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Transporting a prediction model for use in a new target population.
Pubmed ID
35872598 (View this publication on the PubMed website)
Digital Object Identifier
Am J Epidemiol. 2022 Jul 22
Steingrimsson JA, Gatsonis C, Li B, Dahabreh IJ
  • Department of Biostatistics, School of Public Health, Brown University, Providence, RI, CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.

We consider methods for transporting a prediction model for use in a new target population, when outcome and covariate data for model development are available from a source population that has a different covariate distribution compared to the target population, and when covariate data (but not outcome data) are available from the target population. We discuss how to tailor the prediction model to account for differences in the data distribution between the source population and the target population. We also discuss how to assess the model's performance (e.g., by estimating the mean squared prediction error) in the target population. We provide identifiability results for measures of model performance in the target populationfor a potentially misspecified prediction model under a sampling design where the source and the target population samples are obtained separately. We introduce the concept of prediction error modifiers that can be used to reason about tailoring measuresof model performance to the target population. We illustrate the methods in simulated data and apply them to transport a prediction model for lung cancer diagnosis from the National Lung Screening Trial to the nationally representative target population of trial-eligible individuals in the National Health and Nutrition Examination Survey.

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