Clinical Trial Simulation: Efficacy Trials

Pharmacometrics: The Science of Quantitative Pharmacology

Volumn , Issue , 2006, Pages 881-900

  Riggs, Matthew M ^a   Godfrey, Christopher J ^b,c   Gastanguay, Marc R ^a  

^a Metrum Research Group LLC   (United States)

^b VERTEX PHARMACEUTICALS   (United States)

^c Massachusetts   (United States)

Author keywords

Baseline distribution model; Clinical trial simulation; Covariate models; Efficacy trials; Exposure response; Longitudinal effect (disease progression) model; Model based input factors; Output responses; Sensitivity analysis; Simulation model; Simulation model assumptions; Simulation planning; Simulation recommendations; Simulation results interpretation; Simulation team; Study retention (drop out) models; Trial based input factors; Trial design(s)

Indexed keywords

EID: 84889488728     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470087978.ch35     Document Type: Chapter

References (32)

1. L. B. Sheiner, Learning versus confirming in clinical drug development. Clin Pharmacol Ther 61:275-291 (1997).
2. M. O. Karlsson, T. Anehall, L. E. Friberg, A. Henningsson, C. Kloft, M. Sandstrom, and R. Xie, Pharmacokinetic/pharmacodynamic modelling in oncological drug development. Basic Clin Pharmacol Toxicol 96:206-211 (2005).
3. D. R. Huntjens, M. Danhof, and O. E. la Pasqua, Pharmacokinetic-pharmacodynamic correlations and biomarkers in the development of COX-2 inhibitors. Rheumatology 44:846-859 (2005).
4. S. Chabaud, P. Girard, P. Nony, and J. P. Boissel, Clinical trial simulation using therapeutic effect modeling: application to ivabradine efficacy in patients with angina pectoris. J Pharmacokinet Pharmacodyn 29:339-363 (2002).
5. L. E. Friberg, A. Henningsson, H. Maas, L. Nguyen, and M. O. Karlsson, Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J Clin Oncol 20:4713-4721 (2002).
6. T. M. Post, W. de Winter, J. DeJongh, I. Moules, R. Urquhart, D. Eckland, and M. Danhof, Treatment efficacy of combination-therapy based on a mechanistic characterisation of disease processes in type 2 diabetes mellitus over a two-year period. Abstracts of the Annual Meeting of the Population Approach Group in Europe. PAGE 14:Abstr 757 (2005).
7. P. Hovind, P. Rossing, L. Tarnow, U. M. Smidt, and H. H. Parving, Progression of diabetic nephropathy. Kidney Int 59:702-709 (2001).
8. O. Brinchmann-Hansen, K. Dahl-Jorgensen, L. Sandvik, and K. F. Hanssen, Blood glucose concentrations and progression of diabetic retinopathy: the seven year results of the Oslo study. BMJ 304:19-22 (1992).
9. S. M. Grundy, J. I. Cleeman, C. N. Merz, H. B. Brewer, Jr., L. T. Clark, D. B. Hunninghake, R. C. Pasternak, S. C. Smith, Jr., and N. J. Stone, Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Arterioscler Thromb Vasc Biol 24:e149-e161 (2004).
10. D. M. Jonker, L. A. Kenna, D. Leishman, R. Wallis, P. A. Milligan, and E. N. Jonsson, A pharmacokinetic-pharmacodynamic model for the quantitative prediction of dofetilide clinical QT prolongation from human ether-a-go-go-related gene current inhibition data. Clin Pharmacol Ther 77:572-582 (2005).
11. K. G. Kowalski, L. McFadyen, M. M. Hutmacher, B. Frame, and R. Miller, A two-part mixture model for longitudinal adverse event severity data. J Pharmacokinet Pharmacodyn 30:315-336 (2003).
12. G. Graham, S. Gupta, and L. Aarons, Determination of an optimal dosage regimen using a Bayesian decision analysis of efficacy and adverse effect data. J Pharmacokinet Pharmacodyn 29:67-88 (2002).
