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姓名 林文明(Wen-Ming Lin) 查詢紙本館藏 畢業系所 統計研究所 論文名稱 藥物動力資料混合效應模型之研究
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摘要(中) 本文在單期單序列或是多期單序列設計之下的口服藥藥物動力(pharmacokinetic,簡稱為PK)研究,針對受試者的藥物濃度-時間側寫建立乘法混合效應PK統計模型,其中使用第一階吸收與代謝的單區模型(one-compartment model)描述平均藥物濃度-時間側寫。另外,為考慮受試者之間的差異性,將模型中的藥物動力參數視為一個具有厚尾的多維斜線分配隨機向量,並且引用高斯關聯結構連結廣義伽瑪分配描述受試者重複測量的藥物濃度之聯合分配。在多期單序列設計之下,上述PK參數除受試者之間的變異,也包含受試者之內的變異。然後本文根據估計的統計模型,建立暴露參數的信賴區間,其中暴露參數包含藥物濃度-時間側寫下的面積、最大藥物濃度值、達最大藥物濃度所需時間與代謝半衰期。本文進一步使用蒙地卡羅(Monte Carlo)方法模擬上述信賴區間的涵蓋機率及期望長度,藉以探討本文所提模型與其他模型的優劣。最後,本文使用兩筆真實資料,展示本文所提方法的應用。 摘要(英) In this thesis, we consider multiplicative nonlinear mixed effects statistical models for orally administered drug concentration-time profiles obtained in a pharmacokinetic (PK) study under a one period/one sequence and multiple periods/one sequence designs, respectively. In the proposed models, the mean concentration-time curve is described by the one-compartment PK model with first-order absorption and elimination. To take into account the between-subject variability, the logarithms of PK parameters-variables in the proposed models are regarded as a multivariate slash random variable. Moreover, a multivariate generalized gamma distribution is developed for the joint distribution of the drug concentrations that are repeatedly measured from the same subject. Under the multiple periods/one sequence design, the PK parameters- variables also include the with-subject variation. Based on the fitted PK statistical models, we then construct confidence sets for the exposure parameters, such as the area under the drug concentration-time curve, the associated maximum drug concentration, the time to maximum concentration and the elimination half-life. A simulation study is also implemented to investigate the coverage probability and expected length of the proposed confidence sets. Finally, the proposed statistical PK models and the associated inferences are then applied to illustrate two real data sets. 關鍵字(中) ★ 藥物動力統計模型
★ 混合效應
★ 多維廣義伽瑪分配關鍵字(英) ★ Pharmacokinetic study
★ Mixed-effects model
★ Multivariate generalized gamma distribution論文目次 目 錄
第一章 緒論…………………………………………………………… .1
第二章 文獻回顧………………………………………………………..7
2.1藥物動力乘法統計模型……………………………………7
2.2藥物動力加法統計模型………………………………… .10
2.3模型參數的估計………………………………………… ..11
第三章 單期單序列藥物動力統計模型………………………………16
3.1穩健混合效應統計模型……………………….…….…….16
3.2模型選擇…………………………………….…………..…22
3.3藥物暴露參數之區間估計………………………..…….…23
第四章 多期單序列藥物動力統計模型……………………….……...25
4.1穩健混合效應統計模型………………………….…….…25
4.2模型選擇………………………………………….…….…31
4.3藥物暴露參數之聯合信賴域……………………..………32
第五章 模擬研究………………………………………………………36
5.1單期單序列藥物動力統計模型之模擬研究………...……36
5.2多期單序列藥物動力統計模型之模擬研究………..……37
第六章 資料分析……………………………………………….….…..39
6.1單期單序列藥物動力資料分析……………………..……39
6.2多期單序列藥物動力資料分析…………………………..42
第七章 結論與未來研究………………………………………………45
參考文獻………………………………………………………………..47
圖附錄………………………………………………………………..…51
表附錄……………………………………………………………..……56
附錄………………………………………………………………..……66
圖目錄
