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{"title"=>"HIV Treatment as Prevention: Considerations in the Design, Conduct, and Analysis of Cluster Randomized Controlled Trials of Combination HIV Prevention", "type"=>"journal", "authors"=>[{"first_name"=>"Marie-Claude", "last_name"=>"Boily"}, {"first_name"=>"Benoît", "last_name"=>"Mâsse"}, {"first_name"=>"Ramzi", "last_name"=>"Alsallaq"}, {"first_name"=>"Nancy S.", "last_name"=>"Padian"}, {"first_name"=>"Jeffrey W.", "last_name"=>"Eaton"}, {"first_name"=>"Juan F.", "last_name"=>"Vesga"}, {"first_name"=>"Timothy B.", "last_name"=>"Hallett"}], "year"=>2012, "source"=>"PLoS Medicine", "identifiers"=>{"doi"=>"10.1371/journal.pmed.1001250", "issn"=>"1549-1676", "isbn"=>"1549-1676 (Electronic)\\n1549-1277 (Linking)", "pmid"=>"22807657"}, "id"=>"43580cff-fe65-3c71-b79c-791c1f773d95", "abstract"=>"The rigorous evaluation of the impact of combination HIV prevention packages at the population level will be critical for the future of HIV prevention. In this review, we discuss important considerations for the design and interpretation of cluster randomized controlled trials (C-RCTs) of combination prevention interventions. We focus on three large C-RCTs that will start soon and are designed to test the hypothesis that combination prevention packages, including expanded access to antiretroviral therapy, can substantially reduce HIV incidence. Using a general framework to integrate mathematical modelling analysis into the design, conduct, and analysis of C-RCTs will complement traditional statistical analyses and strengthen the evaluation of the interventions. Importantly, even with combination interventions, it may be challenging to substantially reduce HIV incidence over the 2- to 3-y duration of a C-RCT, unless interventions are scaled up rapidly and key populations are reached. Thus, we propose the innovative use of mathematical modelling to conduct interim analyses, when interim HIV incidence data are not available, to allow the ongoing trials to be modified or adapted to reduce the likelihood of inconclusive outcomes. The preplanned, interactive use of mathematical models during C-RCTs will also provide a valuable opportunity to validate and refine model projections.", "link"=>"http://www.mendeley.com/research/hiv-treatment-prevention-considerations-design-conduct-analysis-cluster-randomized-controlled-trials-1", "reader_count"=>11, "reader_count_by_academic_status"=>{"Researcher"=>3, "Student > Ph. D. Student"=>1, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>3, "Student > Ph. D. Student"=>1, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Medicine and Dentistry"=>7, "Psychology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Psychology"=>{"Psychology"=>1}, "Unspecified"=>{"Unspecified"=>3}}, "group_count"=>3}

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• http://doi.org/10.1186/s12879-017-2874-y
• http://doi.org/10.1089/aid.2013.0274
• http://doi.org/10.1002/jia2.25048
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• {"files"=>["https://ndownloader.figshare.com/files/611294"], "description"=>"<p>This approach uses available data from the baseline surveys in each trial cluster and information on process indicators of coverage and intensity available for each cluster within each trial arm gathered after the start of the trial. These data would not include observed HIV incidence. The interim modelling analysis may come to one of four conclusions. (i) The targeted effect size on HIV is likely to be achieved at the end of the study without having to modify the intervention targets/implementation strategy. (ii) The targeted effect size is unlikely to be achieved, and therefore the intervention targets/implementation strategy need to be revised. (iii) The targeted effect size is unlikely to be achieved, even if the intervention targets are improved to their realistic maximum, unless there is a change in the study design (such as an increase in sample size or study duration). (iv) There is little chance of being able to detect an impact at the end of the trial even if the study duration is increased. The number of interim analyses should be predetermined at the start of the trial and take into account trial characteristics, logistical considerations (such as the time and cost required to regularly update programmatic data during the trial and to perform the modelling analyses), and the statistical effect of the interim analysis and proposed changes on the overall type I error.</p>", "links"=>[], "tags"=>["interim"], "article_id"=>281787, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Marie-Claude Boily", "Benoît Mâsse", "Ramzi Alsallaq", "Nancy S. Padian", "Jeffrey W. Eaton", "Juan F. Vesga", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001250.g003", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Logical_flow_of_interim_modelling_analyses_/281787", "title"=>"Logical flow of interim modelling analyses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:41:22"}
• {"files"=>["https://ndownloader.figshare.com/files/611479"], "description"=>"<p>Data as of 15 March 2012.</p>a<p>The design of the intervention and plan of analysis for this trial are still being finalised.</p>b<p>Standard of care is ART for HIV-positive individuals with CD4<350 cells/µl or AIDS.</p>c<p>Standard of care is standard referral to MC and ART according to Tanzania guidelines (this will soon change from CD4<200 cells/µl to CD4<350 cells/µl, initially focusing on HIV-positive people with tuberculosis and pregnant women).</p>d<p>Standard of care is no home-based testing or home-based visit to facilitate linkage to ART. ART given according to country guidelines; standard referral to MC.</p>e<p>Cumulative HIV incidence measured over the trial duration.</p><p>CDC/HSPH, US Centers for Disease Control and Prevention/Harvard School of Public Health; HAART, highly active ART; JHU/USAID, Johns Hopkins University/United States Agency of International Development; PopART (HPTN 071), HIV Prevention Trials Network.</p>", "links"=>[], "tags"=>["characteristics", "randomized", "controlled", "trials", "hiv", "commissioned"], "article_id"=>281964, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Marie-Claude Boily", "Benoît Mâsse", "Ramzi Alsallaq", "Nancy S. Padian", "Jeffrey W. Eaton", "Juan F. Vesga", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001250.t001", "stats"=>{"downloads"=>5, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Main_characteristics_of_cluster_randomized_controlled_trials_for_combination_prevention_of_HIV_transmission_commissioned_by_PEPFAR_/281964", "title"=>"Main characteristics of cluster randomized controlled trials for combination prevention of HIV transmission commissioned by PEPFAR.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 02:42:21"}
