{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T10:16:03Z","timestamp":1777544163509,"version":"3.51.4"},"reference-count":64,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2017,1,26]],"date-time":"2017-01-26T00:00:00Z","timestamp":1485388800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Clinical Trials"],"published-print":{"date-parts":[[2017,4]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Several cluster-randomized trials are underway to investigate the implementation and effectiveness of a universal test-and-treat strategy on the HIV epidemic in sub-Saharan Africa. We consider nesting studies of pre-exposure prophylaxis within these trials. Pre-exposure prophylaxis is a general strategy where high-risk HIV\u2013 persons take antiretrovirals daily to reduce their risk of infection from exposure to HIV. We address how to target pre-exposure prophylaxis to high-risk groups and how to maximize power to detect the individual and combined effects of universal test-and-treat and pre-exposure prophylaxis strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We simulated 1000 trials, each consisting of 32 villages with 200 individuals per village. At baseline, we randomized the universal test-and-treat strategy. Then, after 3 years of follow-up, we considered four strategies for targeting pre-exposure prophylaxis: (1) all HIV\u2013 individuals who self-identify as high risk, (2) all HIV\u2013 individuals who are identified by their HIV+ partner (serodiscordant couples), (3) highly connected HIV\u2013 individuals, and (4) the HIV\u2013 contacts of a newly diagnosed HIV+ individual (a ring-based strategy). We explored two possible trial designs, and all villages were followed for a total of 7 years. For each village in a trial, we used a stochastic block model to generate bipartite (male\u2013female) networks and simulated an agent-based epidemic process on these networks. We estimated the individual and combined intervention effects with a novel targeted maximum likelihood estimator, which used cross-validation to data-adaptively select from a pre-specified library the candidate estimator that maximized the efficiency of the analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The universal test-and-treat strategy reduced the 3-year cumulative HIV incidence by 4.0% on average. The impact of each pre-exposure prophylaxis strategy on the 4-year cumulative HIV incidence varied by the coverage of the universal test-and-treat strategy with lower coverage resulting in a larger impact of pre-exposure prophylaxis. Offering pre-exposure prophylaxis to serodiscordant couples resulted in the largest reductions in HIV incidence (2% reduction), and the ring-based strategy had little impact (0% reduction). The joint effect was larger than either individual effect with reductions in the 7-year incidence ranging from 4.5% to 8.8%. Targeted maximum likelihood estimation, data-adaptively adjusting for baseline covariates, substantially improved power over the unadjusted analysis, while maintaining nominal confidence interval coverage.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Our simulation study suggests that nesting a pre-exposure prophylaxis study within an ongoing trial can lead to combined intervention effects greater than those of universal test-and-treat alone and can provide information about the efficacy of pre-exposure prophylaxis in the presence of high coverage of treatment for HIV+ persons.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/1740774516679666","type":"journal-article","created":{"date-parts":[[2017,1,28]],"date-time":"2017-01-28T14:03:42Z","timestamp":1485612222000},"page":"201-210","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing test-and-treat trial"],"prefix":"10.1177","volume":"14","author":[{"given":"Laura","family":"Balzer","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Staples","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jukka-Pekka","family":"Onnela","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor","family":"DeGruttola","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,1,26]]},"reference":[{"key":"bibr1-1740774516679666","unstructured":"UNAIDS. AIDS by the numbers 2015, http:\/\/www.unaids.org\/en\/resources\/documents\/2015\/AIDS_by_the_numbers_2015"},{"key":"bibr2-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1105243"},{"key":"bibr3-1740774516679666","unstructured":"World Health Organization. Guideline on when to start antiretrovial therapy and on pre-exposure prophylaxis for HIV, 2015, http:\/\/www.who.int\/hiv\/pub\/guidelines\/earlyrelease-arv\/en\/"},{"key":"bibr4-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1507198"},{"key":"bibr5-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1506816"},{"key":"bibr6-1740774516679666","first-page":"20479","volume":"18","author":"Cohen M","year":"2015","journal-title":"JAIDS"},{"key":"bibr7-1740774516679666","unstructured":"Joint United Nations Programme on HIV\/AIDS (UNAIDS). 