Martin, GP, Mamas, M ORCID: https://orcid.org/0000-0001-9241-8890, Peek, N, Buchan, I and Sperrin, M (2018) A Multiple-Model Generalisation of Updating Clinical Prediction Models. Statistics in Medicine, 37 (8). pp. 1343-1358.

[img]
Preview
Text
M Mamas - A multiple model generalisation of updating clinical prediction models.pdf - Published Version
Available under License Creative Commons Attribution.

Download (651kB) | Preview

Abstract

There is growing interest in developing clinical prediction models (CPMs) to aid local healthcare decision‐making. Frequently, these CPMs are developed in isolation across different populations, with repetitive de novo derivation a common modelling strategy. However, this fails to utilise all available information and does not respond to changes in health processes through time and space. Alternatively, model updating techniques have previously been proposed that adjust an existing CPM to suit the new population, but these techniques are restricted to a single model. Therefore, we aimed to develop a generalised method for updating and aggregating multiple CPMs. The proposed “hybrid method” re‐calibrates multiple CPMs using stacked regression while concurrently revising specific covariates using individual participant data (IPD) under a penalised likelihood. The performance of the hybrid method was compared with existing methods in a clinical example of mortality risk prediction after transcatheter aortic valve implantation, and in 2 simulation studies. The simulation studies explored the effect of sample size and between‐population‐heterogeneity on the method, with each representing a situation of having multiple distinct CPMs and 1 set of IPD. When the sample size of the IPD was small, stacked regression and the hybrid method had comparable but highest performance across modelling methods. Conversely, in large IPD samples, development of a new model and the hybrid method gave the highest performance. Hence, the proposed strategy can inform the choice between utilising existing CPMs or developing a model de novo, thereby incorporating IPD, existing research, and prior (clinical) knowledge into the modelling strategy.

Item Type: Article
Uncontrolled Keywords: clinical prediction models, logistic regression, stacked regression, model aggregation, model updating, validation
Subjects: H Social Sciences > HA Statistics
R Medicine > R Medicine (General) > R735 Medical education. Medical schools. Research
Divisions: Faculty of Medicine and Health Sciences > Institute for Science and Technology in Medicine
Depositing User: Symplectic
Date Deposited: 20 Nov 2017 11:35
Last Modified: 14 Mar 2019 14:25
URI: http://eprints.keele.ac.uk/id/eprint/4246

Actions (login required)

View Item View Item