A Nonparametric Control Chart for Dynamic Disease Risk Monitoring
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Some deadly diseases can be treated or even prevented if they or some of their symptoms are detected early. Disease early detection and prevention is thus important for our health improvement. In this paper, we suggest a novel and effective new method for disease early detection. By this method, a patient’s risk to the disease is first quantified at each time point by survival data analysis of a training dataset that contains patients’ survival information and longitudinally observed disease predictors (e.g., disease risk factors and other covariates). To improve the effectiveness of the proposed method, variable selection is used in the survival analysis to keep only important disease predictors in disease risk quantification. Then, the longitudinal pattern of the quantified risk is monitored sequentially over time by a nonparametric control chart. A signal will be given by the chart once the cumulative difference between the risk pattern of the patient under monitoring and the risk pattern of a typical person without the disease in concern exceeds a control limit.
KeywordsDisease screening Disease early detection Dynamic process Longitudinal data Statistical process control Survival data
The authors thank the editors for the invitation of this contribution to the edited book. One referee provided some comments about the paper for improvements, which is greatly appreciated. This research is supported in part by an NSF grant in USA.
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