TY - JOUR AU - Finkelman, Brian S. AU - French, Benjamin AU - Kimmel, Stephen E. PY - 2016 DA - 2016/01/27 TI - The prediction accuracy of dynamic mixed-effects models in clustered data JO - BioData Mining SP - 5 VL - 9 IS - 1 AB - 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. SN - 1756-0381 UR - https://doi.org/10.1186/s13040-016-0084-6 DO - 10.1186/s13040-016-0084-6 ID - Finkelman2016 ER -