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

arXiv: https://arxiv.org/abs/1709.06859 · HTML: https://mlsanity.com/paper/1709.06859/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift](https://mlsanity.com/paper/2504.02694/) (2025)
- [Fixing a Model That Learned Worse Cancer Means Lower Risk: Monotonic Constraints in Bladder Cancer Recurrence Prediction](https://mlsanity.com/paper/2610.00858/) (2026)

## Abstract (arXiv)

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.