90-90-90 an ambitious treatment target to help end the AIDS epidemic, 2014, http:\/\/www.unaids.org\/en\/resources\/documents\/2014\/90-90-90"},{"key":"bibr8-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1016\/j.clp.2010.09.002"},{"key":"bibr9-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(10)62303-3"},{"key":"bibr10-1740774516679666","unstructured":"French National Institute for Health and Medical Research-French National Agency for Research on AIDS and Viral Hepatitis (Inserm-ANRS). Impact of immediate versus South African recommendations guided ART initiation on HIV incidence (TasP). ClinicalTrials.gov,\n2011, https:\/\/clinicaltrials.gov\/ct2\/show\/NCT01509508"},{"key":"bibr11-1740774516679666","unstructured":"Centers for Disease Control and Prevention. Botswana Combination Prevention Project (BCPP). ClinicalTrials.gov,\n2013, http:\/\/clinicaltrials.gov\/show\/NCT01965470"},{"key":"bibr12-1740774516679666","unstructured":"HIV Prevention Trials Network. Population Effects of Antiretroviral Therapy to Reduce HIV Transmission (PopART). ClinicalTrials.gov,\n2013, http:\/\/clinicaltrials.gov\/show\/NCT01900977"},{"key":"bibr13-1740774516679666","unstructured":"University of California, San Francisco. Sustainable East Africa Research in Community Health (SEARCH). ClinicalTrials.gov,\n2013, http:\/\/clinicaltrials.gov\/show\/NCT01864603"},{"key":"bibr14-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1011205"},{"key":"bibr15-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1108524"},{"key":"bibr16-1740774516679666","volume-title":"Proceedings of the 21st international AIDS conference","author":"Baeten J"},{"key":"bibr17-1740774516679666","volume-title":"Proceedings of the 21st international AIDS conference","author":"Gill K"},{"key":"bibr18-1740774516679666","doi-asserted-by":"publisher","DOI":"10.7448\/IAS.18.2.19408"},{"key":"bibr19-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1016\/S2352-3018(15)00251-9"},{"key":"bibr20-1740774516679666","volume-title":"Poceedings of the conference on Retroviruses and Opportunistic Infections (CROI)","author":"Chamie G"},{"key":"bibr21-1740774516679666","unstructured":"US Public Health Service. Preexposure prophylaxis for prevention of HIV infection in the United States\u2014 2014: clincal practice guideline, 2014, https:\/\/www.cdc.gov\/hiv\/pdf\/prepguidelines2014.pdf"},{"key":"bibr22-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(15)61117-5"},{"key":"bibr23-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1097\/QAD.0b013e328285df98"},{"key":"bibr24-1740774516679666","first-page":"181","volume":"67","author":"Dennis A","year":"2014","journal-title":"JAIDS"},{"key":"bibr25-1740774516679666","unstructured":"Joint United Nations Programme on HIV\/AIDS (UNAIDS). The gap report. Geneva: UNAIDS, 2014."},{"key":"bibr26-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1016\/S2352-3018(16)00037-0"},{"key":"bibr27-1740774516679666","volume-title":"In: Poceedings of the conference on Retroviruses and Opportunistic Infections (CROI)","author":"Fidler S"},{"key":"bibr28-1740774516679666","unstructured":"Havlir D, Kamya M, Petersen M, SEARCH study protocol version 7.0. Technical report, Makerere University - University of California, San Francisco (MU-UCSF) Research Collaboration, February 2016."},{"key":"bibr29-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2022"},{"key":"bibr30-1740774516679666","unstructured":"Fisher R. The design of experiments. London: Oliver and Boyd Ltd. 1935."