在今日，藥物的風險評估是一項非常重要的課題。透過建立標竿劑量，即可求出適合人體的藥物劑量。而標竿劑量的定義為有效劑量之信賴區間的下界。目前為止，大部份的文獻都專注在完整資料上的探討，而較少著重在右設限資料上。因此，本文的目的在於應用Buckley和James及Koul, Susarla和Van Ryzin所提出的兩種無母數方法，針對右設限資料所建立的迴歸模型來估計標竿劑量。並利用一實例來說明前述兩種方法之應用。此外，利用模擬實驗來探討上述兩種方法在估計標竿劑量上的表現。

　Risk assessment in drug development is a very important issue in order to gain benefit from drug treatment. The benchmark dose (BMD) approach based on a dose-response relationship is proposed to identify a proper drug dosage for human. The BMD is defined to be the lower limit of confidence interval for an effective dose (ED), where ED is determined by the dose-response model. So far, most literature focuses on complete responses, and less attention has been given to right-censored responses. Therefore, two nonparametric estimation methods for linear model for right-censored data proposed by Buckley and James (1979) and Koul, Susarla and Van Ryzin (1981) are applied to estimate the BMD in this thesis. The implementations of these two methods are demonstrated by a real data set. Furthermore, a simulation study is conducted to investigate the performance of these two methods to estimate a BMD.

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