| Summary: | 博士 === 國立臺灣大學 === 流行病學與預防醫學研究所 === 104 === In clinical practice, the records of patients with chronic diseases is a form of the longitudinal data. At each patient’s visit, the physician will collect the signs or event information to understand the level of the patient''s future risk of complications or death. According to the level of these risks, physicians need to take some appropriate actions to prevent or delay the occurrence of complications or death. So, how to quantify such risks is a clinically important issue. The purpose of this paper is to use the dynamic messages of marker and the patients’ basic characteristics to predict the patients’ survival. Time-dependent Cox’s model is a population regression model which constructs explicit dependence of the hazard of termination time on baseline covariates and marker process by taking the advantage of longitudinal data with chronological features. However, in the time-dependent Cox’s model, the effect of the marker on the immediate survival has no meaning of prediction. That is, it is not straightforward to predict the future survival given the past information of the marker process in the time-dependent Cox’s model. Therefore, we adopt Bayes'' theorem and conditional probability to overcome such problems. We estimate the conditional probability of future survival given the different information of marker process by using the conditional distribution of baseline covariates and marker process given surviving at a time point and the Cox modeling information. The advantage of the proposed method is that marginal distribution of marker process and baseline hazard function in the Cox’s model are not required. Simulation studies are conducted to assess the performance of the proposed method. An example of papillary thyroid carcinoma is provided for illustration.
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