A wavelet-mixed landmark survival model for the effect of short-term oscillations in longitudinal biomarker's profiles
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Statistical methods to study the association between a longitudinal biomarker and the risk of death are very relevant for the long-term monitoring of biomarkers. In this context, sudden crises can cause the biomarker to undergo very abrupt changes. Although these oscillations are typically short-term, they can contain prognostic information. We propose a method that couples a linear mixed-model with a wavelet smoothing to extract both the long-term component and the short-term oscillations of the individual longitudinal biomarker profiles. We then use them as predictors in a landmark model to study their association with the risk of death. To illustrate the method, we use the clinical application which motivated our work i.e. the monitoring of potassium in Heart Failure patients. The dataset consists of real-world data coming from the integration of Administrative Health Records and Outpatient and Inpatient Clinic E-chart. Our method not only allows us to identify the short-term oscillations but also reveals their prognostic role, according to their duration, demonstrating the importance of including them in the modeling. Compared to landmark analyses and joint models, the proposed method archives higher predictive performances. In the context of the monitoring of potassium, our analysis has important clinical implications because it allows us to derive a dynamic score that can be used in clinical practice to assess the risk related to an observed patient's potassium trajectory.
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