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使用定量偏倚分析调整新冠病毒疾病结局的错误分类:吸入性糖皮质激素与新冠病毒疾病结局的应用实例

Using Quantitative Bias Analysis to Adjust for Misclassification of COVID-19 Outcomes: An Applied Example of Inhaled Corticosteroids and COVID-19 Outcomes.

作者信息

Bokern Marleen, Rentsch Christopher T, Quint Jennifer K, Hunnicutt Jacob, Douglas Ian, Schultze Anna

机构信息

Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.

Faculty of Medicine, National Heart & Lung Institute, Imperial College London, London, UK.

出版信息

Pharmacoepidemiol Drug Saf. 2025 Jan;34(1):e70086. doi: 10.1002/pds.70086.

Abstract

BACKGROUND

During the pandemic, there was concern that underascertainment of COVID-19 outcomes may impact treatment effect estimation in pharmacoepidemiologic studies. We assessed the impact of outcome misclassification on the association between inhaled corticosteroids (ICS) and COVID-19 hospitalisation and death in the United Kingdom during the first pandemic wave using probabilistic bias analysis (PBA).

METHODS

Using data from the Clinical Practice Research Datalink Aurum, we defined a cohort with chronic obstructive pulmonary disease (COPD) on 1 March 2020. We compared the risk of COVID-19 hospitalisation and death among users of ICS/long-acting β-agonist (LABA) and users of LABA/LAMA using inverse probability of treatment weighted (IPTW) logistic regression. We used PBA to assess the impact of non-differential outcome misclassification. We assigned beta distributions to sensitivity and specificity and sampled from these 100 000 times for summary-level and 10 000 times for record-level PBA. Using these values, we simulated outcomes and applied IPTW logistic regression to adjust for confounding and misclassification. Sensitivity analyses excluded ICS + LABA + LAMA (triple therapy) users.

RESULTS

Among 161 411 patients with COPD, ICS users had increased odds of COVID-19 hospitalisations and death compared with LABA/LAMA users (OR for COVID-19 hospitalisation 1.59 (95% CI 1.31-1.92); OR for COVID-19 death 1.63 (95% CI 1.26-2.11)). After IPTW and exclusion of people using triple therapy, ORs moved towards the null. All implementations of QBA, both record- and summary-level PBA, modestly shifted the ORs away from the null and increased uncertainty.

CONCLUSIONS

We observed increased risks of COVID-19 hospitalisation and death among ICS users compared to LABA/LAMA users. Outcome misclassification was unlikely to change the conclusions of the study, but confounding by indication remains a concern.

摘要

背景

在疫情期间,人们担心新冠病毒病(COVID-19)结局的漏诊可能会影响药物流行病学研究中的治疗效果估计。我们使用概率偏差分析(PBA)评估了在疫情第一波期间,结局错误分类对英国吸入性糖皮质激素(ICS)与COVID-19住院和死亡之间关联的影响。

方法

利用临床实践研究数据链奥鲁姆(Clinical Practice Research Datalink Aurum)的数据,我们在2020年3月1日定义了一个慢性阻塞性肺疾病(COPD)队列。我们使用治疗加权逆概率(IPTW)逻辑回归比较了ICS/长效β受体激动剂(LABA)使用者和LABA/长效M受体拮抗剂(LAMA)使用者中COVID-19住院和死亡的风险。我们使用PBA评估非差异性结局错误分类的影响。我们为敏感度和特异度分配了贝塔分布,并针对汇总水平抽样100000次,针对记录水平PBA抽样10000次。利用这些值,我们模拟结局并应用IPTW逻辑回归来调整混杂因素和错误分类。敏感性分析排除了ICS + LABA + LAMA(三联疗法)使用者。

结果

在161411例COPD患者中,与LABA/LAMA使用者相比,ICS使用者发生COVID-19住院和死亡的几率更高(COVID-19住院的比值比为1.59(95%置信区间1.31 - 1.92);COVID-19死亡的比值比为1.63(95%置信区间1.26 - 2.11))。在IPTW并排除使用三联疗法的人群后,比值比趋向于无效值。QBA的所有实施方式,包括记录水平和汇总水平的PBA,都使比值比略有偏离无效值并增加了不确定性。

结论

与LABA/LAMA使用者相比,我们观察到ICS使用者发生COVID-19住院和死亡的风险增加。结局错误分类不太可能改变研究结论,但指征性混杂因素仍然令人担忧。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/980c/11706700/c76a04b0f2c3/PDS-34-e70086-g001.jpg

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