藥劑生體可用及相等試驗,對於藥品的研發佔有非常重要之地位.如何在各種交叉實驗設計中選取適當的樣本,以達到我們要求的檢測能力,是本文的主要目的.Liu and chow(1992a)根據Schuirmann(1987)的區間假設檢定以正負20決策準則,針對二乘二交叉實驗設計,提出了一個簡易的樣本數計算方法.本文將對高階交叉實驗設計之樣本數計算方法,做進一步的研究.

在本文中，分別就四種不同參數組合（包括六個模式）的二維交叉實驗設計，採用一般線性模式法及二樣本t檢定法予以分析，並探討模式間與分析方法間的異同。此外，在二維重覆測量交叉實驗設計方面，我們也分別以單變量分裂區集變異數分析法及多變量變異數分析法進行探討。 / Four possible parametrizations ( including six models ) are considered in this study to clearify some ambigous issues related to a 2*2 cross-over design. Each model is analyzed using both the GLM procedure and two-sample t test. In addition, we also discuss issues related to the 2*2 repeated measurements cross-over design by using the univariate split-plot and multivariate analysis of variance techniques.

傳統上，判定一種學名藥(generic drug)與原廠藥(innovator drug)是否具有生體相等性所常用的統計方法為：比較兩種藥物的生體可用相對值(relative bioavailability)的母體平均數是否相等，此即所謂的平均生體相等性(average bioequivalence)。然而就兩種藥物可互用的觀點而言，似乎更需要考慮的是每位受測個體在服用藥物後，不同藥物在個體內反應的差異性，因此 Anderson and Hauck (1990)提出個體生體相等性(individual bioequivalence)的觀點。 本文採 Schall (1995)所建議的判定準則來作為評估一種學名藥與原廠藥是否具有個體生體相等性的依據。內容重點為透過模擬(simulation)實驗的方式，對判定藥物為個體生體相等性的檢定力(power)作一評比，研究的項目有：(1)檢定力在不同交叉實驗設計(crossover design)下表現的異同；(2)檢定力在不同參數組合情況下表現的趨勢；(3)樣本數(sample size)對檢定力的影響。 / Conventionally, that a generic drug and an innovator drug are regarded as having the same treatment effects is based on the concept of average bioequivalence,i.e., that average responses between individuals on the two formulations are similar. Anderson and Hauck (1990) argued that it was not sufficient to expect that an individual patient would response similarly to the two formulations. The thought has received a lot of attention lately, and quite a few methods have been proposed to deal with the issue of the individual bioequivalence. According to the "unified" approach proposes by Schall (1995), a simulation study on power to declare bioequivalence and coverage probability of confidence intervals is carried out here to compare their performance under different experimental designs.

在製藥工業中，藥量特性之研究常被應用於藥理學、毒物學及臨床試驗中。藥量特性之研究同時在臨床試驗中的第一階段的藥物安全性及第二階段的藥物有效性中扮演著重要的角色。透過藥量特性的研究，使我們對於藥物的開發有著更深一層的認識，並可藉此縮短藥物核准上市的時間。 在多數的情況下，我們對於藥物動力學之參數與藥物劑量間的線性關係，有著特別的興趣。基於這個目的，一些典型的方法即是由一般線性模式發展而成的。然而，這些典型的統計方法常遇到下列的難題而違反其假設：（一）不同藥物劑量間的異質變異性。（二）不滿足常態性的假設。針對這些問題，我們藉由比較不同劑量間的斜率關係的無母數檢定程序來評估其線性關係並刻劃出藥物的反應曲線，文中並藉此方法舉出交叉實驗之實例。 / The problem of characterizing dose response curve for a pharmacokinetic parameter over a specific dose range is considered. In many cases, it is of interest to determine dose linearity (or dose proportionality) between the pharmacokinetic parameters and dose levels. For this purpose, several classical methods based on a general linear model procedure are available. However, two difficulties commonly encountered, namely (i) heterogeneity of the varibility at different dose levels and (ii) violation of the normality assumptions, often make the classical methods not applicable. To account for these problems, we propose a general nonparametric test procedure by comparing the slopes at different dose level to asses dose linearity and to characterize dose response curve. An example concerning the study of dose response of a compound based on a four-way crossover experiment is presented.