Background: Previously published predictionmodels are often ignored during the development of a novel prediction model. Consequently, numerous prediction models generalize poorly across patient populations, and might have been improved by incorporating such evidence. Unfortunately, aggregation of prediction models is not straightforward, and methods to combine differently specified models are currently lacking.

Methods: We propose two approaches for aggregating the previously published prediction models with observed individual participant data. These approaches yield a new explicit prediction model that, once derived, no longer requires the original models. The first approach is based on model averaging and estimates an overall prediction model that weighs the predictions of the literature models. The second approach is based on stacked regressions, and combines the predictions of the literature models in a logistic regression analysis. We illustrate an implementation in two empirical datasets for predicting Deep Venous Thrombosis and Traumatic Brain Injury, where we compare the approaches to established methods for prediction modeling.

Results: Results from the case studies demonstrate that aggregation yields prediction models with an improved discrimination and calibration in a vast majority of scenarios, and result in equivalent performance (compared to the standard approach) in a small minority of situations.

Conclusions: The proposed aggregation approaches may considerably improve the quality of novel prediction models, and are particularly useful when few participant data are at hand. In addition, they can be combined with variable selection algorithms and random effects weighting schemes to further improve the performance of the aggregated models.