ﻻ يوجد ملخص باللغة العربية
With the availability of massive amounts of data from electronic health records and registry databases, incorporating time-varying patient information to improve risk prediction has attracted great attention. To exploit the growing amount of predictor information over time, we develop a unified framework for landmark prediction using survival tree ensembles, where an updated prediction can be performed when new information becomes available. Compared to the conventional landmark prediction, our framework enjoys great flexibility in that the landmark times can be subject-specific and triggered by an intermediate clinical event. Moreover, the nonparametric approach circumvents the thorny issue in model incompatibility at different landmark times. When both the longitudinal predictors and the outcome event time are subject to right censoring, existing tree-based approaches cannot be directly applied. To tackle the analytical challenges, we consider a risk-set-based ensemble procedure by averaging martingale estimating equations from individual trees. Extensive simulation studies are conducted to evaluate the performance of our methods. The methods are applied to the Cystic Fibrosis Patient Registry (CFFPR) data to perform dynamic prediction of lung disease in cystic fibrosis patients and to identify important prognosis factors.
Lung cancer is among the most common cancers in the United States, in terms of incidence and mortality. In 2009, it is estimated that more than 150,000 deaths will result from lung cancer alone. Genetic information is an extremely valuable data sourc
In the process of clinical diagnosis and treatment, the restricted mean survival time (RMST), which reflects the life expectancy of patients up to a specified time, can be used as an appropriate outcome measure. However, the RMST only calculates the
Gliomas are lethal type of central nervous system tumors with a poor prognosis. Recently, with the advancements in the micro-array technologies thousands of gene expression related data of glioma patients are acquired, leading for salient analysis in
This paper proposes a spatio-temporal model for wind speed prediction which can be run at different resolutions. The model assumes that the wind prediction of a cluster is correlated to its upstream influences in recent history, and the correlation b
Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive accuracy and interpretability. Starting from a decision tree ensemble, like a boosted tree ensemble or a random forest