Dynamic logistic state space prediction model for clinical decision making

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.

Automated summary

In this article, a dynamic logistic state space model is proposed to continuously update parameters for better prediction accuracy. The model allows for both time-varying and time-invariant coefficients, with time-varying coefficients modeled using smoothing splines and smoothing parameters chosen by maximum likelihood. The model is updated using batch data to approximate the underlying binomial density function. Simulation results show significantly higher prediction accuracy compared to existing methods. The method is applied to predict 1 year survival after lung transplantation using United Network for Organ Sharing data.