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 p opulations. Although screening for HIV provides a direct means for est imating its prevalence, it is complicated by the heterogeneity of avai lable diagnostic tests and the degree to which they can diagnose HIV a ccurately. 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 readil y 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-b ased techniques, we develop an approach to Bayesian analysis of preval ence. This methodology is quite general for a variety of sampling sche mes and sufficiently flexible to accommodate various practical conside rations that arise from HIV screening. We illustrate the methodology w ith real as well as simulated data sets. Further, by utilizing the met hodology, 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.