摘要(英) |
In a pharmacokinetic (PK) study, to claim a test drug under study as a generic drug, proof of the bioequivalence between the test drug and a comparative reference drug is needed. To do so, some healthy volunteers are recruited and administered with the two drugs in a 2×2 crossover design with a reasonable wash-out time period, where the volunteers in one sequence receive the reference drug and then the test drug in two different periods, while the volunteers in the other sequence take the drugs in reverse order in the two periods. After the drug is administered to each volunteer, the drug concentrations in blood or plasma at different time points are then measured, which is referred to as the drug concentration–time curve or profile. The average bioavailability parameters such as the area under the drug concentration–time curve (AUC) is conventionally of interest for assessing the bioequivalence of the test drug to the reference drug. Conventionally, however, the distribution of the logarithm of individual AUC (denoted by logAUC) is followed the lognormal distribution. In practice, this assumption is violated and hence, in this thesis, we propose an alternative distribution, inverse gamma distribution, to satisfy the assumption of the distribution of individual logAUC. In this thesis, we consider to construct the model of individual logAUC which has subject variation and the error term are distributed by normal distribution and inverse gamma distribution, respectively, under the 2x2 crossover design. We consider using the stochastic approximation expectation- maximization algorithm to find the maximum likelihood estimates of the parameters. Then, the bioequivalence test of two drugs is inducted by estimated mean AUC. We further present some results of a simulation study investigation of the level and power performances of the purposed method and the application of the proposed test is finally illustrated by using a real data.
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參考文獻 |
1.Berger, R.L. and Hsu, J. (1996). Bioequivalence trials,
intersection-union tests and equivalence confidence sets. Statistical Science, 11, 283-319.
2.Cox,C. , Chu,H. , Schneider,F. and Munoz,A. (2007). “Parametric survival analysis and taxonomy of hazard functions for the generalized gamma distribution.” Statist. in Medicine, 26, 4352-4374.
3.Cristian Meza • Felipe Osorio • Rolando De la Cruz(2010). Estimation in nonlinear mixed-effects models using heavy-taileddistributions. Springer Science+Business Media, LLC 2010
4.Dempster, A. P., Laird, N. M. & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39, 1-22.
5.Delyon, B., Lavielle, M. and Moulines, E. (1999) Convergence of a stochastic approximation version of the EM algorithm, Annals of Statistics, 27, 94-128.
6.FDA. Guidance for Industry: Statistical Approaches to Establishing Bioequivalence, Center for Drug Evaluation and Research, Food and Drug Administration, U.S. Department of Health and Human Services, 2001.
7.Louis, T.A. (1982): Finding the observed information matrix when using theEM algorithm. J. R. Stat. Soc. Ser. B 44, 226–233
8.Meza, C., Jaffrézic, F., Foulley, J.-L. (2009):Estimation in the probit normalmodel for binary outcomes using the SAEM algorithm. Comput.Stat. Data Anal. 53, 1350–1360
9.Wei, G., Tanner, M. (1990): A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms. J. Am.Stat. Assoc. 85, 699–704
10.Yeh, K. C., and Kwan, K. C. (1978): A comparison of numerical algorithms by trapezoidal, LaGrange, and spline approximations. J. Pharmacokinet. Biopharm., 6: 79-98, 1978.
11.Draper, N.R. and Smith, H. (1981): Applied Regression Analysis.2nd ed.,John Wiley & Sons, New York.
12.Rowland, M. and Tozer, T.N. (1980): Clinical pharmacokinetics Concepts and Applications. Lea& Febiger, Philadelphia,PA.
13.李念純,一維及二維右設限存活資料的適合度檢定,國立中央大
學,碩士論文,民國100年
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