Efficient estimation of human immunodeficiency virus incidence rate using a pooled cross‐sectional cohort study design.

Development of methods to accurately estimate human immunodeficiency virus (HIV) incidence rate remains a challenge. Ideally, one would follow a random sample of HIV‐negative individuals under a longitudinal study design and identify incident cases as they arise. Such designs can be prohibitively resource intensive and therefore alternative designs may be preferable. We propose such a simple, less resource‐intensive study design and develop a weighted log likelihood approach which simultaneously accounts for selection bias and outcome misclassification error. The design is based on a cross‐sectional survey which queries individuals' time since last HIV‐negative test, validates their test results with formal documentation whenever possible, and tests all persons who do not have documentation of being HIV‐positive. To gain efficiency, we update the weighted log likelihood function with potentially misclassified self‐reports from individuals who could not produce documentation of a prior HIV‐negative test and investigate large sample properties of validated sub‐sample only versus pooled sample estimators through extensive Monte Carlo simulations. We illustrate our method by estimating incidence rate for individuals who tested HIV‐negative within 1.5 and 5 years prior to Botswana Combination Prevention Project enrolment. This article establishes that accurate estimates of HIV incidence rate can be obtained from individuals' history of testing in a cross‐sectional cohort study design by appropriately accounting for selection bias and misclassification error. Moreover, this approach is notably less resource‐intensive compared to longitudinal and laboratory‐based methods. [ABSTRACT FROM AUTHOR]