Publication date: Jul 23, 2025

Background: The COVID-19 pandemic caused hospital pressures resulting in some patients with severe COVID-19 not being admitted. Studies aiming to measure treatment effects in patients with severe COVID-19 might produce biased estimates if restricted to hospitalised cohorts. Aim: To quantify the effects of potential selection bias due to deaths outside of hospital in a case study of inhaled corticosteroids (ICS) and COVID-19 death among people with chronic obstructive pulmonary disease (COPD) hospitalised with COVID-19. Methods: Using Clinical Practice Research Datalink Aurum linked to hospitalisation and death registries, we defined a cohort with COPD on 01 Mar 2020, followed up until 31st August 2020. We assessed the odds of COVID-19 death (International Classification of Diseases, 10th Revision U07) among hospitalised COVID-19 patients, comparing current users of ICS/long-acting {beta}-agonist (LABA) and LABA/long-acting muscarinic antagonist (LAMA)). Our target population was those with COPD and severe COVID-19 warranting hospitalisation. We evaluated potential selection bias using quantitative bias analysis (QBA) in four plausible scenarios, varying assumed death rates among non-hospitalised patients. Selection probabilities for deaths due to COVID-19 were known. The assumptions were: (1) equal odds of death between non-hospitalised and hospitalised groups; (2) doubled odds of death in non-hospitalised ICS group compared to hospitalised; (3) halved odds of death in non-hospitalised ICS group; and (4) doubled odds of death in both treatment groups among non-hospitalised patients. We calculated bootstrapped 95% confidence intervals (CIs). Results: During the study period, 107 ICS users and 133 LABA/LAMA users were hospitalised with COVID-19. COVID-19 deaths occurred in 42 (39.3%) ICS users versus 50 (37.6%) LABA/LAMA users. The OR after inverse probability of treatment weighting was 1.01 (95% CI 0.59-1.72). In scenario 1, the OR was unchanged (OR 1.07, 95% CI 0.70-1.67). In scenario 2, the corrected OR was 1.28 (95% CI 0.83-2.00). In scenario 3, the corrected OR was 0.81 (95% CI 0.52-1.23). In scenario 4, the corrected OR was 1.08 (95% CI 0.69-1.71). Conclusion: QBA facilitated an assessment of the sensitivity of study results to potential selection bias. The results of the four scenarios presented are in line with the null hypothesis, but CIs were wide. Death rates in the non-hospitalised would have needed to be substantially different in the treatment groups to change the study conclusions.

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