Aim 2. Develop statistical methods to infer the true adherence pattern using data from different measuring instruments.
Aim 3. Develop statistical methods to detect and analyze the association between the adherence patterns and the level of protection from HIV acquisition.
Aim 4. Develop a statistical/mathematical modeling framework to predict the population impact of adherence improvement on a population’s overall HIV incidence reduction.