Weighted target interval stochastic control methods with global optimization and their applications in individualizing therapy

Journal of Pharmacokinetics and Pharmacodynamics

Volumn 34, Issue 4, 2007, Pages 433-449

  Ji, Shaolin ^a,c   Zeng, Yingzhi ^b   Wu, Ping ^b   Lee, Edmund Jon Deoon ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b INSTITUTE OF HIGH PERFORMANCE COMPUTING   (Singapore)

^c SHANDONG UNIVERSITY   (China)

Author keywords

Bayesian estimation; Dosage regimen; Global optimization; Target interval stochastic control; Weighting function

Indexed keywords

THEOPHYLLINE;

ARTICLE; BAYES THEOREM; CLINICAL PRACTICE; COMPARTMENT MODEL; DRUG CLEARANCE; DRUG DISTRIBUTION; DRUG DOSE REGIMEN; DRUG EFFICACY; HUMAN; INDIVIDUALIZATION; MEDICAL DECISION MAKING; POPULATION MODEL; PRACTICE GUIDELINE; PRIORITY JOURNAL; STOCHASTIC MODEL; TARGET INTERVAL STOCHASTIC CONTROL; TREATMENT PLANNING;

ALGORITHMS; BAYES THEOREM; COMPUTER SIMULATION; DRUG THERAPY; HUMANS; LIKELIHOOD FUNCTIONS; MODELS, BIOLOGICAL; PHARMACEUTICAL PREPARATIONS; PHARMACOKINETICS; STATISTICAL DISTRIBUTIONS; STOCHASTIC PROCESSES; THEOPHYLLINE; VASODILATOR AGENTS;

EID: 34447129536     PISSN: 1567567X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10928-007-9054-4     Document Type: Article

References (18)

1. D'Argenio DZ and Katz D (1988). Application of stochastic control methods to the problem of individualizing intravenous theophylline therapy. Biomed Meas Inform Contr 2: 115-122
2. D'Argenio DZ and Park K (1997). Uncertainty pharmacokinetic/pharmaco- dynamic systems: design, estimation and control. Control Eng Pract 5: 1707-1716
3. Hu C (1994) Suboptimal control policies in medical drug therapy. PhD Dissertation, Stanford University
4. Jelliffe R, Bayard D, Milman M, Guilder MV and Schumitzky A (2000). Achieving target goals most precisely using nonparametric compartmental models and "multiple model" design of dosage regimens. Ther Drug Monit 22: 346-353
5. Jelliffe RW, Schumitzky A and Bayard D (1998). Model-based, goal-oriented, individualized drug therapy. Clin Pharmacokinet 34: 57-77
6. Taright N, Mentre F, Mallet A and Jouvent R (1994). Nonparametric estimation of population characteristics of the kinetics of lithium from observational and experimental data. Ther Drug Monit 16: 258-269
7. Rowland M and Tozer T (1994). Clinical pharmacokinetics: concepts and applications. Williams & Wilkins, Baltimore
8. Wakefield J and Racine-Poon A (1995). An application of Bayesian population pharmacokinetic/pharmacodynamic models to dose recommendation. Stat Med 14: 971-986
9. Comets E, Mentre F, Pousset F, Diquet B, Montalescot G, Ankri A, Mallet A and Lechat P (1998). Modeling INR data to predict maintenance fluindione dosage. Ther Drug Monit 20: 631-639
10. Wakefield J (1994). An expected loss approach to the design of dosage regimens via sampling-based methods. Statistics 43: 13-29
11. Harold C Jr, Sox et al (1988) Medical decision making. Butterworths, Boston
12. Berger JO (1985). Statistical decision theory and Bayesian analysis. Springer-Verlag, New York
13. Parmigiani G (2002) Modeling in medical decision making. John Wiley & Sons, England, chap. 2
14. Hu C, Lovejoy WS and Shafer SL (1994). An efficient control strategy for dosage regimens. J Pharmacokin Biopharm 22: 73-94
15. 1 optimal. Computat Stat Data Anal 1: 175-181
16. Katz D and D'Argenio DZ (1984). Discrete approximation of multivariate densities with application to Bayesian estimation. Computat Stat Data Anal 2: 27-36
17. Statistics Toolbox User's Guide for use with MATLAB. The Mathworks Inc. http://www.mathworks.com
18. Ji S, Zeng Y, Wu P and Lee E (2005). Sampling schedule design towards optimal drug monitoring for individualizing therapy. Comput Meth Prog Bio 80: 57-63