{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T15:45:24Z","timestamp":1765554324625,"version":"3.48.0"},"reference-count":17,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000002","name":"U.S. National Institutes of Health","doi-asserted-by":"crossref","award":["R01AI177078"],"award-info":[{"award-number":["R01AI177078"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"U.S. National Institutes of Health","doi-asserted-by":"crossref","award":["R01AI029168"],"award-info":[{"award-number":["R01AI029168"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_scid-2022-0004_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>The approach of using HIV recency assay to estimate the counterfactual incidence rate is being used as the primary efficacy method in a few ongoing large-scale HIV pre-exposure prophylaxis (PrEP) trials, and the current available approach for the inference is based on the Wald method that leverages the asymptotic distribution of the estimators. One issue with the Wald test is that it does not work well when the number of HIV infections are small in the active arm, and it fails to work when there are zero HIV infections. As future long-acting PrEP products are becoming more efficacious, it is very likely that a small or zero number of infections will be observed in HIV prevention trials, especially for subgroup analyses or interim analyses, hence there is a pressing need to develop inference methods that work under such scenarios.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_scid-2022-0004_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>It is well known that when the sample size is small to moderate, likelihood ratio tests are more reliable than Wald tests in terms of actual error probabilities coming close to matching nominal levels. In this manuscript we derive the likelihood ratio test and the likelihood-based confidence intervals for HIV prevention trials based on recency assays.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_scid-2022-0004_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared with the Wald test, the proposed method works when there are zero infections. Additionally, unlike the Wald test, the p-value from the likelihood ratio test is an increasing function with respect to the number of infections, which is a desirable property as otherwise it will cause confusions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_scid-2022-0004_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>For HIV PrEP trials based on recency assay, the likelihood-based p-value and confidence interval can be preferable to the Wald based inference methods when the number of HIV infections is expected to be small.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/scid-2023-0004","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T03:48:02Z","timestamp":1715140082000},"source":"Crossref","is-referenced-by-count":1,"title":["Likelihood-based inferences for active-arm trial with counterfactual incidence based on recency assay"],"prefix":"10.1515","volume":"16","author":[{"given":"Yongwu","family":"Shao","sequence":"first","affiliation":[{"name":"Gilead Sciences , Foster City , CA , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Gao","sequence":"additional","affiliation":[{"name":"Vaccine and Infectious Disease Division , Fred Hutchinson Cancer Center , Seattle , WA , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2024,5,8]]},"reference":[{"key":"2025121215180900621_j_scid-2023-0004_ref_001","doi-asserted-by":"crossref","unstructured":"Mayer, KH, Agwu, A, Malebranche, D. Barriers to the wider use of pre-exposure prophylaxis in the United States: a narrative review. Adv Ther 2020;37:1778\u2013811. https:\/\/doi.org\/10.1007\/s12325-020-01295-0.","DOI":"10.1007\/s12325-020-01295-0"},{"key":"2025121215180900621_j_scid-2023-0004_ref_002","doi-asserted-by":"crossref","unstructured":"Parkin, N, Gao, F, Grebe, E, Cutrell, A, Das, M, Donnell, D, et al.. Facilitating next- generation pre- exposure prophylaxis clinical trials using HIV recent infection assays: a consensus statement from the forum HIV prevention trial design project. Clin Pharmacol Therapeut 2023;114:29\u201340. https:\/\/doi.org\/10.1002\/cpt.2830.","DOI":"10.1002\/cpt.2830"},{"key":"2025121215180900621_j_scid-2023-0004_ref_003","doi-asserted-by":"crossref","unstructured":"Kassanjee, R, McWalter, TA, Barnighausen, T, Welte, A. A new general biomarker-based incidence estimator. Epidemiology 2012;23:721\u20138. https:\/\/doi.org\/10.1097\/ede.0b013e3182576c07.","DOI":"10.1097\/EDE.0b013e3182576c07"},{"key":"2025121215180900621_j_scid-2023-0004_ref_004","doi-asserted-by":"crossref","unstructured":"Gao, F, Glidden, DV, Hughes, JP, Donnell, DJ. Sample size calculation for active-arm trial with counterfactual incidence based on recency assay. Stat Commun Infect Dis 2021;13:20200009. https:\/\/doi.org\/10.1515\/scid-2020-0009.","DOI":"10.1515\/scid-2020-0009"},{"key":"2025121215180900621_j_scid-2023-0004_ref_005","doi-asserted-by":"crossref","unstructured":"Gao, F, Bannick, M. Statistical