In randomized clinical trials, power can be greatly increased and sample size reduced by using an ordinal outcome instead of a binary one. The proportional odds model is the most popular model for analyzing ordinal outcomes, and it borrows treatment effect information across outcome levels to obtain a single overall treatment effect as an odds ratio. When deaths can occur, it is logical to have death as one of the ordinal categories. Consumers of the results frequently seek evidence of a mortality reduction even though they were not willing to fund a study large enough to be able to detect this with decent power. The same goes when assessing whether there is an increase in mortality, indicating a severe safety problem for the new treatment. The partial proportional odds model provides a continuous bridge between standalone evidence for a mortality effect and obtaining evidence using statistically richer information on the combination of nonfatal and fatal endpoints. A simulation demons

Estimating tendencies is usually a more appropriate goal than classification, and classification leads to the use of discontinuous accuracy scores which give rise to misleading results.

Sometimes it is unclear exactly what a specific statistical estimator or analysis method is assuming. This is especially true for methods that at first glance appear to be nonparametric when in reality they are semiparametric. This article attempts to explain what it means to make different types of assumptions, and how to tell when a certain type of assumption is being made.