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Clinical prediction in defined populations: a simulation study investigating when and how to aggregate existing
Glen P Martin1, Mamas A Mamas2,3, Niels Peek2,4
1Health e-Research Centre, University of Manchester, Vaughan House, Portsmouth Street, M13 9GB, Manchester, UK. glen.martin@manchester.ac.uk.
BMC Medical Research Methodology
|January 7, 2017
Summary
Aggregating existing clinical prediction models (CPMs) is effective, especially with limited local data. This approach offers better calibration and discrimination than de novo model development when data is sparse.
Area of Science:
- Biostatistics
- Health Informatics
- Clinical Epidemiology
Background:
- Clinical prediction models (CPMs) are crucial for healthcare decisions but often derived inconsistently due to data limitations.
- Aggregating existing CPMs from similar settings presents an alternative to de novo model development.
Purpose of the Study:
- To investigate the impact of between-population heterogeneity and sample size on aggregating existing CPMs.
- To compare aggregation methods with de novo model development in a defined population.
Main Methods:
- Simulations generated heterogeneous populations and a new 'local' population.
- Compared CPM aggregation (stacked regression, PCA, PLS) with de novo development (backwards selection, penalized regression).
Main Results:
- Aggregation methods showed good calibration across all scenarios.
- Aggregation outperformed de novo development with <1000 observations and low heterogeneity, offering better discrimination and lower mean square error.
- De novo redevelopment was superior with >1000 observations and high heterogeneity.
Conclusions:
- Model aggregation is a pragmatic approach for contextualizing CPMs to specific populations.
- Aggregation is a suitable strategy, particularly when local data is sparse.
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