A multiple-model generalisation of updating clinical prediction models.
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.
['Martin|Glen P|GP|0000-0002-3410-9472', 'Mamas|Mamas A|MA|', 'Peek|Niels|N|', 'Buchan|Iain|I|', 'Sperrin|Matthew|M|0000-0002-5351-9960']
D000368:Aged D000369:Aged, 80 and over D001021:Aortic Valve D001024:Aortic Valve Stenosis D003198:Computer Simulation D003661:Decision Support Techniques D005260:Female D006801:Humans D016014:Linear Models D016015:Logistic Models D008297:Male D011336:Probability D012044:Regression Analysis D015203:Reproducibility of Results D018570:Risk Assessment D065467:Transcatheter Aortic Valve Replacement
clinical prediction models model updating stacked regression logistic regression model aggregation validation