Sullivan Sheena G, Khvorov Arseniy, Huang Xiaotong, Wang Can, Ainslie Kylie E C, Nealon Joshua, Yang Bingyi, Cowling Benjamin J, Tsang Tim K
WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, and Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.
Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
NPJ Vaccines. 2023 Aug 12;8(1):118. doi: 10.1038/s41541-023-00716-9.
Test negative studies have been used extensively for the estimation of COVID-19 vaccine effectiveness (VE). Such studies are able to estimate VE against medically-attended illness under certain assumptions. Selection bias may be present if the probability of participation is associated with vaccination or COVID-19, but this can be mitigated through use of a clinical case definition to screen patients for eligibility, which increases the likelihood that cases and non-cases come from the same source population. We examined the extent to which this type of bias could harm COVID-19 VE through systematic review and simulation. A systematic review of test-negative studies was re-analysed to identify studies ignoring the need for clinical criteria. Studies using a clinical case definition had a lower pooled VE estimate compared with studies that did not. Simulations varied the probability of selection by case and vaccination status. Positive bias away from the null (i.e., inflated VE consistent with the systematic review) was observed when there was a higher proportion of healthy, vaccinated non-cases, which may occur if a dataset contains many results from asymptomatic screening in settings where vaccination coverage is high. We provide an html tool for researchers to explore site-specific sources of selection bias in their own studies. We recommend all groups consider the potential for selection bias in their vaccine effectiveness studies, particularly when using administrative data.
检测呈阴性的研究已被广泛用于估计新冠病毒疫苗有效性（VE）。此类研究能够在某些假设下估计针对需就医疾病的疫苗有效性。如果参与研究的概率与疫苗接种或新冠病毒感染有关，可能会存在选择偏倚，但这可以通过使用临床病例定义来筛选符合条件的患者来减轻，这会增加病例组和非病例组来自同一源人群的可能性。我们通过系统评价和模拟研究了这种偏倚对新冠病毒疫苗有效性的危害程度。对检测呈阴性的研究进行了系统评价重新分析，以识别那些忽视临床标准必要性的研究。与未使用临床病例定义的研究相比，使用临床病例定义的研究汇总疫苗有效性估计值更低。模拟改变了按病例和疫苗接种状态的选择概率。当健康、接种疫苗的非病例比例较高时，观察到偏离无效值的正偏倚（即与系统评价一致的夸大的疫苗有效性），如果数据集中包含许多来自疫苗接种覆盖率高的地区无症状筛查的结果，这种情况可能会发生。我们为研究人员提供了一个超文本标记语言工具，以探索他们自己研究中特定地点的选择偏倚来源。我们建议所有研究团队在疫苗有效性研究中考虑选择偏倚的可能性，尤其是在使用行政数据时。
