Extending the latent variable model for extra correlated longitudinal dichotomous responses

Journal of Pharmacokinetics and Pharmacodynamics

Volumn 38, Issue 6, 2011, Pages 833-859

  Hutmacher, Matthew M ^a   French, Jonathan L ^b  

^a ANN ARBOR PHARMACOMETRICS GROUP   (United States)

^b PFIZER INC   (United States)

Author keywords

Generalized nonlinear mixed effects models; Latent variable; Multivariate probit; Probit

Indexed keywords

ACCURACY; CASE STUDY; COMPUTER PROGRAM; DRUG EFFECT; HUMAN; MATHEMATICAL COMPUTING; METHODOLOGY; POPULATION DYNAMICS; PREDICTION; PRIORITY JOURNAL; REVIEW; RHEUMATOID ARTHRITIS; SIMULATION; STRUCTURAL EQUATION MODELING;

COMPUTER SIMULATION; HUMANS; MODELS, BIOLOGICAL; PHARMACOLOGY;

EID: 84855675946     PISSN: 1567567X     EISSN: 15738744     Source Type: Journal    
DOI: 10.1007/s10928-011-9222-4     Document Type: Review

References (28)

1. Sheiner LB (1994) A new approach to the analysis of analgesic drug trials, illustrated with bromfenac data. Clin Pharmacol Ther 56:309-322 (Pubitemid 24323923)
2. Sheiner LB, Beal SL, Dunne A (1997) Analysis of nonrandomly censored ordered categorical longitudinal data from analgesic trials. J Am Stat Assoc 92:1235-1244
3. Hutmacher MM, Krishnaswami S, Kowalski KG (2008) Exposure-response modeling using latent variables for the efficacy of a JAK3 inhibitor administered to rheumatoid arthritis patients. J Pharmacokinet Pharmacodyn 35:139-157
4. Hu C, Xu Z, Rahman MU, Davis HM, Zhou H (2010) A latent variable approach for modeling categorical endpoints among patients with rheumatoid arthritis treated with golimumab plus methotrexate. J Pharmacokin Pharmacodyn 37:309-321
5. Lacroix BD, Lovern MR, Stockis A, Sargentini-Maier ML, Karlsson MO, Friberg LE (2009) A pharmacodynamic Markov mixed-effects model for determining the effect of exposure to certolizumab pegol on the ACR20. Clin Pharmacol Ther 86:387-395
6. Felson DT, Anderson JJ, Boers M, Bombardier C, Furst D, Goldsmith C, Katz LM, Lightfoot R Jr, Paulus H, Strand V, Tugwell P, Weinblatt M, Williams HJ, Wolfe F, Kieszak S (1995) ACR preliminary definition of improvement in rheumatoid arthritis. Arthritis Rheum 38:727-735
7. Karlsson MO, Schoemaker RC, Kemp B, Cohen AF, van Gerven JM, Tuk B, Peck CC, Danhof M (2000) A pharmacodynamic Markov mixed-effects model for the effect of temazepam on sleep. Clin Pharmacol Ther 68:175-188 (Pubitemid 30659173)
8. Xu H, Craig BA (2009) A probit latent class model with general correlation structures for evaluating accuracy of diagnostics tests. Biometrics 65:1145-1155
9. Beal SL (2001) Ways to fit a PK model with some data below the quantification limit. J Pharmacokin Pharmacodyn 28:481-504 (Pubitemid 33151426)
10. Ahn JE, Karlsson MO, Dunne A, Ludden TM (2008) Likelihood based approaches to handling data below the quantification limit using NONMEM VI. J Pharmacokinet Pharmacodyn 35:401-421
11. Mendell NR, Elston RC (1974) Multifactorial qualitative traits: Genetic analysis and prediction of recurrence risks. Biometrics 30:41-57
12. Rice J, Reich T, Cloninger CR, Wette R (1979) An approximation to the multivariate normal integral: Its application to multifactorial qualitative traits. Biometrics 35:451-459 (Pubitemid 9208265)
13. Smith C, Mendell NR (1974) Recurrence risks from family history and metric traits. Ann Hum Genet 37:275-286
14. Cappellari L, Jenkins SP (2003) Multivariate probit regression using simulated maximum likelihood. Stata J 3:278-294
15. Joe H (1995) Approximations to multivariate normal rectangle probabilities based on conditional expectations. J Am Stat Assoc 90:957-964
16. SAS Institute Inc (2002) SAS software: Usage and reference, version 9. SAS Institute Inc, Cary
17. MathSoft Inc (2005) S-Plus for Windows. Data Analysis Division, MathSoft Inc, Seattle
18. Beal SL, Sheiner LB, Boeckmann AJ (eds) (1989-2006) NONMEM users guides. Icon Development Solutions, Ellicott City
19. Drezner Z, Wesolowsky GO (1990) On the computation of the bivariate normal integral. J Stat Comput Simul 35:101-107
20. Kuhn E, Lavielle M (2005) Maximum likelihood estimation in nonlinear mixed effects models. Comput Stat Data Anal 49:1020-1038 (Pubitemid 40710158)
21. Bock RD, Gibbons RD (1996) High-dimensional multivariate probit analysis. Biometrics 52:1183-1194 (Pubitemid 26425270)
22. Kremer M, Bloom BJ, Breedveld FC, Coombs J, Fletcher MP, Gruben D, Krishnaswami S, Burgos-Vargas R, Wilkinson B, Zerbini CAF, Zwillich SH (2006) The safety and efficacy of a JAK inhibitor in patients with active rheumatoid arthritis: Results of a double-blind, placebo-controlled phase IIa trial of three dosage levels of CP-690, 550 versus placebo. Arthritis Rheum 60:1895-1905
23. Ochi Y, Prentice RL (1984) Likelihood inference in a correlated probit regression model. Biometrika 71:531-543
24. Liang KY, Zeger SL (1986) Longitudinal data analysis using generalized linear models. Biometrika 73:13-22
25. Jacqmin-Gadda H, Proust-Lima C, Amiéva H (2010) Semi-parametric latent process model for longitudinal ordinal data: Application to cognitive decline. Stat Med 29:2723-2731
26. Genz A (1992) Numerical computation of multivariate normal probabilities. J Comput Graph Stat 1:141-149
27. Yano I, Beal SL, Sheiner LB (2001) The need for mixed-effects modeling with population dichotomous data. J Pharmacokinet Pharmacodyn 28:389-412 (Pubitemid 32953512)
28. Philipson PM, Ho WK, Henderson R (2008) Comparative review of methods for handling drop-out in longitudinal studies. Stat Med 27:6276-6298