Impact of stochastically generated heterogeneity in hazard rates on individually randomized vaccine efficacy trials.
Impact of stochastically generated heterogeneity in hazard rates on individually randomized vaccine efficacy trials.
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DOI：
10.1177/1740774517752671
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发表时间：
2018-04
期刊：
影响因子：
--
通讯作者：
Lipsitch M
中科院分区：
文献类型：
--
作者：
Kahn R;Hitchings M;Bellan S;Lipsitch M
网络结构和个体对病原体的暴露水平会影响对传染病干预措施的疗效评估研究的结果。感染风险的异质性可能导致随机分组随着试验的进展和越来越多的高风险个体被感染（在先前的工作中被描述为“脆弱”现象）而越来越不同。在这里，我们展示了这种现象可能对泄漏疫苗的个体随机试验产生的影响，在该试验中，所有参与者都是先天可交换的。我们通过创建一个分组为社区的个人网络来模拟疫苗试验，这些社区与更大的主要人群相连。然后，我们以确定性和时变传播率模拟主要人群中的流行病，并随机模拟社区中的流行病。该病的自然历史遵循易感-暴露-感染-恢复模式。利用模拟结果用Cox比例风险模型估计疫苗效力（）。我们发现，即使在随机化时所有的试验参与者都是可交换的，研究人群中社区之间的低连通性和高感染力也存在向下偏倚。出现这种现象是因为这种环境中的随机动力学随机导致感染力量在社区水平上的变化。按社区对Cox模型进行分层可以在不损失功率的情况下减轻这种偏差。了解和计算异质性危险率的影响，可以更准确地估计流行病情况。
Network structure and individuals’ level of exposure to a pathogen can impact results from efficacy evaluation studies of interventions against infectious diseases. Heterogeneity in infection risk can cause randomized groups to increasingly differ as a trial progresses and as more high risk individuals become infected (described in prior work as the “frailty” phenomenon). Here we show the impact this phenomenon can have on an individually randomized trial of a leaky vaccine in which all participants are exchangeable a priori. We model a vaccine trial by generating a network of individuals grouped into communities, which are connected to a larger main population. We then simulate an epidemic, deterministically and with time-varying transmission rates in the main population, and stochastically in the communities. The disease natural history follows a Susceptible-Exposed-Infectious-Recovered model. Simulation results are used to estimate vaccine efficacy ( ) with a Cox proportional hazards model. We find downward bias in associated with low connectivity between communities in the study population and high force of infection, even when all participants in the trial are exchangeable at the time of randomization. This phenomenon arises because the stochastic dynamics in such a setting randomly lead to community-level variation in the force of infection. Stratifying a Cox model by community alleviates this bias with no loss of power. Understanding and accounting for the impact of heterogeneous hazard rates can allow for more accurate estimates of in epidemic settings.
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DOI：
10.1056/nejmoa1411100
发表时间：
2014-10-16
期刊：
The New England journal of medicine
影响因子：
--
作者：
WHO Ebola Response Team;Aylward B;Barboza P;Bawo L;Bertherat E;Bilivogui P;Blake I;Brennan R;Briand S;Chakauya JM;Chitala K;Conteh RM;Cori A;Croisier A;Dangou JM;Diallo B;Donnelly CA;Dye C;Eckmanns T;Ferguson NM;Formenty P;Fuhrer C;Fukuda K;Garske T;Gasasira A;Gbanyan S;Graaff P;Heleze E;Jambai A;Jombart T;Kasolo F;Kadiobo AM;Keita S;Kertesz D;Koné M;Lane C;Markoff J;Massaquoi M;Mills H;Mulba JM;Musa E;Myhre J;Nasidi A;Nilles E;Nouvellet P;Nshimirimana D;Nuttall I;Nyenswah T;Olu O;Pendergast S;Perea W;Polonsky J;Riley S;Ronveaux O;Sakoba K;Santhana Gopala Krishnan R;Senga M;Shuaib F;Van Kerkhove MD;Vaz R;Wijekoon Kannangarage N;Yoti Z
通讯作者：
Yoti Z
影响因子：
3.1
作者：
FAUST, K;WASSERMAN, S
通讯作者：
WASSERMAN, S
影响因子：
158.5
作者：
Hadinegoro, S. R.;Arredondo-Garcia, J. L.;Saville, M.
通讯作者：
Saville, M.
影响因子：
5.4
作者：
O'Hagan, Justin J.;Lipsitch, Marc;Hernan, Miguel A.
通讯作者：
Hernan, Miguel A.
影响因子：
4.6
作者：
Staples, Patrick C.;Ogburn, Elizabeth L.;Onnela, Jukka-Pekka
通讯作者：
Onnela, Jukka-Pekka