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Abstract

Over the last three decades, statisticians and mathematical modelers have played a role in advancing the HIV/AIDS research byworking closelywith clinicians, experimentalists, subject matter area researchers and computer scientists. Their contributions include developingmathematical models to study the pathogenesis of the virus and to develop statistical methods for the design and analysis of HIV/AIDS therapeutics and vaccine clinical trials. This issue of Statistical Communication in Infectious Diseases contains papers from a workshop conducted by the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH) on 23 March 2019 in Philadelphia, PA. The title of the workshop was “Statistical Challenges and Opportunities in HIV/AIDS Research in the Era of Getting-to-Zero HIV infections” The workshop was conducted as a pre-conference workshop at the Eastern North American Region (ENAR) of the International Biometric Society (IBS) 2019 conference. The purpose of the workshop was to bring together statisticians and subject matter area researchers working in HIV–AIDS research to: highlight current topics in HIV/AIDS Research with novel statistical challenges, to galvanize methodological research in priority areas in HIV–AIDS research and foster collaborations between statisticians in these priority areas, and to identify opportunities to strengthen collaborations internationally-particularly where input from statisticians may be most needed. The workshop participants were, mathematicians, statisticians, subject matter area researchers and computer scientists. At the end of the workshop, a panel discussion was conducted to encourage interaction between statisticians and subject matter area researchers. The discussion and the oral presentations showed that the advances in research will occur most productively when quantitative methods researchers are working in multidisciplinary teams with subject matter researchers and computer scientists. The eight papers in this issue cover a range of topics in HIV/AIDS research. Below are the summaries of the eight papers. Foulkes et al. demonstrate how leveraging prior information about the biological relationships among variables can increase power for novel discovery. They illustrated that application of a class-level testing strategy provides an alternative to single immune variables by defining hypotheses based on a collection of variables that share a known underlying biological relationship. Brown et al. propose joint modeling, along with the proposed empirical Bayes estimation approach that can provide valid estimation of the per-exposure efficacy of a preventive intervention. The proposed approach is illustrated with data from a simulation study and from the MTN-020/ASPIRE trial. Bing et al. compare empirical and dynamicmodels for HIV viral load rebound after treatment interruption. They apply and compare the two modeling approaches in analysis of data from 346 participants in six AIDS Clinical Trial Group studies. Although based on different sets of assumptions, they demonstrated that both models lead to similar conclusions regarding features of viral rebound process. Kimaina et al. compare machine learning techniques for predicting viral failure. Their goal is to use electronic health record data from a large HIV care program in Kenya to characterize and compare the