The prediction accuracy of dynamic mixed-effects models in clustered data

BioData Mining
Brian S FinkelmanStephen E Kimmel

Abstract

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. 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 accura...Continue Reading

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Citations

Sep 24, 2016·Clinical Journal of the American Society of Nephrology : CJASN·Jason RoyUNKNOWN Chronic Renal Insufficiency Cohort (CRIC) Study Investigators
Aug 8, 2018·BMC Medical Research Methodology·Haifang NiIrene Klugkist
Nov 3, 2016·Pharmacoepidemiology and Drug Safety·Brian S FinkelmanStephen E Kimmel
May 17, 2019·Diagnostic and Prognostic Research·David A JenkinsNiels Peek
May 6, 2021·Statistics in Medicine·Valentijn M T de JongThomas P A Debray

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