13. N. L. Dayneka, V. Garg, and W. J. Jusko, Comparison of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm 21:457-478 (1993).
14. P. L. Chan and N. H. Holford, Drug treatment effects on disease progression. Annu Rev Pharmacol Toxicol 41:625-659 (2001).
15. N. Holford, J. Li, L. Benincosa, and M. Birath, Population disease progress models for the time course of HAMD score in depressed patients receiving placebo in antidepressant clinical trials. Abstracts of the Annual Meeting of the Population Approach Group in Europe. PAGE 11:Abstr 311 (2002).
16. F. E. Harrell, Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer-Verlag, New York, 2001, pp. 79-83.
17. J. Ribbing and E. N. Jonsson, Power, selection bias and predictive performance of the population pharmacokinetic covariate model. J Pharmacokinet Pharmacodyn 31:109-134 (2004).
18. D. R. Mould, Defining covariate distribution models for clinical trial simulation, in Simulation for Defining Clinical Trials, H. Kimko and S. Duffull (Eds.). Marcel Dekker, New York, 2001, pp. 31-54.
19. R. R. Bies, M. R. Gastonguay, K. C. Coley, P. D. Kroboth, and B. G. Pollock, Evaluating the consistency of pharmacotherapy exposure by use of state-of-the-art techniques. Am J Geriatr Psychiatry 10:696-705 (2002).
20. P. Girard, T. F. Blaschke, H. Kastrissios, and L. B. Sheiner, A Markov mixed effect regression model for drug compliance. Stat Med 17:2313-2333 (1998).
21. P. Girard, L. B. Sheiner, H. Kastrissios, and T. F. Blaschke, Do we need full compliance data for population pharmacokinetic analysis? J Pharmacokinet Biopharm 24:265-282 (1996).
22. J. A. Cramer, M. M. Amonkar, A. Hebborn, and R. Altman, Compliance and persistence with bisphosphonate dosing regimens among women with postmenopausal osteoporosis. Curr Med Res Opin 21:1453-1460 (2005).
23. J. J. McNabb, D. P. Nicolau, J. A. Stoner, and J. Ross, Patterns of adherence to antiretroviral medications: the value of electronic monitoring. AIDS 17:1763-1767 (2003).
24. A. J. O'Malley and S. L. Normand, Likelihood methods for treatment noncompliance and subsequent nonresponse in randomized trials. Biometrics 61:325-334 (2005).
25. L. B. Sheiner, A new approach to the analysis of analgesic drug trials, illustrated with bromfenac data. Clin Pharmacol Ther 56:309-322 (1994).
26. C. Hu and M. E. Sale, A joint model for nonlinear longitudinal data with informative dropout. J Pharmacokinet Pharmacodyn 30:83-103 (2003).
27. J. W. Hogan, J. Roy, and C. Korkontzelou, Handling drop-out in longitudinal studies. Stat Med 23:1455-1497 (2004).
28. A. M. Law and W. D. Kelton, Selecting input probability distributions, in Simulation Modeling and Analysis, 3rd ed. McGraw-Hill, New York, 2000, pp. 292-401.
29. I. Nestorov, I. Gueorguieva, H. M. Jones, B. Houston, and M. Rowland, Incorporating measures of variability and uncertainty into the prediction of in vivo hepatic clearance from in vitro data. Drug Metab Dispos 30:276-282 (2002).
30. D. Farrar, B. Allen, K. Crump, and A. Shipp, Evaluation of uncertainty in input parameters to pharmacokinetic models and the resulting uncertainty in output. Toxicol Lett 49:371-385 (1989).
31. W. R. Gillespie, M. Sale, and M. R. Gastonguay, Modeling uncertainty in clinical trial simulation: a Bayesian approach. Clin Pharmacol Ther 65:Abstr, PIII-21 (1999).
32. H. Kraiczi and M. Frisen, Effect of uncertainty about population parameters on pharmacodynamics-based prediction of clinical trial power. Contemp Clin Trials 26:118-130 (2005).