在原廠藥(Innovative Drugs)專利期到期後，任何藥廠都可以製造和原廠藥含有相同主要療效成份的藥，稱為學名藥(Generic Drugs)。新藥研發常耗時長達十數年，但對於學名藥，核准上市並不需提出冗長的臨床試驗過程，只需證明學名藥和原廠藥是否具藥劑生体相等性(Bioequivalence)，因此藥廠可以節省大量時間及金錢成本獲取更多的利潤。藥劑生体相等性分為三種型式:平均生体相等性、族群生体相等性以及個体生体相等性。以族群藥劑生体相等性(Population Bioequivalence)在評估藥物的可處方性(Prescribility)，個体藥劑生体相等性(Individual Bioequivalence)在評估原廠藥和學名藥的可互換性(Switchability)。直到現在，仍有許多學者提出各種不同評定藥劑個体生体相等性的方法。
針對個体生体相等性，FDA 提出一整合的測度量，此測度量為兩藥劑(例如:原廠藥和學名藥)母体平均數的差，個体和藥劑交互作用和兩藥劑個体內變異數的函數。利用上述的測度量，有許多學者提出不同方法來評定個体生体相等性(IBE)，包括Hyslop, Hsuan and Holder (2000)所提出的3H方法，McNally, Irer, and Mathew(2003)提出廣義P值法。對於一兩序列四期重覆的交叉試驗，FDA所提測度量經線性化後所得之測度量可被幾個獨立的卡方分配的線性組合不偏估計，而且個体生体相等性為一假設檢定問題，因此，本文使用大樣本下近似常態分配的方法來計算此檢定統計量的P值和信賴區間。再者，測度量中的各參數如何影響本文所提的型I誤的機率和檢定力也被提出。最後，利用-統計模擬來比較不同方法在型I誤的機率和檢定力的優劣。

Only after the patent of a brand-name innovative drug product is expired, its generic copies are allowed to market. However, regulatory approval requires evidence of bioequivalence based on the pharmacokinetic responses derived from the time-plasma concentration curve of the active ingredients. There are three types of bioequivalence: average bioequivalence (ABE), population bioequivalence (PBE) and individual bioequivalence (IBE). IBE is to evaluate the switchability between the brand-name innovative drug product and its generic copy within the same patient.
The United States Food and Drug Administration (U.S FDA) proposed an aggregate criterion to evaluate IBE. This aggregate criterion is a function of the difference in population averages, subject-by-formulation interaction, and the test and reference intrasubject variabilities. Based on the FDA aggregate criterion, several methods have been proposed to evaluate the IBE, including the 3H method suggested by Hyslop, Hsuan, and Holder (2000) and the generalized p-value method proposed by McNally, Iyer, and Mathew (2003). Because under a two-sequence and four-period replicate crossover design, the linearized form of the FDA aggregate criterion can be unbiasedly estimated by a linear combination of independent chi-square random variables, therefore, the p-value and confidence interval can be obtained from the sampling distribution of the test statistic using the normal approximation. Furthermore, the impact of nuisance parameters on the size and power of the proposed procedure is investigated. A large simulation study was conducted to empirically examine and compare the size and power of different methods.

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