圖1. 設定 、 與 時,執行SAEM演算法的估計參
數路徑示意圖…………………………………………………....51
圖2. 18位受試者服用100毫克氟司喹南後的藥物濃度-時間側
寫...…………………………………………………………….…51
圖3. 18位受試者服用100與150毫克氟司喹南後的代謝物濃度-時間
側寫………….………………………………………………...…52
圖4. 估計18位受試者服用100毫克氟司喹南後的藥物暴露參數之盒
鬚圖………………….……………………………………..….…52
圖5. 18位受試者服用100毫克氟司喹南的估計平均藥物濃度-時間側
寫….…………………………………………………………..….53
圖6. 引進性別的最佳模型下100毫克氟司喹南藥物濃度之暴露參數
95%信賴區間…………….………………………………………53
圖7. 估計1 8位受試者服用100與150毫克氟司喹南後代謝物暴露
參數之盒鬚圖……………………………………………………54
圖8. 18位受試者服用100與150毫克氟司喹南的估計平均代謝物濃
度-時間側寫..………………………………………..………..…54
圖9. 引進性別的最佳模型下100與150毫克氟司喹南代謝物之暴露
參數95%聯合信賴域……………………………………………55
圖10. 100與150毫克氟司喹南代謝物在MGG-2下估計的暴露參
數………………………………………………………………55
表目錄
表1. 單期單序列設計之下95%信賴區間的模擬結果……….………56
表2. 兩期單序列設計之下的95%聯合信賴域模擬結果……….……57
表3. 不具共變數的單期單序列設計之下的氟司喹南藥物濃度-時間
側寫模型選 取………………….………………….……………59
表4. 單期單序列設計之下的氟司喹南藥物濃度-時間側寫模型的重
要變數選取…………………………………….….….….………59
表5. 單期單序列設計之下的氟司喹南藥物濃度-時間側寫模型的參
數估計值及其標準誤……………………………………………60
表6. 單期單序列設計下,100毫克氟司喹南藥物濃度-時間側寫配適
模型的統計量……………………………………………………61
表7. 兩期單序列設計之下的氟司喹南代謝物濃度-時間側寫模型
NLMEM-2在組內與組間共變數結構皆為非對角線時選取誤差
結構……….……………………………………………...………61
表8. 兩期單序列設計之下的氟司喹南代謝物濃度-時間側寫模型的
組內與組間共變數結構選取……………………………………62
表9. 兩期單序列設計之下的氟司喹南代謝物濃度-時間側寫模型的
重要變數選取……………………………………………………62
表10. 兩期單序列設計之下的氟司喹南代謝物濃度-時間側寫模型的
參數估計值及其標準誤……..…………………………………63
表11. 兩期單序列設計下, 100與150毫克氟司喹南代謝物濃度-時
間側寫配適模型的統計量……………………..………………64
附 錄
附錄1. 生成單期單序列設計下的MGG-1藥物動力資料R程式碼…66
附錄2. 使用MGG-1配適資料時使用Gibbs sampler生成隨機樣本的
R程式碼…………………………………………..……...……66
附錄3. 使用MGG-1配適資料的R程式碼……………………………68
附錄4. 單期單序列設計下,計算單一受試者的對數概似函數值R程
式碼……………………………………………………………71
附錄5. 使用MGG-K配適資料時使用Gibbs sampler生成隨機樣本的
R程式碼…………………...……………………………….…72
附錄6. 使用MGG-K配適資料的R程式碼…………………………74
附錄7. 生成多期單序列設計下的MGG-K藥物動力資料R程……81
附錄8. 使用自助法估計共變異數矩陣的R程式……………………82
附錄9. 多期單序列設計下,計算單一受試者的對數概似函數值R程
式碼...……………………………………………………….…82
附錄10. 18位受試者服用100mg氟司喹南後測量的藥物濃度時間-側
寫.………………………………………………………….…85
附錄11. 18位受試者服用150mg氟司喹南後測量的藥物濃度時間-側
寫…..…………………………………………………………86
附錄12. 18位受試者服用100mg氟司喹南後測量的代謝物濃度時間-
側寫………………………………………………………..…87
附錄13. 18位受試者服用150mg氟司喹南後測量的代謝物濃度時間-
側寫…………………………………………………………..88參考文獻 Andrews, D. F. and Mallows, C. L. (1974) Scale mixtures of normal distributions. Journal of the Royal Statistical Society Series B-Statistical Methodology 36,
99-102.
Casella, G. and George, E. I. (1992). Explaining the Gibbs sampler. The American Statistician 46, 167–174.
Chen, Y. I. and Lin, W. M. (2013). Statistical model and inference for pharmacokinetic data. To appear in Journal of Statistical Computation and
Simulation.
Chow, C. S. and Liu, J. P. (2008). Design and analysis of bioavalability and
bioequivalence studies. Marcel Dekker, New York.
Cox, C., Chu, H., Schneider, M. F. and Muñoz, A. (2007). Parametric survival analysis and taxonomy of hazard functions for the generalized gamma
distribution. Statistics in Medicine 26, 4352-4374.
Delyon, B, Lavielle, M. and Moulines, E. (1999). Convergence of a stochastic approximation version of the EM algorithm. The Annals of Statistics 27, 94-128.
