Hitchings Matt David Thomas, Grais Rebecca Freeman, Lipsitch Marc
Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Epicentre, Paris, France.
PLoS Negl Trop Dis. 2017 Mar 22;11(3):e0005470. doi: 10.1371/journal.pntd.0005470. eCollection 2017 Mar.
The 2014-6 West African Ebola epidemic highlights the need for rigorous, rapid clinical trial methods for vaccines. A challenge for trial design is making sample size calculations based on incidence within the trial, total vaccine effect, and intracluster correlation, when these parameters are uncertain in the presence of indirect effects of vaccination.
We present a stochastic, compartmental model for a ring vaccination trial. After identification of an index case, a ring of contacts is recruited and either vaccinated immediately or after 21 days. The primary outcome of the trial is total vaccine effect, counting cases only from a pre-specified window in which the immediate arm is assumed to be fully protected and the delayed arm is not protected. Simulation results are used to calculate necessary sample size and estimated vaccine effect. Under baseline assumptions about vaccine properties, monthly incidence in unvaccinated rings and trial design, a standard sample-size calculation neglecting dynamic effects estimated that 7,100 participants would be needed to achieve 80% power to detect a difference in attack rate between arms, while incorporating dynamic considerations in the model increased the estimate to 8,900. This approach replaces assumptions about parameters at the ring level with assumptions about disease dynamics and vaccine characteristics at the individual level, so within this framework we were able to describe the sensitivity of the trial power and estimated effect to various parameters. We found that both of these quantities are sensitive to properties of the vaccine, to setting-specific parameters over which investigators have little control, and to parameters that are determined by the study design.
Incorporating simulation into the trial design process can improve robustness of sample size calculations. For this specific trial design, vaccine effectiveness depends on properties of the ring vaccination design and on the measurement window, as well as the epidemiologic setting.
2014 - 2016年西非埃博拉疫情凸显了对疫苗进行严格、快速临床试验方法的需求。试验设计面临的一个挑战是，当这些参数在疫苗接种存在间接效应的情况下不确定时，要根据试验中的发病率、总体疫苗效果和集群内相关性来计算样本量。
我们提出了一种用于环形疫苗接种试验的随机、分区模型。在识别出索引病例后，招募一圈接触者，并立即或在21天后进行疫苗接种。试验的主要结果是总体疫苗效果，仅计算来自预先指定窗口内的病例，在该窗口中，立即接种组被假定为完全得到保护，而延迟接种组未得到保护。模拟结果用于计算所需样本量和估计的疫苗效果。在关于疫苗特性、未接种环中的月发病率和试验设计的基线假设下，一个忽略动态效应的标准样本量计算估计，需要7100名参与者才能有80%的把握检测出两组之间发病率的差异，而在模型中纳入动态因素后，估计值增加到了8900。这种方法用关于个体层面疾病动态和疫苗特性的假设取代了关于环层面参数的假设，因此在这个框架内，我们能够描述试验效能和估计效果对各种参数的敏感性。我们发现，这两个量都对疫苗特性、研究人员几乎无法控制的特定环境参数以及由研究设计决定的参数敏感。
将模拟纳入试验设计过程可以提高样本量计算的稳健性。对于这种特定的试验设计，疫苗效力取决于环形疫苗接种设计的特性、测量窗口以及流行病学环境。
