Bayesian estimation of HIV acquisition dates for prevention trials

AI Summary

This study introduces a new statistical method to improve how researchers estimate the date a person was infected with HIV in prevention trials. Knowing when someone acquired HIV is important for understanding how well prevention methods such as vaccines or other interventions work. Traditional methods often rely on when a person tests positive and on simple assumptions about when infection occurred, which can be imprecise. The authors developed a Bayesian statistical approach that uses all available data from a person’s HIV tests, including laboratory measurements that reflect how recently the infection happened, to produce more accurate estimates of the likely date of infection. Their method can combine the timing of tests with biological markers that change shortly after infection, providing a clearer picture of when each person likely became infected. The authors tested the approach using data from several HIV prevention studies and showed that it can narrow down the possible infection window more precisely than earlier methods. Better estimates of HIV acquisition dates can improve the analysis of prevention trial results, help researchers understand how different interventions work at different stages of exposure, and allow more accurate comparisons across studies. Overall, this work provides a useful tool for HIV prevention research that could enhance the interpretation of trial outcomes and support the development of more effective prevention strategies.