Dynamic logistic state space prediction model for clinical decision making |
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Authors: | Jiakun Jiang Wei Yang Erin M. Schnellinger Stephen E. Kimmel Wensheng Guo |
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Affiliation: | 1. Center for Statistics and Data Science, Beijing Normal University, Zhuhai, China;2. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA;3. Department of Epidemiology, University of Florida, Gainesville, Florida, USA |
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Abstract: | Prediction modeling for clinical decision making is of great importance and needed to be updated frequently with the changes of patient population and clinical practice. Existing methods are either done in an ad hoc fashion, such as model recalibration or focus on studying the relationship between predictors and outcome and less so for the purpose of prediction. In this article, we propose a dynamic logistic state space model to continuously update the parameters whenever new information becomes available. The proposed model allows for both time-varying and time-invariant coefficients. The varying coefficients are modeled using smoothing splines to account for their smooth trends over time. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at prespecified time intervals, which allows for better approximation of the underlying binomial density function. In the simulation, we show that the new model has significantly higher prediction accuracy compared to existing methods. We apply the method to predict 1 year survival after lung transplantation using the United Network for Organ Sharing data. |
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Keywords: | dynamic prediction Laplace approximation smoothing spline |
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