Abstract
BACKGROUND: Clinical prediction models often fail to generalize in the context of clustered data, because most models fail to account for heterogeneity in outcome values and covariate effects across clusters. Furthermore, standard approaches for modeling clustered data, including generalized linear mixed-effects models, would not be expected to provide accurate predictions in novel clusters, because such predictions are typically based on the hypothetical mean cluster. We hypothesized that dynamic mixed-effects models, which incorporate data from previous predictions to refine the model for future predictions, would allow for cluster-specific predictions in novel clusters as the model is updated over time, thus improving overall model generalizability. RESULTS: We quantified the potential gains in prediction accuracy from using a dynamic modeling strategy in a simulation study. Furthermore, because clinical prediction models in the context of clustered data often involve outcomes that are dependent on patient volume, we examined whether using dynamic mixed-effects models would be robust to misspecification of the volume-outcome relationship. Our results indicated that dynamic mixed-effects models led to substantial improvements in prediction accuracy in clustered populations over a broad range of conditions, and were uniformly superior to static models. In addition, dynamic mixed-effects models were particularly robust to misspecification of the volume-outcome relationship and to variation in the frequency of model updating. The extent of the improvement in prediction accuracy that was observed with dynamic mixed-effects models depended on the relative impact of fixed and random effects on the outcome as well as the degree of misspecification of model fixed effects. CONCLUSIONS: Dynamic mixed-effects models led to substantial improvements in prediction model accuracy across a broad range of simulated conditions. Therefore, dynamic mixed-effects models could be a useful alternative to standard static models for improving the generalizability of clinical prediction models in the setting of clustered data, and, thus, well worth the logistical challenges that may accompany their implementation in practice.