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Scoping review of methodology for aiding generalisability and transportability of clinical prediction models
Kritchavat Ploddi1, Glen P Martin2, Maurice O'Connell2,3
1Division of Informatics, Imaging & Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK. kritchavat.ploddi@postgrad.manchester.ac.uk.
Background:
Generalisability and transportability of clinical prediction models (CPMs) refer to their ability to maintain predictive performance when applied to new populations. While CPMs may show good generalisability or transportability to a specific new population, it is rare for a CPM to be developed using methods that prioritise good generalisability or transportability. There is an emerging literature of such techniques; therefore, this scoping review aims to summarise the main methodological approaches, assumptions, advantages, disadvantages and future development of methodology aiding the generalisability and transportability of CPMs.
Methods:
Relevant articles were systematically searched from MEDLINE, Embase, medRxiv, arxiv databases until August 2023 using a predefined set of search terms. Extracted information included methodology description, assumptions, applied examples, advantages and disadvantages.
Results:
The searches found 1,761 articles; 172 were retained for full text screening; 18 were finally included. We categorised the methodologies based on whether they are data-driven or knowledge-driven, and whether they are generalisable or transportable to specific target population. Data-driven approaches range from data augmentation to ensemble methods and density ratio weighting, while knowledge-driven strategies rely on causal methodology.
Conclusions:
Future research could focus on comparing these methodologies across simulated and real datasets to identify their strengths and weaknesses in broad applications, as well as synthesising these approaches for enhancing their practical usefulness.
