{"title":"Tomorrow’s HIV Prevention Trials of Vaccines and Antibodies","authors":"D. Follmann","doi":"10.1515/scid-2019-0007","DOIUrl":null,"url":null,"abstract":"Abstract Effective HIV prevention has the potential to change the landscape of HIV prevention trials. Low infection rates will make superiority studies necessarily large while non-inferiority trials will need some evidence that a counterfactual placebo group had a meaningful HIV infection rate in order to provide evidence of effective interventions. This paper explores these challenges in the context of immune related interventions of mAbs and vaccines. We discuss the issue of effect modification in the presence of PrEP, where subjects on PrEP may have less of a benefit of a mAb or (vaccine) than subjects off PrEP. We also discuss different methods of placebo infection rate imputation. We estimate infection risk as a function of mAb level (or vaccine induced immune response) in the mAb (or vaccine) arm and then extrapolate this infection risk to zero mAbs as a proxy for the placebo infection rate. Important aspects are the use of triangulation or multiple methods to impute the placebo infection rate, concern about extrapolation if few mAbs are close to zero, and the use of currently available data with placebo groups to rigorously evaluate the accuracy of imputation methods. We also discuss use of historical controls and some generalizations of the idea of (DMurray, J. 2019. “Regulatory Perspectives for Streamlining HIV Prevention Trials.” Statistical Communications in Infectious Diseases.) to use rectal gonorrhea rates to impute HIV infection rate. Generalizations include regression adjustment to calibrate for potential differences in baseline covariates for ongoing vs historical studies and the use of the gonorrhea, HIV relationship in a contemporaneous observational study. Examples of recent and ongoing trials of malaria chemoprophylaxis and HPV vaccines, where extremely effect prevention methods are available, are provided.","PeriodicalId":74867,"journal":{"name":"Statistical communications in infectious diseases","volume":"107 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical communications in infectious diseases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/scid-2019-0007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract Effective HIV prevention has the potential to change the landscape of HIV prevention trials. Low infection rates will make superiority studies necessarily large while non-inferiority trials will need some evidence that a counterfactual placebo group had a meaningful HIV infection rate in order to provide evidence of effective interventions. This paper explores these challenges in the context of immune related interventions of mAbs and vaccines. We discuss the issue of effect modification in the presence of PrEP, where subjects on PrEP may have less of a benefit of a mAb or (vaccine) than subjects off PrEP. We also discuss different methods of placebo infection rate imputation. We estimate infection risk as a function of mAb level (or vaccine induced immune response) in the mAb (or vaccine) arm and then extrapolate this infection risk to zero mAbs as a proxy for the placebo infection rate. Important aspects are the use of triangulation or multiple methods to impute the placebo infection rate, concern about extrapolation if few mAbs are close to zero, and the use of currently available data with placebo groups to rigorously evaluate the accuracy of imputation methods. We also discuss use of historical controls and some generalizations of the idea of (DMurray, J. 2019. “Regulatory Perspectives for Streamlining HIV Prevention Trials.” Statistical Communications in Infectious Diseases.) to use rectal gonorrhea rates to impute HIV infection rate. Generalizations include regression adjustment to calibrate for potential differences in baseline covariates for ongoing vs historical studies and the use of the gonorrhea, HIV relationship in a contemporaneous observational study. Examples of recent and ongoing trials of malaria chemoprophylaxis and HPV vaccines, where extremely effect prevention methods are available, are provided.
有效的艾滋病预防有可能改变艾滋病预防试验的格局。低感染率将使优势研究必然扩大,而非劣效性试验将需要一些证据,证明反事实安慰剂组具有有意义的艾滋病毒感染率,以便为有效干预提供证据。本文在单克隆抗体和疫苗免疫相关干预的背景下探讨了这些挑战。我们讨论了在PrEP存在的情况下效果改变的问题,其中使用PrEP的受试者可能比不使用PrEP的受试者获得更少的单抗或(疫苗)益处。我们还讨论了安慰剂感染率估算的不同方法。我们估计感染风险是单克隆抗体(或疫苗)组中单克隆抗体水平(或疫苗诱导免疫反应)的函数,然后将这种感染风险推断为零单克隆抗体,作为安慰剂感染率的代理。重要的方面是使用三角测量或多种方法来推算安慰剂感染率,如果少数单克隆抗体接近于零,则需要考虑外推,并使用安慰剂组的现有数据来严格评估推算方法的准确性。我们还讨论了历史控制的使用以及(DMurray, J. 2019)的一些概括。“简化艾滋病毒预防试验的监管视角。”《传染病统计通讯》)使用直肠淋病率推算HIV感染率。概括包括回归调整,以校准正在进行的研究与历史研究的基线协变量的潜在差异,以及在同期观察性研究中使用淋病与艾滋病毒的关系。提供了最近和正在进行的疟疾化学预防和人乳头瘤病毒疫苗试验的例子,其中有非常有效的预防方法。