  Over time, the performance of clinical prediction models may deteriorate due
to changes in clinical management, data quality, disease risk and/or patient
mix. Such prediction models must be updated in order to remain useful. Here, we
investigate methods for discrete and dynamic model updating of clinical
survival prediction models based on refitting, recalibration and Bayesian
updating. In contrast to discrete or one-time updating, dynamic updating refers
to a process in which a prediction model is repeatedly updated with new data.
Motivated by infectious disease settings, our focus was on model performance in
rapidly changing environments. We first compared the methods using a simulation
study. We simulated scenarios with changing survival rates, the introduction of
a new treatment and predictors of survival that are rare in the population.
Next, the updating strategies were applied to patient data from the QResearch
database, an electronic health records database from general practices in the
UK, to study the updating of a model for predicting 70-day covid-19 related
mortality. We found that a dynamic updating process outperformed one-time
discrete updating in the simulations. Bayesian dynamic updating has the
advantages of making use of knowledge from previous updates and requiring less
data compared to refitting.
