Clinical trials simulation: A statistical approach

Journal of Biopharmaceutical Statistics

Volumn 18, Issue 4, 2008, Pages 611-630

  Westfall, Peter H ^a   Tsai, Kuenhi ^d   Ogenstad, Stephan ^c   Tomoiaga, Alin ^a   Moseley, Scott ^b   Lu, Yonggang ^e  

^a TEXAS TECH UNIVERSITY   (United States)

^b VERTEX PHARMACEUTICALS   (United States)

^c Statogen Consulting LLC   (United States)

^d MERCK RESEARCH LABORATORIES   (United States)

^e JP Morgan Chase   (United States)

Author keywords

Binary data; Correlation structure; Grid computing; Mixture data; Non compliance; Non normality; Ordinal data; Patient dropouts; Survival data

Indexed keywords

ALGORITHM; ARTICLE; CLINICAL TRIAL; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; SIMULATION; STATISTICAL ANALYSIS; STATISTICAL MODEL;

CLINICAL TRIALS AS TOPIC; COMPUTER SIMULATION; HUMANS; MODELS, STATISTICAL; PATIENT DROPOUTS;

EID: 46749104753     PISSN: 10543406     EISSN: 15205711     Source Type: Journal    
DOI: 10.1080/10543400802071303     Document Type: Article

References (27)

1. Aalen, O. O., Gjessing, H. K. (2001). Understanding the shape of the hazard rate: A process point of view. Statistical Science 16:1-22.
2. Anderson, J. J., Bolognese, J. A., Felson, D. T. (2003). Comparison of rheumatoid arthritis clinical trial outcome measures. Arthritis and Rheumatism 48:3031-3038.
3. Bremer, R., Perez, J., Smith, P., Westfall, P. (2004). Grid computing at Texas Tech University using SAS. Proceedings of the 14th Annual South-Central SAS User's Group Regional Conference, 64-72.
4. Day, N. E. (1969). Estimating the components of a mixture of normal distributions. Biometrika 56:463-474.
5. Frison, L., Pocock, S. J. (1992). Repeated measures in clinical trials: Analysis using mean summary statistics and its implications for design. Statistics in Medicine 11:1685-1704.
6. Gao, F., Thompson, P., Xiong, C., Miller, J. P. (2006). Analyzing multivariate longitudinal data Using SAS©. Paper 187-31 of Proceedings of the Thirty-first Annual SAS® Users Group International Conference, pp. 1-9.
7. Gieschke, R., Steimer, J.-L. (2000). Pharmacometrics: modeling and simulation tools to improve decision making in clinical dug development. European Journal of Drug Metabolism and Pharmacokinetics 25(1):49-58.
8. Hale, M. D. (1997). Using population pharmacokinetics for planning a randomized concentration-controlled trial with binary response. In: Aarons, L., Balant, L. P., Danhof, M., eds. European Cooperation in the Field of Scientific and Technical Research. Geneva: European Communites, pp. 227-235.
9. Hale, M., Gillespie, D. W., Gupta, S. K., Holford, N. H. (1996). Clinical trial simulation streamlining your drug development process. Applied Clinical Trials 15:35-40.
10. Hale, M. D., Nicholls, A. J., Bullingham, R. E. S., Hene, R., Hoitsman, A., Squifflet, J. P., Weimar, W., Vanrenterghem, Y., Van de Woude, F. J., Verpooten, G. A. (1998). The pharmacokinetic-pharmacodynamic relationship for mycophenolate mofetil in renal transplantation. Clinical Pharmacology and Therapeutics 64:672-683.
11. Holford, N. H. G., Peace, K. E. (1992). Methodologic aspects of a population pharmacodynamic model for cognitive effects in Alzheimer patients treated with Tacrine. Proceedings of the National Academy of Sciences of the United States of America 89:11466-11470.
12. Holford, N., Kimko, H., Montelone, J., Peck, C. (2000). Simulation of clinical trials. Annual Review of Pharmacology and Toxicology 40:209-234.
13. Kimko, H. C., Duffull, S. B. (2003). Simulation for Designing Clinical Trials: A Pharmacokinetic-Pharmacodynamic Modeling Perspective. New York: Marcel Dekker Inc.
14. Lee, M.-L. T., Whitmore, G. A. (2004). First hitting time models for lifetime data. In: Balakrishnan, N., Rao, C., eds. Handbook of Statistics 23, Advances in Survival Analysis. Elsevier: Amsterdam, pp. 537-543.
15. Lee, H., Kimko, H. C., Rogge, M., Wang, D., Nestorov, I., Peck, C. C. (2003). Population pharmacokinetic and pharmacodynamic modeling of Etanercept using logistic regression analysis. Clinical Pharmacology and Therapeutics 73:348-365.
16. Machado, S. C., Miller, R., Hu, C. (1999). A regulatory perspective on pharmacokinetic/pharmacodynamic modeling. Statistical Methods in Medical Research 8:217-245.
17. Mahmood, I. (1999). Allometric issues in drug development. Journal of Pharmaceutical Sciences 88:1101-1106.
18. Maxwell, C., Domenet, J. G., Joyce, C. R. R. (1971). Instant experience in clinical trials: a novel aid to teaching by simulation. Journal of Clinical Pharmacology 11(5):323-331.
19. O'Brien, P. C., Zhang, D., Bailey, K. R. (2005). Semi-parametric and non-parametric methods for clinical trials with incomplete data. Statistics in Medicine 24:341-358.
20. Peck, C., Desjardins, R. (1996). Simulation of clinical trials - encouragement and cautions. Applied Clinical Trials 5:30-32.
21. Peck, C. C., Rubin, D. B., Sheiner, L. B. (2003). Hypothesis: A single clinical trial plus causal evidence of effectiveness is sufficient for drug approval. Clinical Pharmacology and Therapeutics 73:481-490.
22. Poland, B., Wadda, R. (2001). Combining drug-disease and economic modelling to inform drug development decisions. Drug Discovery Today 6:1165-1170.
23. Reigner, B., William, P., Patel, I., Steimer, J.-L., Peck, C. van Brummelen, P. (1997). An evaluation of the integration of pharmacokinetic and pharmacodynamic principles in clinical drug development: experience within Hoffman La Roche. Clinical Pharmacokinetics 33:142-152.
24. Sheiner, L., Steimer, J.-L. (2000). Pharmacokinetic/pharmacodynamic modeling in drug development. Annual Review of Pharmacology and Toxicology 40:67-95.
25. U.S. Department of Health and Human Services, Food and Drug Administration (1999). Guidance for Industry: Clinical Development Programs for Drugs, Devices, and Biological Products for the Treatment of Rheumatoid Arthritis (RA); http://www.fda.gov/cder/guidance/index.htm.
26. Westfall, P. H., Krishen, A. (2001). Optimally weighted, fixed sequence, and gatekeeping multiple testing procedures. Journal of Statistical Planning and Inference 99:25-40.
27. Wiens, B. L., Dmitrienko, A. (2005). The fallback procedure for evaluating a single family of hypotheses. Journal of Biopharmaceutical Statistics 15:929-942.