  Objectives: Clinical prediction models (CPMs) can inform decision-making
concerning treatment initiation. Here, one requires predicted risks assuming
that no treatment is given. This is challenging since CPMs are often derived in
datasets where patients receive treatment; moreover, treatment can commence
post-baseline - treatment drop-ins. This study presents a novel approach of
using marginal structural models (MSMs) to adjust for treatment drop-in.
  Study Design and Setting: We illustrate the use of MSMs in the CPM framework
through simulation studies, representing randomised controlled trials and
observational data. The simulations include a binary treatment and a covariate,
each recorded at two timepoints and having a prognostic effect on a binary
outcome. The bias in predicted risk was examined in a model ignoring treatment,
a model fitted on treatment na\"ive patients (at baseline), a model including
baseline treatment, and the MSM.
  Results: In all simulation scenarios, all models except the MSM
under-estimated the risk of outcome given absence of treatment. Consequently,
CPMs that do not acknowledge treatment drop-in can lead to under-allocation of
treatment.
  Conclusion: When developing CPMs to predict treatment-na\"ive risk, authors
should consider using MSMs to adjust for treatment drop-in. MSMs also allow
estimation of individual treatment effects.
