Using quantitative bias analysis to adjust for misclassification of COVID-19 outcomes: An applied example of inhaled corticosteroids and COVID-19 outcomes

Abstract

1AbstractDuring 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 UK during the first pandemic wave using probabilistic bias analysis (PBA).Using data from Clinical Practice Research Datalink Aurum, we defined a cohort with chronic obstructive pulmonary disease (COPD) on 01 Mar 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.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 null. All implementations of QBA, both record and summary-level PBA, modestly shifted ORs away from the null and increased uncertainty.The results provide reassurance that outcome misclassification was unlikely to change the conclusions of the study but confounding by indication remains a concern.