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. There are various instruments used in clinical practce for researchers trying to measure the adherence. Since each of these instruments has its limitations, we have realized that there is a serious lack of systematic statistical methods, being flexible and reliable to the different data collection procedures. To address the need, in this application we aim to 1) develop consistent statistical measures that can capture different longitudinal adherence patterns; 2) develop statistical methods to infer the true adherence pattern using data from different measuring instruments; 3) develop statistical methods to detect and analyze the association between the adherence patterns and the level of protection from HIV acquisition; 4) develop a statistical/mathematical modeling framework to predict the population impact of adherence improvement on a population's overall HIV incidence reduction. Upon the completion of this project, an innovative, useful and easy?to?implement set of statistical tools and software will be developed for assessing, analyzing and improving adherence in HIV prevention research.
This application aims to develop new statistical tools and software to measure, assess and analyze longitudinal adherence patterns in HIV prevention research. Its overall public health impact lies in applying these new tools to improve our understanding of the health implications of adherence in translational research and clinical care.
