{"doi":"10.1002/sim.8661","title":"Efficient estimation of human immunodeficiency virus incidence rate using a pooled cross‐sectional cohort study design","abstract":"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.","journal":"Statistics in Medicine","year":2020,"id":116895,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9589,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":545809,"name":"Lesego Gabaitiri","orcid":"0000-0002-7969-2286","position":1,"is_corresponding":false},{"id":546381,"name":"Lucky Mokgatlhe","orcid":null,"position":2,"is_corresponding":false},{"id":425983,"name":"Sikhulile Moyo","orcid":"0000-0003-3821-4592","position":3,"is_corresponding":false},{"id":425985,"name":"Simani Gaseitsiwe","orcid":"0000-0002-7089-3735","position":4,"is_corresponding":false},{"id":438199,"name":"Kathleen E. Wirth","orcid":"0000-0002-7468-851X","position":5,"is_corresponding":false},{"id":271746,"name":"Victor DeGruttola","orcid":"0000-0003-2772-6242","position":6,"is_corresponding":false},{"id":546382,"name":"Eric Tchetgen Tchetgen","orcid":null,"position":7,"is_corresponding":false},{"id":514551,"name":"Kesaobaka Molebatsi","orcid":"0000-0002-9293-4470","position":0,"is_corresponding":true}],"reference_count":22,"raw_metadata":null,"created_at":"2026-07-18T23:13:47.803267Z","pmid":"32875624","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}