Marginal estimates through regression standardization in competing risks and relative survival models

In large disease registers, there is often interest in mortality due a specific cause. Individuals are at risk of death from a variety of other causes, making this a competing-risks situation. Disease registers are observational, and comparisons between exposure groups are prone to confounding. I will introduce a general command, standsurv, for obtaining marginal effects and contrasts from a variety of survival models. In this talk, I will focus on a marginal cause-specific cumulative incidence function after fitting some cause-specific models. These models need to be combined in order to obtain the marginal predictions. If the models appropriately adjust for relevant confounders, then contrasts between marginal estimates can be interpreted as causal effects. I will also describe a number of other useful measures including marginal estimates of the expected life years lost. Relative survival has some similarities to competing risks, and I will demonstrate how many of the ideas for competing risks also apply in a relative survival framework.