Missing data is a common problem in HIV research due to non-participation in testing, and selection bias can occur if non-participation in testing is associated with HIV status. For example, longitudinal data suggests that individuals who know or suspect that they are HIV positive are less likely to participate in HIV surveys. Four researchers from Harvard Pop Center, including Mark McGovern, PhD, Till Bärnighausen, MD, Joshua Salomon, PhD, and David Canning, PhD, have published a study in BMC Medical Research Methodology that explores a practical and easy-to-implement method to correct for selection bias in HIV prevalence estimates.