Mitigation of biases in estimating hazard ratios under non-sensitive and non-specific observation of outcomes-applications to influenza vaccine effectiveness.

IF 3.6 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Emerging Themes in Epidemiology Pub Date : 2021-01-14 DOI:10.1186/s12982-020-00091-z
Ulrike Baum, Sangita Kulathinal, Kari Auranen
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引用次数: 4

Abstract

Background: Non-sensitive and non-specific observation of outcomes in time-to-event data affects event counts as well as the risk sets, thus, biasing the estimation of hazard ratios. We investigate how imperfect observation of incident events affects the estimation of vaccine effectiveness based on hazard ratios.

Methods: Imperfect time-to-event data contain two classes of events: a portion of the true events of interest; and false-positive events mistakenly recorded as events of interest. We develop an estimation method utilising a weighted partial likelihood and probabilistic deletion of false-positive events and assuming the sensitivity and the false-positive rate are known. The performance of the method is evaluated using simulated and Finnish register data.

Results: The novel method enables unbiased semiparametric estimation of hazard ratios from imperfect time-to-event data. False-positive rates that are small can be approximated to be zero without inducing bias. The method is robust to misspecification of the sensitivity as long as the ratio of the sensitivity in the vaccinated and the unvaccinated is specified correctly and the cumulative risk of the true event is small.

Conclusions: The weighted partial likelihood can be used to adjust for outcome measurement errors in the estimation of hazard ratios and effectiveness but requires specifying the sensitivity and the false-positive rate. In absence of exact information about these parameters, the method works as a tool for assessing the potential magnitude of bias given a range of likely parameter values.

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在非敏感和非特异性结果观察下估计风险比偏差的缓解——流感疫苗有效性的应用
背景:对事件时间数据结果的非敏感和非特异性观察影响事件计数和风险集,因此,对风险比的估计有偏倚。我们研究了对事件的不完美观察如何影响基于风险比的疫苗有效性估计。方法:不完全事件时间数据包含两类事件:一部分感兴趣的真实事件;假阳性事件被误记为感兴趣的事件。我们开发了一种估计方法,利用加权的部分似然和假阳性事件的概率删除,并假设敏感性和假阳性率是已知的。使用模拟和芬兰注册数据对该方法的性能进行了评估。结果:这种新方法可以从不完美的事件时间数据中对风险比进行无偏半参数估计。小的假阳性率可以近似为零而不会引起偏差。只要正确指定接种疫苗者与未接种疫苗者的敏感性比,且真实事件的累积风险较小,该方法对敏感性错配具有鲁棒性。结论:加权的部分似然可用于校正风险比和有效性估计中的结果测量误差,但需要指定敏感性和假阳性率。在缺乏关于这些参数的确切信息的情况下,该方法作为评估给定可能参数值范围的潜在偏差大小的工具。
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来源期刊
Emerging Themes in Epidemiology
Emerging Themes in Epidemiology Medicine-Epidemiology
CiteScore
4.40
自引率
4.30%
发文量
9
审稿时长
28 weeks
期刊介绍: Emerging Themes in Epidemiology is an open access, peer-reviewed, online journal that aims to promote debate and discussion on practical and theoretical aspects of epidemiology. Combining statistical approaches with an understanding of the biology of disease, epidemiologists seek to elucidate the social, environmental and host factors related to adverse health outcomes. Although research findings from epidemiologic studies abound in traditional public health journals, little publication space is devoted to discussion of the practical and theoretical concepts that underpin them. Because of its immediate impact on public health, an openly accessible forum is needed in the field of epidemiology to foster such discussion.
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