},{"key":"bibr31-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1016\/j.amepre.2014.06.021"},{"key":"bibr32-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.83.016107"},{"key":"bibr33-1740774516679666","doi-asserted-by":"crossref","unstructured":"Anderson R, May R, Anderson B. Infectious diseases of humans: dynamics and control, vol. 28. Hoboken, NJ: Wiley Online Library, 1992.","DOI":"10.1093\/oso\/9780198545996.001.0001"},{"key":"bibr34-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM200003303421303"},{"key":"bibr35-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1097\/QAD.0b013e32832b7dca"},{"key":"bibr36-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0012598"},{"key":"bibr37-1740774516679666","unstructured":"Staples P. Python code for targeted PrEP in test-and-treat trials. Technical report, Harvard T.H. Chan School of Public Health, August 2016, https:\/\/github.com\/ctphoenix\/HIV-PrEP-Simulation"},{"key":"bibr38-1740774516679666","doi-asserted-by":"crossref","unstructured":"Pearl J. Causality: models, reasoning and inference. New York: Cambridge University Press, 2000 (Second edition, 2009).","DOI":"10.1017\/CBO9780511803161"},{"key":"bibr39-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9782-1"},{"key":"bibr40-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1177012031"},{"key":"bibr41-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1177012032"},{"key":"bibr42-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1162\/003465304323023651"},{"key":"bibr43-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3337"},{"key":"bibr44-1740774516679666","doi-asserted-by":"publisher","DOI":"10.3102\/1076998611432176"},{"key":"bibr45-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6965"},{"key":"bibr46-1740774516679666","unstructured":"Fisher R. Statistical methods for research workers. 4th ed. Edinburgh: Oliver and Boyd Ltd. 1932."},{"key":"bibr47-1740774516679666","doi-asserted-by":"publisher","DOI":"10.2307\/2527916"},{"key":"bibr48-1740774516679666","doi-asserted-by":"publisher","DOI":"10.2307\/2530040"},{"key":"bibr49-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3113"},{"key":"bibr50-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3445"},{"key":"bibr51-1740774516679666","doi-asserted-by":"publisher","DOI":"10.2202\/1557-4679.1138"},{"key":"bibr52-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7023"},{"key":"bibr53-1740774516679666","doi-asserted-by":"crossref","unstructured":"Prague M, Staples P, Onnela J, Leveraging contact network information in cluster randomized trials of infectious diseases. In: Infectious disease research: quantitative methods and models in the era of big data, Bethesda, MD, 9\u201310 November 2015.","DOI":"10.1038\/srep17581"},{"key":"bibr54-1740774516679666","volume-title":"Proceedings of the conference on Retroviruses and Opportunistic Infections (CROI)","author":"Zheng W"},{"key":"bibr55-1740774516679666","unstructured":"HPTN 071 (PopART) Protocol Team. HPTN 071 population effects of antiretroviral therapy to reduce HIV transmission (PopART): a cluster-randomized trial of the impact of a combination prevention package on population-level HIV incidence in Zambia and South Africa, 2015, https:\/\/www.hptn.org\/research\/studies\/137"},{"key":"bibr56-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1201\/9781584888178"},{"key":"bibr57-1740774516679666","unstructured":"Balzer L, Zheng W, van der Laan M, Hierarchical targeted maximum likelihood estimation: evaluating the causal effects of cluster based interventions. Technical report, University of California at Berkeley, January 2017."},{"key":"bibr58-1740774516679666","unstructured":"Balzer L. R code for targeted PrEP in test-and-treat trials. Technical report, University of California at Berkeley, May 2016, https:\/\/works.bepress.com\/laura\\text_balzer\/36\/."},{"key":"bibr59-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001245"},{"key":"bibr60-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0075624"},{"key":"bibr61-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0098272"},{"key":"bibr62-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2015.02.002"},{"key":"bibr63-1740774516679666","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6193"},{"key":"bibr64-1740774516679666","author":"Zheng W","year":"2016","journal-title":"Technical Report 354, Division of Biostatistics, University of California at Berkeley"}],"container-title":["Clinical Trials"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1740774516679666","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/1740774516679666","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/1740774516679666","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:29:36Z","timestamp":1777397376000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/1740774516679666"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1,26]]},"references-count":64,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2017,4]]}},"alternative-id":["10.1177\/1740774516679666"],"URL":"https:\/\/doi.org\/10.1177\/1740774516679666","relation":{},"ISSN":["1740-7745","1740-7753"],"issn-type":[{"value":"1740-7745","type":"print"},{"value":"1740-7753","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,1,26]]}}}