Power, selection bias and predictive performance of the population pharmacokinetic covariate model

Journal of Pharmacokinetics and Pharmacodynamics

Volumn 31, Issue 2, 2004, Pages 109-134

  Ribbing, Jakob ^a   Niclas Jonsson, E ^a  

^a UPPSALA UNIVERSITY   (Sweden)

Author keywords

competition bias; NONMEM; statistical significance; stepwise model building; stopping rule bias

Indexed keywords

ARTICLE; CORRELATION ANALYSIS; DRUG CLEARANCE; MOLECULAR MODEL; POPULATION MODEL; PREDICTION; PRIORITY JOURNAL; SIMULATION;

COMPUTER SIMULATION; HUMANS; MODELS, BIOLOGICAL; PHARMACOKINETICS; SELECTION BIAS;

EID: 3142724653     PISSN: 1567567X     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:JOPA.0000034404.86036.72     Document Type: Article

References (18)

1. E. N. Jonsson and M. O. Karlsson. Automated covariate model building within NON-MEM. Pharm. Res. 15:1463-1468 (1998).
2. A. J. Miller. Selection of subsets of regression variables. J. R. Stat. Soc. Series A (General). 147:389-425 (1984).
3. U. Wahlby, E. N. Jonsson, and M. O. Karlsson. Comparison of stepwise covariate model building strategies in population pharmacokinetic- pharmacodynamic analysis. AAPS Pharm. Sci. 4:27 (2002).
4. P. L. Bonate. The effect of collinearity on parameter estimates in nonlinear mixed effect models. Pharm. Res. 16:709-717 (1999).
5. R. O'Neill and G. B. Wetherill. The present state of multiple comparison methods. J. R. Stat. Soc. Series B (Methodological). 33:218-250 (1971).
6. E. W. Steyerberg, M. J. Eijkemans, and J. D. Habbema. Stepwise selection in small data sets: a simulation study of bias in logistic regression analysis. J. Clin. Epidemiol. 52:935-942 (1999).
7. G. E. P. Box. Use and abuse of regression. Technometrics. 8:629-629 (1966).
8. D. W. Cockroft and M. H. Gault. Prediction of creatinine clearance from serum creatinine. Nephron. 16:31-41 (1976).
9. R. Bruno, N. Vivler, J. C. Vergniol, S. L. De Phillips, G. Montay, and L. B. Sheiner. A population pharmacokinetic model for docetaxel (Taxotere): model building and validation. J. Pharmacokinet. Biopharm. 24:153-172 (1996).
10. I. The MathWorks. MATLAB. The Math Works, Natick, MA.
11. S. L. Beal. NONMEM Users Guides. NONMEM Project Group, University of California at San Francisco, San Francisco, CA, 1994.
12. U. Wahlby, E. N. Jonsson, and M. O. Karlsson. Assessment of actual significance levels for covariate effects in NONMEM. J. Pharmacokinet. Pharmacodyn. 28:231-252 (2001).
13. L. B. Sheiner and S. L. Beal. Some suggestions for measuring predictive performance. J. Pharmacokinet. Biopharm. 9:503-512 (1981).
14. J. S. U. Hjorth. Computer Intensive Statistical Methods. Chapman & Hall, London; 1994.
15. P. I. Lee. Design and power of a population pharmacokinetic study. Pharm. Res. 18:75-82 (2001).
16. E. N. Jonsson, J. R. Wade, and M. O. Karlsson. Comparison of some practical sampling strategies for population pharmacokinetic studies. J. Pharmacokinet. Biopharm. 24:245-263 (1996).
17. S. Retout, S. Duffull, and F. Mentre. Development and implementation of the population Fisher information matrix for the evaluation of population pharmacokinetic designs. Comput. Meth. Prog. Biomed. 65:141-151 (2001).
18. i. MathSoft, S-Plus 2000. Math Soft, Seattle, WA.