More than twenty years ago, a pseudo-observation approach to regression in survival analysis was suggested. Such an approach allows for handling incomplete observation of a relevant outcome variable by transforming the available data into pseudo-observations that can replace the potentially unobserved outcomes in a regression analysis. The pseudo-observations are here the jack-knife leave-one-out pseudo-values from a suitable estimator.

The original motivation of the approach was in a multi-state setting with right censoring. It has since been suggested for settings with complications such as left-truncation, interval censoring or recurrent events.

We will review this sort of approach and attempt to find answers to questions such as: How do I use it? When does it work? Why is left-truncated data a challenge? Is variance estimation an issue? How does it compare to similar approaches? Where is it going from here? Are pseudo-observations fun?