Using simulated infectious disease outbreaks to inform site selection and sample size for individually randomized vaccine trials during an ongoing epidemic.
Using simulated infectious disease outbreaks to inform site selection and sample size for individually randomized vaccine trials during an ongoing epidemic.
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DOI：
10.1177/17407745211028898
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发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
Dean NE
中科院分区：
文献类型：
--
作者：
Madewell ZJ;Pastore Y Piontti A;Zhang Q;Burton N;Yang Y;Longini IM;Halloran ME;Vespignani A;Dean NE
鉴于传染病爆发的流行病学不确定，如寨卡病毒等虫媒病毒，需要新的策略来使疫苗有效性试验更加稳健。空间解析的数学和统计模型可以帮助研究人员确定未来传播风险最高的部位，并优先考虑这些部位以纳入试验。模型还可以描述一个地点是否会发生传播的不确定性，以及附近或相连的地点如何产生相关结果。需要一个结构来指导试验如何使用模型来解决关键的设计问题，包括如何确定研究中心的优先级、研究中心的最佳数量以及如何在研究中心之间分配参与者。我们使用2015-2017年寨卡疫情期间寨卡疫苗试验计划的激励性例子来说明模型的附加值。我们使用了一个随机的、空间分辨的传播模型（全球流行病和流动性模型）来模拟美洲100个高风险地点的流行病和地点一级的发病率。我们考虑了几种策略来优先考虑研究中心（流行病中的平均研究中心水平感染发生率、中位数发生率、超过1%发生率的概率）、选择研究中心数量和在研究中心之间分配样本量（相等的入组人数、与平均发生率成比例、与排名成比例）。为了评估每个设计，我们随机模拟试验，在每个假设的流行病，从现场水平的发病率数据绘制观察到的情况。当限制总体试验规模时，最佳临床试验机构数量代表了优先考虑最高风险临床试验机构和拥有足够的临床试验机构以减少观察到太少终点的机会之间的平衡。尽管有必要增加样本量以达到预期功效，但无论目标事件数量如何，最佳研究中心数量大致保持不变。虽然不同的排名策略返回不同的网站订单，他们表现出类似的审判权力。研究者可以根据预测的发病率按比例分配参与者，而不是从每个研究中心平等地招募参与者，尽管这在我们的示例中没有提供优势，因为顶级研究中心具有相似的风险特征。来自相同地理区域的站点可能具有类似的结果，因此站点的最佳组合可能在地理上分散，即使这些站点不是排名最高的站点。数学和统计模型可以通过捕捉未来传播的不确定性和相关性来帮助设计成功的疫苗接种试验。虽然有许多因素会影响研究中心的选择，例如后勤可行性，但模型可以帮助研究人员优化研究中心的选择以及参与研究中心的数量和规模。虽然我们的研究集中在一种新出现的虫媒病毒的试验设计上，但对于任何传染病，都可以采用类似的方法，并为特定疾病提供适当的模型。
Novel strategies are needed to make vaccine efficacy trials more robust given uncertain epidemiology of infectious disease outbreaks, such as arboviruses like Zika. Spatially resolved mathematical and statistical models can help investigators identify sites at highest risk of future transmission and prioritize these for inclusion in trials. Models can also characterize uncertainty in whether transmission will occur at a site, and how nearby or connected sites may have correlated outcomes. A structure is needed for how trials can use models to address key design questions, including how to prioritize sites, the optimal number of sites, and how to allocate participants across sites. We illustrate the added value of models using the motivating example of Zika vaccine trial planning during the 2015–2017 Zika epidemic. We used a stochastic, spatially resolved, transmission model (the Global Epidemic and Mobility model) to simulate epidemics and site-level incidence at 100 high-risk sites in the Americas. We considered several strategies for prioritizing sites (average site-level incidence of infection across epidemics, median incidence, probability of exceeding 1% incidence), selecting the number of sites, and allocating sample size across sites (equal enrollment, proportional to average incidence, proportional to rank). To evaluate each design, we stochastically simulated trials in each hypothetical epidemic by drawing observed cases from site-level incidence data. When constraining overall trial size, the optimal number of sites represents a balance between prioritizing highest-risk sites and having enough sites to reduce the chance of observing too few endpoints. The optimal number of sites remained roughly constant regardless of the targeted number of events, although it is necessary to increase the sample size to achieve the desired power. Though different ranking strategies returned different site orders, they performed similarly with respect to trial power. Instead of enrolling participants equally from each site, investigators can allocate participants proportional to projected incidence, though this did not provide an advantage in our example because the top sites had similar risk profiles. Sites from the same geographic region may have similar outcomes, so optimal combinations of sites may be geographically dispersed, even when these are not the highest ranked sites. Mathematical and statistical models may assist in designing successful vaccination trials by capturing uncertainty and correlation in future transmission. Although many factors affect site selection, such as logistical feasibility, models can help investigators optimize site selection and the number and size of participating sites. Although our study focused on trial design for an emerging arbovirus, a similar approach can be made for any infectious disease with the appropriate model for the particular disease.
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影响因子：
9.3
作者：
Halloran, M. Elizabeth;Auranen, Kari;Lipsitch, Marc
通讯作者：
Lipsitch, Marc
影响因子：
19
作者：
Poletto;Gomes, M. F.;Vespignani, A.
通讯作者：
Vespignani, A.
影响因子：
3.3
作者：
Balcan, Duygu;Goncalves, Bruno;Hu, Hao;Ramasco, Jose J.;Colizza, Vittoria;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
影响因子：
9.3
作者：
Tizzoni M;Bajardi P;Poletto C;Ramasco JJ;Balcan D;Gonçalves B;Perra N;Colizza V;Vespignani A
通讯作者：
Vespignani A
影响因子：
5
作者：
Hitchings, Matt D. T.;Lipsitch, Marc;Bellan, Steven E.
通讯作者：
Bellan, Steven E.