considerations for cross-sectional HIV incidence estimation based on recency test. Stat Med 2022;41:1446\u201361. https:\/\/doi.org\/10.1002\/sim.9296.","DOI":"10.1002\/sim.9296"},{"key":"2025121215180900621_j_scid-2023-0004_ref_006","doi-asserted-by":"crossref","unstructured":"Cox, S, Kintu, A, Shao, Y, Kiweewa, FM, Lukyamuzi, Z, Kiweewa, F, et al.. Comparison of recency assays to estimate HIV incidence in the SIENA (estimating HIV incidence among AGYW) study in Uganda. Open Forum Infect Dis 2022;9:ofac492\u20131701. https:\/\/doi.org\/10.1093\/ofid\/ofac492.1701.","DOI":"10.1093\/ofid\/ofac492.1701"},{"key":"2025121215180900621_j_scid-2023-0004_ref_007","unstructured":"Cox, S, Shao, Y, Rees, H, Demirdjian, S, Andreatta, K, Heller, KB, et al.. Evaluation of cross-sectional HIV incidence recency testing in samples from the evidence for contraceptive options and HIV outcomes (ECHO) trial. In: 12th IAS conference on HIV science. Brisbane, Australia; 2023."},{"key":"2025121215180900621_j_scid-2023-0004_ref_008","doi-asserted-by":"crossref","unstructured":"Delany-Moretlwe, S, Hughes, JP, Bock, P, Ouma, SG, Hunidzarira, P, Kalonji, D, et al.. Cabotegravir for the prevention of HIV-1 in women: results from HPTN 084, a phase 3, randomised clinical trial. Lancet 2022;399:1779\u201389. https:\/\/doi.org\/10.1016\/s0140-6736(22)00538-4.","DOI":"10.2139\/ssrn.4066619"},{"key":"2025121215180900621_j_scid-2023-0004_ref_009","doi-asserted-by":"crossref","unstructured":"Kassanjee, R, Pilcher, CD, Busch, MP, Murphy, G, Facente, SN, Keating, SM, et al.. Viral load criteria and threshold optimization to improve HIV incidence assay characteristics - a CEPHIA analysis. AIDS 2016;30:2361\u201371. https:\/\/doi.org\/10.1097\/qad.0000000000001209.","DOI":"10.1097\/QAD.0000000000001209"},{"key":"2025121215180900621_j_scid-2023-0004_ref_010","doi-asserted-by":"crossref","unstructured":"Agresti, A. An introduction to categorical data analysis, 2nd ed. Hoboken, NJ: John Wiley & Sons; 2007.","DOI":"10.1002\/0470114754"},{"key":"2025121215180900621_j_scid-2023-0004_ref_011","doi-asserted-by":"crossref","unstructured":"Cox, DR, Hinkley, DV. Theoretical statistics. Boca Raton, FL: CRC Press; 1979.","DOI":"10.1201\/b14832"},{"key":"2025121215180900621_j_scid-2023-0004_ref_012","unstructured":"Rohatgi, V. Statistical inference. Mineola, NY: Courier Corporation; 2013."},{"key":"2025121215180900621_j_scid-2023-0004_ref_013","doi-asserted-by":"crossref","unstructured":"Bbosa, N, Kaleebu, P, Ssemwanga, D. HIV subtype diversity worldwide. Curr Opin HIV AIDS 2019;14:153\u201360. https:\/\/doi.org\/10.1097\/coh.0000000000000534.","DOI":"10.1097\/COH.0000000000000534"},{"key":"2025121215180900621_j_scid-2023-0004_ref_014","doi-asserted-by":"crossref","unstructured":"Nazziwa, J, Njai, HF, Ndembi, N, Birungi, J, Lyagoba, F, Gershim, A, et al.. HIV type 1 transmitted drug resistance and evidence of transmission clusters among recently infected antiretroviral-naive individuals from Ugandan fishing communities of lake Victoria. AIDS Res Hum Retrovir 2013;29:788\u201395. https:\/\/doi.org\/10.1089\/aid.2012.0123.","DOI":"10.1089\/aid.2012.0123"},{"key":"2025121215180900621_j_scid-2023-0004_ref_015","doi-asserted-by":"crossref","unstructured":"Landovitz, RJ, Donnell, D, Clement, ME, Hanscom, B, Cottle, L, Coelho, L, et al.. Cabotegravir for HIV prevention in cisgender men and transgender women. N Engl J Med 2021;385:595\u2013608. https:\/\/doi.org\/10.1056\/nejmoa2101016.","DOI":"10.1056\/NEJMoa2101016"},{"key":"2025121215180900621_j_scid-2023-0004_ref_016","doi-asserted-by":"crossref","unstructured":"Price, RM, Bonett, DG. Estimating the ratio of two poisson rates. Comput Stat Data Anal 2000;34:345\u201356. https:\/\/doi.org\/10.1016\/s0167-9473(99)00100-0.","DOI":"10.1016\/S0167-9473(99)00100-0"},{"key":"2025121215180900621_j_scid-2023-0004_ref_017","doi-asserted-by":"crossref","unstructured":"Sweeting, MJ, Sutton, AJ, Lambert, PC. What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data. Stat Med 2004;23:1351\u201375. https:\/\/doi.org\/10.1002\/sim.1761.","DOI":"10.1002\/sim.1761"}],"container-title":["Statistical Communications in Infectious Diseases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/scid-2023-0004\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/scid-2023-0004\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T15:18:22Z","timestamp":1765552702000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/scid-2023-0004\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,1]]},"references-count":17,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,8,12]]},"published-print":{"date-parts":[[2024,1,29]]}},"alternative-id":["10.1515\/scid-2023-0004"],"URL":"https:\/\/doi.org\/10.1515\/scid-2023-0004","relation":{},"ISSN":["2194-6310","1948-4690"],"issn-type":[{"type":"print","value":"2194-6310"},{"type":"electronic","value":"1948-4690"}],"subject":[],"published":{"date-parts":[[2024,1,1]]},"article-number":"20230004"}}