Transporting a prediction model for use in a new target population

TL;DR

This paper discusses methods for adapting and evaluating prediction models when transferring them from a source to a target population, especially when outcome data is limited in the target.

Contribution

It introduces new methods for tailoring prediction models and assessing their performance in the target population using only covariate data, with theoretical identifiability results.

Findings

Identifiability results for mean squared error in target populations

Introduction of prediction error modifiers for model tailoring

Simulation studies demonstrating method effectiveness

Abstract

We consider methods for transporting a prediction model and assessing its performance for use in a new target population, when outcome and covariate information for model development is available from a simple random sample from the source population, but only covariate information is available on a simple random sample from the target population. We discuss how to tailor the prediction model for use in the target population, how to assess model performance in the target population (e.g., by estimating the target population mean squared error), and how to perform model and tuning parameter selection in the context of the target population. We provide identifiability results for the target population mean squared error of a potentially misspecified prediction model under a sampling design where the source study and the target population samples are obtained separately. We also introduce the concept of prediction error modifiers that can be used to reason about the need for tailoring measures of model performance to the target population and provide an illustration of the methods using simulated data.