• {"files"=>["https://ndownloader.figshare.com/files/611436"], "description"=>"a<p>Stepped-wedge design can still be useful for programme and intermediate outcomes, as changes in these outcomes can occur more rapidly than for HIV incidence or prevalence.</p>", "links"=>[], "tags"=>["considerations", "randomized", "controlled", "trials"], "article_id"=>281927, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Marie-Claude Boily", "Benoît Mâsse", "Ramzi Alsallaq", "Nancy S. Padian", "Jeffrey W. Eaton", "Juan F. Vesga", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001250.t002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_important_considerations_for_the_design_and_interpretation_of_cluster_randomized_controlled_trials_of_combination_interventions_/281927", "title"=>"Summary of important considerations for the design and interpretation of cluster randomized controlled trials (of combination interventions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 02:42:09"}
• {"files"=>["https://ndownloader.figshare.com/files/611369"], "description"=>"<p>Logical flow of modelling stages for the final impact analyses.</p>", "links"=>[], "tags"=>["stages"], "article_id"=>281863, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Marie-Claude Boily", "Benoît Mâsse", "Ramzi Alsallaq", "Nancy S. Padian", "Jeffrey W. Eaton", "Juan F. Vesga", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001250.g004", "stats"=>{"downloads"=>3, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Logical_flow_of_modelling_stages_for_the_final_impact_analyses_/281863", "title"=>"Logical flow of modelling stages for the final impact analyses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:41:46"}
• {"files"=>["https://ndownloader.figshare.com/files/611159"], "description"=>"<p>The model is based on a high-transmission setting under conditions of the current standard of care versus a high-coverage combination intervention (see <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001250#pmed.1001250-Alsallaq1\" target=\"_blank\">[26]</a>). The instantaneous HIV incidence rate ratio in the <i>y</i>-axis is intervention versus control. Impact estimates include an initial 6-mo period of preparation for the study. Assumptions for the combination intervention: 90% of adults in the intervention community are tested in the first year and thereafter every 4 y; those who test positive reduce risk behaviour for 3 y (on average) (25.0%/12.5% of men/women increase condom use; 25%/25% reduce partner acquisition); 70% of uncircumcised men are circumcised in the first year (efficacy = 60%); and all those in need of treatment (CD4 cell count <350 cells/µl) are immediately treated with ART (efficacy = 92%) with an annual dropout rate from treatment of 5%. The efficacy of MC in reducing susceptibility is assumed to be immediate (i.e., the wound healing period is negligible). Viral suppression for infected individuals once on treatment is immediate (i.e., no delay between treatment initiation and viral suppression). Assumptions for the standard of care: 20% of individuals test annually; 12.5%/6.5% of men/women who test positive increase condom use, and 12.5%/12.5% reduce partner acquisition, for one year; HIV-positive individuals are treated if CD4<200 cells/µl (dropout rate of 15%); and 27% of men are circumcised at baseline and 10% more over 4 y since the start of the intervention.</p>", "links"=>[], "tags"=>["components", "linked", "hiv"], "article_id"=>281643, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Marie-Claude Boily", "Benoît Mâsse", "Ramzi Alsallaq", "Nancy S. Padian", "Jeffrey W. Eaton", "Juan F. Vesga", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001250.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_short_term_impact_of_three_intervention_components_linked_to_HIV_testing_in_KwaZulu_Natal_South_Africa_/281643", "title"=>"Predicted short-term impact of three intervention components linked to HIV testing in KwaZulu-Natal, South Africa.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:40:32"}
• {"files"=>["https://ndownloader.figshare.com/files/611230"], "description"=>"<p>Comparison of the instantaneous reduction in HIV incidence measured at one time point with the cumulative incidence rate ratio (IRR) measured over the whole trial duration (i.e., in a cohort that was initiated at the start of the trial) in a simulated population in Zimbabwe <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001250#pmed.1001250-Hallett1\" target=\"_blank\">[16]</a>. The grey dotted line shows the IRR if the full impact were achieved at the start of the intervention rather than after 10 y. The instantaneous IRR is 0.65 compared with only 0.77 for the cumulative IRR at year 10. From <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001250#pmed.1001250-Hallett1\" target=\"_blank\">[16]</a>.</p>", "links"=>[], "tags"=>["measuring", "hiv", "incidence"], "article_id"=>281721, "categories"=>["Biotechnology", "Infectious Diseases"], "users"=>["Marie-Claude Boily", "Benoît Mâsse", "Ramzi Alsallaq", "Nancy S. Padian", "Jeffrey W. Eaton", "Juan F. Vesga", "Timothy B. Hallett"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1001250.g002", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Consequence_of_measuring_HIV_incidence_over_the_whole_trial_duration_/281721", "title"=>"Consequence of measuring HIV incidence over the whole trial duration.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:40:57"}

PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime