在生物醫學及臨床領域中,檢驗雙樣本存活曲線是一個常見的問題。在過去所有方法中,log rank test 為最常使用的方法,但是這個方法卻在兩條存活曲線交叉的情況下無法得到較好的檢定結果。此外,目前沒有一個方法可以適用於所有情況。因此,我們利用經驗概似比檢定方法的好性質,去延伸成其他的檢定方法。我們也建構幾種不同存活曲線的交叉情況,利用模擬實驗的方式,討論方法的檢定力及型I誤差,然後再將提出的方法和現存的方法一同比較,例如有 log rank test, Gehan-Wilcoxon test 和 Tarone-Ware test 等等。由模擬結果來看,我們發現當兩條存活曲線有兩個交叉點的情況下,使用經驗概似比延伸的檢定方法會比其他方法檢定力更高。除此之外,每個經驗概似比延伸的檢定方法也都具有適合的型I誤差。最後,我們應用所有的方法來分析兩筆醫學的資料作為實證分析。 It has been a common question for detecting two-sample survival curves in biomedical studies and clinical field. Among those proposed tests, the log rank test is the most popular approach. However, it fails to perform well under conditions of crossing survival functions. Moreover, there is no approach in the possession of preponderance for all situations in evidence. Therefore, we utilize the decent properties of empirical likelihood ratio to develop other methods. We also conduct several types of crossing survival curves and conduct the simulations to investigate the power and type I error rate. Then we compare the proposed approaches with some existing methods such as log rank test, Gehan-Wilcoxon test and Tarone-Ware test, etc. From simulations, we recommend EL-based method for two survival curves with two crossing points. The EL-based approaches are also possessed of adaptable type I error. Eventually, we analyze two medical data examples for illustrations.