Scoping review of methodology for aiding generalisability and transportability of clinical prediction models

TL;DR

This scoping review summarizes recent methodological approaches to improve the generalisability and transportability of clinical prediction models, highlighting data-driven and knowledge-driven strategies and their potential for future research.

Contribution

It categorizes and compares methodologies aimed at enhancing clinical prediction models' applicability across diverse populations.

Findings

Data-driven approaches include data augmentation, ensemble methods, and density ratio weighting.

Knowledge-driven strategies rely on causal inference techniques.

Future research should compare methodologies on real and simulated datasets.

Abstract

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/transportability. Relevant articles were systematically searched from MEDLINE, Embase, medRxiv, arxiv databases until September 2023 using a predefined set of search terms. Extracted information included methodology description, assumptions, applied examples, advantages and disadvantages. The searches found 1,761 articles; 172 were retained for full text screening; 18 were finally included. We categorised the methodologies according to whether they were data-driven or knowledge-driven, and whether are specifically tailored for target population. Data-driven approaches range from data augmentation to ensemble methods and density ratio weighting, while knowledge-driven strategies rely on causal methodology. Future research could focus on comparison of such methodologies on simulated and real datasets to identify their strengths specific applicability, as well as synthesising these approaches for enhancing their practical usefulness.