Center for Statistics and Data Science, Beijing Normal University, Zhuhai, China.
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Biometrics. 2023 Mar;79(1):73-85. doi: 10.1111/biom.13593. Epub 2021 Nov 15.
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.
预测模型在临床决策中具有重要意义，需要随着患者人群和临床实践的变化而频繁更新。现有的方法要么是临时进行的，例如模型重新校准，要么专注于研究预测因子与结果之间的关系，而较少关注预测本身。在本文中，我们提出了一种动态逻辑斯谛状态空间模型，以便在有新信息可用时随时更新参数。所提出的模型允许同时存在时变和时不变系数。使用平滑样条对时变系数进行建模，以解释其随时间的平滑趋势。通过最大似然法客观地选择平滑参数。该模型使用在预定时间间隔内累积的批量数据进行更新，这可以更好地逼近基础二项式密度函数。在模拟中，我们表明，与现有方法相比，新模型具有更高的预测准确性。我们使用美国器官共享网络的数据来预测肺移植后 1 年的生存率。
