Bayesian inference on prevalence using a missing-data approach with simulation-based techniques: applications to HIV screening.
Health departments and other health-related authorities seek accurate assessment of the spread of human immunodeficiency virus (HIV) among populations. Although screening for HIV provides a direct means for estimating its prevalence, it is complicated by the heterogeneity of available diagnostic tests and the degree to which they can diagnose HIV accurately. To integrate the limited precision of screening tests with prior results, Bayesian inference becomes a method of choice. Current Bayesian methods, however, have limited applications and do not readily generalize for complicated sampling designs and for modelling needs, particularly those that relate to HIV screening. By utilizing recent developments in the theories of missing-data analysis and simulation-based techniques, we develop an approach to Bayesian analysis of prevalence. This methodology is quite general for a variety of sampling schemes and sufficiently flexible to accommodate various practical considerations that arise from HIV screening. We illustrate the methodology with real as well as simulated data sets. Further, by utilizing the methodology, we performed simulations to demonstrate that pooled testing provides a cost-effective means to improve the precision of estimates of prevalence under the currently limited screening technology.