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Accounting for treatment use when validating a prognostic model: a simulation study.

Romin Pajouheshnia1, Linda M Peelen2, Karel G M Moons2,3

  • 1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508, GA, Utrecht, the Netherlands. R.Pajouheshnia@umcutrecht.nl.

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Summary
This summary is machine-generated.

Treatment use in validation data can bias prognostic model performance. Ignoring treatment leads to poorer discrimination and calibration; excluding treated individuals or using inverse probability weighting (IPW) can correct this, but IPW has limitations.

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Health Services Research

Background:

  • Prognostic models often exhibit reduced performance when validated on new datasets.
  • Treatment use within validation datasets can distort performance metrics, leading to inaccurate assessments.

Purpose of the Study:

  • To illustrate how treatment use affects prognostic model performance measures.
  • To evaluate analytical methods for accounting for treatment use during model validation.

Main Methods:

  • Simulated data were used to assess the impact of treatment use on model discrimination (c-index) and calibration.
  • Methods evaluated included excluding treated individuals and using inverse probability weighting (IPW).

Main Results:

  • Ignoring effective treatments in validation datasets results in underestimated model performance.
  • Excluding treated individuals corrects performance estimates only with random treatment allocation.
  • IPW followed by exclusion provided valid estimates when IPW assumptions were met, but failed with unobserved confounding.

Conclusions:

  • Treatment use in validation sets must be considered to avoid biased prognostic model performance estimates.
  • Exclusion is suitable for random treatment; IPW with exclusion is recommended for non-random use but requires careful assumption checking.