Models
REGRESSION
We focus on four specific regression models, with increasing complexity:Linear regression, which primarily models mean values with no lower or upper limitsHurdle-negative binomial regression [A logistic regression model modelling the probabilities of 0 daysA negative-binomial model, an over-dispersed count model modelling the means for all patients with ≥ 1 day with no upper limitZero–one-inflated beta-regression [Two logistic regression models modelling the probabilities of having a proportion of either 0 or 1, and for these patients, the probability of having a proportion of 1A beta regression model (using a beta distribution that may be unimodal, U-shaped, or uniform) modelling the Cumulative logistic regression (also known as proportional odds logistic regression) [These models all support as many treatment arms and covariates as desired and can be used to estimate both relative and absolute differences (by calculating either adjusted conditional or marginal estimates for each treatment group), thus enabling interpretation according to clinical importance, which is ideally done on the absolute scale [Different models’ handling of days alive without life support (DAWOLS) using COVID STEROID 2 trial data [Additional details on these models, including their structure, parameters estimated, key assumptions, available effect measures, presentation of results, and benefits and challenges with each model are summarised in Table S1 in Additional file 