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

Matthew Sperrin1, Glen P Martin1, Alexander Pate1

  • 1Farr Institute, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.

Statistics in Medicine
|August 4, 2018
PubMed
Summary

Clinical prediction models (CPMs) can underestimate treatment-naïve risk due to "treatment drop-ins." Marginal structural models (MSMs) effectively adjust for this bias, improving treatment allocation accuracy.

Keywords:
clinical prediction modelscounterfactual causal inferencelongitudinal datamarginal structural modelstreatment drop-invalidation

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Informatics

Background:

  • Clinical prediction models (CPMs) are crucial for treatment decisions, requiring accurate estimates of treatment-naïve risk.
  • CPMs often use data with postbaseline treatment initiation ('treatment drop-ins'), biasing risk predictions.
  • Existing models struggle to account for this complex treatment initiation pattern.

Purpose of the Study:

  • To propose and evaluate marginal structural models (MSMs) for adjusting CPMs to account for treatment drop-in.
  • To assess the impact of treatment drop-in on predicted treatment-naïve risk in various scenarios.
  • To demonstrate the utility of MSMs in improving the accuracy of CPMs for clinical decision-making.

Main Methods:

  • Utilized simulation studies mimicking randomized controlled trials and observational data.
  • Incorporated a binary treatment, a covariate at two timepoints, and a binary outcome.
  • Compared risk predictions from models ignoring treatment, using baseline-only data, including baseline treatment, and employing MSMs.

Main Results:

  • All models except the MSM systematically underestimated treatment-naïve risk across simulation scenarios.
  • Analysis of statin initiation for cardiovascular disease prevention confirmed this underestimation.
  • Ignoring postbaseline statin initiation led to significantly underestimated 10-year event risks.

Conclusions:

  • Marginal structural models (MSMs) are essential for accurately adjusting clinical prediction models for treatment drop-in.
  • Failure to account for treatment drop-in can lead to underallocation of necessary treatments.
  • MSMs offer a robust method for estimating treatment-naïve risk and individual treatment effects.