Multiple Correspondence Analysis (MCA)
In the original studies, a total of 104 variables were available for MCA analysis. Firstly, we removed variables if missing data was >15%, then further removed variables if there were less than 20 infants for each categorical result (e.g. present or absent)23. We then selected variables based on clinical importance for bronchiolitis severity which included; age (<12-months), preterm (<37-weeks), low birth weight (<2.5kg), weight-for-length z-score, exposure to household smoke or in utero, currently breastfed, lobar collapse/consolidation on chest x-ray, previous respiratory hospitalisation, sex, remote (defined as >100km from hospital with paediatric expertise)7, any antibiotics during hospital, supplemental oxygen requirement, any co-morbidity, carer-reported cough or breathing difficulty in last 7-days, any NPS bacteria, bronchiectasis on HRCT in addition to including as many variables (RSV, HRV, accessory muscle use and LOS) as Dumas and colleagues included in their study3. These variables were then entered into the MCA23 to identify the most relevant variables for LCA and to decompose the inertia by identifying a small number of mutually independent dimensions that represent the most important deviations from independence24.