Efron B., Tibshirani R. J. (1993). An Introduction to the Bootstrap. Chapman & Hall:
New York.
FDA. (2003). Guidance for industry: Exposure-response relationships-study design, data analysis, and regulatory applications. Center for Drug Evaluation and
Research, U.S. Food and Drug Administration, Rockville, MD.
Ferguson, T. S. (1996). A Course in Large Sample Theory, Chapman & Hall, New
York.
Funatogawa, T., Funatogawa, I. and Yafune, A. (2006). Profile likelihood-based confidence intervals using Monte Carlo integration for population pharmacokinetic parameters. Journal of Biopharmaceutical Statistics 16,
193-205.
Gallo, B. V., Hinson, J. L. and Weidler, D. J. (1993). Pharmacokinetics profile of flosequinan in patients with compromised renal function. Journal of
Pharmaceutics Sciences 82, 282-285.
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and
their applications. Biometrika 57, 97-109.
Hinson, J. L., Hind, I. D. and Weidler, D. J. (1994). Pharmacokinetics, safety, and tolerability of flosequinan in patients with hepatic dysfunction. Journal of
Pharmaceutics Sciences. 83, 382-385.
Jank, W. (2006). Implementing and diagnosing the stochastic approximation EM
algorithm. Journal of Computational and Graphical Statistic 15, 803-829.
Kuhn, E. and Lavielle, M. (2005). Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis 49, 1020-1038.
Lachos, V. H., Castro, L. M. and Dey, D. K. (2013). Bayesian inference in nonlinear mixed-effects models using normal independent distributions. Computational
statistics and data analysis 64, 237-252.
Lange, K. L., Little, R. J. A. and Taylor, J. M. G. (1989). Robust statistical modeling
using the t distribution. Journal of the American Statistical Association 84,
881-896.
Lavielle. M. (2005). Monolix user guide manual. http://www.monolix.org.
Lindsey, J. K., Byrom, W. D., Wang J., Jarvis, P. and Jones B. (2000). Generalized
nonlinear models for pharmacokinetic data. Biometrics 56, 81-88.
Lindsey, J. K. (2001). Nonlinear Models in Medical Statistics. Oxford University Press, New York. Available data at: http://www.commanster.eu/books.html.
Lindsey, J. K. and Lindsey, P. J. (2006). Multivariate distributions with correlation matrices for nonlinear repeated measurements. Computational Statistics and
Data Analysis 50, 720-732.
Louis, T. A. (1982). Finding the observed information matrix when using the EM
algorithm. Journal of the Royal Statistical Society B 44, 226-233.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). Equations of state calculations by fast computing machines. Journal of
Chemical Physics 21, 1087-1092.
Meza, C., Osorio, F. and De la Cruz, R. (2012). Estimation in nonlinear mixed-
effects models using heavy-tailed distributions. Statistical Computation 22,
121-139.
Panhard, X. and Samson, A. (2009). Extension of the SAEM algorithm for nonlinear
mixed models with 2 levels of random effects. Biostatistics 10, 121-135.
R Development Core Team. (2009) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN:
3-900051-07-0, URL http:www.R-project.org.
Rogers, W. H. and Tuckey, J. W. (1972). Understanding some long-tailed distributions. Statistica Neerlandica 26, 211-226.
Salway, R. and Wakefield, J. (2008). Gamma generalized linear models for
pharmacokinetic data. Biometrics 64, 620-626.
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics 6, 461–464.
Sheiner, L. B. and Beal, S. L. (1980). Evaluation of Methods for Estimating PopulationPharmacokinetic Parameters I. Michaelis-Menten Model: Routine Clinical Pharmacokinetic Data. Journal of Pharmacokinetics and
Biopharmaceutics 8, 553-571.
Song, P. X. K. (2000). Multivariate dispersion models generated from Gaussian
copula. Scandinavian Journal of Statistics 27, 305-320.
Stacy, E. W. (1962). A generalization of gamma distribution. Annals of Mathematical Statistics 33, 1187-1192.
Wagner, J. G. (1993). Pharmacokinetics for the Pharmaceutical Scientist. Technomic,
Pennsylvania.
Wei, G. and Tanner, M. (1990). A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithm. Journal of the American
Statistical Association 85, 699-704.指導教授 陳玉英(Yuh-Ing Chen) 審核日期 2013-7-30 推文 facebook plurk twitter funp google live udn HD myshare reddit netvibes friend youpush delicious baidu 網路書籤 Google bookmarks del.icio.us hemidemi myshare