 The use of day-walls and similar outcomes is challenged by different definitions and non-normal outcome distributions which complicate statistical analysis decisions. We scrutinize the central methodological considerations when using day-walls and similar outcomes and provide a description and overview of the pros and cons of various statistical methods for analysis supplemented with a comparison of these methods using data from the COVID-steroid 2 randomized clinical trial. We focus on readily available regression models of increasing complexity, linear, hurdle negative binomial, 0-1 inflated beta, and cumulative logistic regression models that allow comparison of multiple treatment arms, adjustment for covariates and interaction terms to assess treatment effect heterogeneity. In general, the simpler models adequately estimated group means despite not fitting the data well enough to mimic the input data. The more complex models better fitted and thus better replicated the input data although this came with increased complexity and uncertainty of estimates. While the more complex models can model separate components of the outcome distributions, example the probability of having zero day-walls, this complexity means that the specification of interpretable priors in a Bayesian setting is. This article was authored by Anders Granholm, Benjamin Skoff-Koss Hansen, Tys Langer, and others.