復發事件資料在醫學領域的長期追蹤研究中相當常見。研究中，病患沒有回診接受追蹤的這段空缺，我們稱之為追蹤研究中的觀測缺口。沒有觀測缺口的復發事件資料常用的模型有兩種，分別為相乘型風險模型與相加型風險模型。而本研究將觀測缺口的訊息納入考量，針對使用發生率函數的半參數相加型風險模型發展參數估計方法。針對所推導的方法，本研究以模擬的方法驗證所提估計方法的正確性，最後以一組研究運動員感染社區相關抗青黴素金黃色葡萄球菌的復發事件資料做實例分析。

Recurrent events data are common arisen in longitudinal studies. In particular, some study subjects may out of the study for a period of time for various reasons and then comeback in the study again. During the absence, which is called an observation gap, the recording of events may not be observed. In the analysis of the recurrent event data, we consider the use of an additive rates model to fit recurrent event data with observation gaps. Simulation studies are performed to evaluate the finite sample behaviors of the estimators in the case of time-independent and time-dependent baseline rate functions. Finally, we apply our method to a real case which is about the community- associated methicillin-resistant Staphylococcus aureus (CA-MRSA) colonization study.

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