The impact of selection bias on vaccine effectiveness estimates from test-negative studies
The impact of selection bias on vaccine effectiveness estimates from test-negative studies
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DOI：
10.1016/j.vaccine.2017.12.022
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发表时间：
2018-01-29
期刊：
影响因子：
5.5
通讯作者：
Jackson, Lisa A.
中科院分区：
文献类型：
--
作者：
Jackson, Michael L.;Phillips, C. Hallie;Jackson, Lisa A.
引言：来自检测阴性研究的疫苗有效性（VE）估计值可能存在选择偏倚。在流感VE的背景下，我们使用模拟来识别可能发生有意义的选择偏倚的情况。我们还分析了观察性研究数据的证据选择bias.Methods：对于模拟研究，我们定义了一个假设的人口，其成员是在急性呼吸道疾病（ARI）的风险，由于流感和其他病原体。一个不可测量的“寻求医疗保健的倾向”影响接种疫苗的可能性和寻求护理的ARI的可能性。我们改变了这些影响的方向和程度，并确定了发生有意义的偏倚的情况。对于观察性研究，我们重新分析了来自美国流感VE网络的数据，这是一项正在进行的测试阴性研究。我们比较了“偏见天真”VE估计偏差调整的估计，使用的数据从源人群，以纠正sampling bias.Results：在模拟研究中，一个未测量的寻求护理的倾向可能会产生选择偏差，如果与流感ARI的人更多（或更少）可能寻求护理比非流感ARI的人。然而，只有当流感ARI和非流感ARI之间的求医率差异很大时，选择偏倚才有意义。在观察性研究中，55%的偏倚初始VE估计值（95%CI，47- 62%）与偏倚调整后的VE估计值57%相比差异很小。（95%可信区间，49- 63%）。综合起来，这些研究表明，虽然在测试阴性VE研究中可能存在选择偏倚，在实践中可能遇到的情况下，这种偏差不太可能有意义。研究人员和公共卫生官员可以继续依赖检测阴性研究的VE估计值。(C)2017爱思唯尔有限公司版权所有。
Introduction: Estimates of vaccine effectiveness (VE) from test-negative studies may be subject to selection bias. In the context of influenza VE, we used simulations to identify situations in which meaningful selection bias can occur. We also analyzed observational study data for evidence of selection bias.Methods: For the simulation study, we defined a hypothetical population whose members are at risk for acute respiratory illness (ARI) due to influenza and other pathogens. An unmeasured "healthcare seeking proclivity" affects both probability of vaccination and probability of seeking care for an ARI. We varied the direction and magnitude of these effects and identified situations where meaningful bias occurred. For the observational study, we reanalyzed data from the United States Influenza VE Network, an ongoing test-negative study. We compared "bias-naive" VE estimates to bias-adjusted estimates, which used data from the source populations to correct for sampling bias.Results: In the simulation study, an unmeasured care-seeking proclivity could create selection bias if persons with influenza ARI were more (or less) likely to seek care than persons with non-influenza ARI. However, selection bias was only meaningful when rates of care seeking between influenza ARI and non-influenza ARI were very different. In the observational study, the bias-naive VE estimate of 55% (95% CI, 47--62%) was trivially different from the bias-adjusted VE estimate of 57% (95% CI, 49--63%).Conclusions: In combination, these studies suggest that while selection bias is possible in test-negative VE studies, this bias in unlikely to be meaningful under conditions likely to be encountered in practice. Researchers and public health officials can continue to rely on VE estimates from test-negative studies. (C) 2017 Elsevier Ltd. All rights reserved.