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

Matthew Sperrin; Glen Martin; Tjeerd Van Staa; Niels Peek; Iain Buchan

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arXiv · 1709.06859

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

Matthew Sperrin·Glen Martin·Tjeerd Van Staa·Niels Peek·Iain Buchan

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Abstract

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.

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Matthew Sperrin, Glen Martin, Tjeerd Van Staa, Niels Peek, Iain Buchan. 2017-09-20. Using marginal structural models to adjust for treatment drop-in when developing clinical prediction models. https://arxiv.org/abs/1709.06859

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