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

Using marginal structural models to adjust for treatment drop-in when developing clinical prediction models