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• W2775351331 abstract "In many studies, it is of interest to predict the future trajectory of subjects based on their historical data, referred to as dynamic prediction. Mixed effects models have traditionally been used for dynamic prediction. However, the commonly used random intercept and slope model is often not sufficiently flexible for modeling subject‐specific trajectories. In addition, there may be useful exposures/predictors of interest that are measured concurrently with the outcome, complicating dynamic prediction. To address these problems, we propose a dynamic functional concurrent regression model to handle the case where both the functional response and the functional predictors are irregularly measured. Currently, such a model cannot be fit by existing software. We apply the model to dynamically predict children's length conditional on prior length, weight, and baseline covariates. Inference on model parameters and subject‐specific trajectories is conducted using the mixed effects representation of the proposed model. An extensive simulation study shows that the dynamic functional regression model provides more accurate estimation and inference than existing methods. Methods are supported by fast, flexible, open source software that uses heavily tested smoothing techniques." @default.
• W2775351331 created "2017-12-22" @default.
• W2775351331 creator A5020009656 @default.
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• W2775351331 date "2017-12-11" @default.
• W2775351331 modified "2023-10-17" @default.
• W2775351331 title "Dynamic prediction in functional concurrent regression with an application to child growth" @default.
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• W2775351331 doi "https://doi.org/10.1002/sim.7582" @default.
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