  In this paper we study approaches for dealing with treatment when developing
a clinical prediction model. Analogous to the estimand framework recently
proposed by the European Medicines Agency for clinical trials, we propose a
`predictimand' framework of different questions that may be of interest when
predicting risk in relation to treatment started after baseline. We provide a
formal definition of the estimands matching these questions, give examples of
settings in which each is useful and discuss appropriate estimators including
their assumptions. We illustrate the impact of the predictimand choice in a
dataset of patients with end-stage kidney disease. We argue that clearly
defining the estimand is equally important in prediction research as in causal
inference.
