Missing data in model-based pharmacometric applications: Points to consider

Journal of Clinical Pharmacology

Volumn 50, Issue 9, 2010, Pages

  Gastonguay, Marc R ^a   French, Jonathan L ^b   Heitjan, Daniel F ^c   Rogers, James A ^a   Jae Eun Ahn, ^b   Ravva, Patanjali ^b  

^a Metrum Research Group LLC   (United States)

^b PFIZER INC   (United States)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

clinical trial simulation; longitudinal; missing data; modeling simulation; Pharmacometrics; population pharmacokinetics

Indexed keywords

BIOMEDICINE; EXPERIMENTAL MODEL; MEDICAL INFORMATICS; MEDICAL INFORMATION; MEDICAL RECORD; MEDICAL RESEARCH; PHARMACOMETRICS; REVIEW; SIMULATION;

CLINICAL TRIALS AS TOPIC; COMPUTER SIMULATION; HUMANS; LONGITUDINAL STUDIES; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PHARMACEUTICAL PREPARATIONS; RESEARCH DESIGN; TREATMENT OUTCOME;

EID: 78449300997     PISSN: 00912700     EISSN: 15524604     Source Type: Journal    
DOI: 10.1177/0091270010378409     Document Type: Review

References (22)

1. Little RJA, Rubin DB Statistical Analysis With Missing Data. 2 nd ed. New York, NY: John Wiley ; 2002.
2. Daniels MJ, Hogan JW Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis. Vol 109 Boca Raton, Fla: Chapman & Hall/CRC ; 2008.
3. Hogan JW, Roy J., Korkontzelou C. Handling drop-out in longitudinal studies. Stat Med. 2004 ; 23: 1455-1497.
4. Zeger SL, Liang KY Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986 ; 42: 121-130.
5. Heitjan DF, Basu S. Distinguishing "missing at random" and "missing completely at random ". Am Stat. 1996 ; 50: 207-213.
6. Hu C., Sale ME A joint model for nonlinear longitudinal data with informative dropout. J Pharmacokinet Pharmacodyn. 2003 ; 30: 83-103.
7. Sheiner LB, Beal SL, Dunne A. Analysis of nonrandomly censored ordered categorical longitudinal data from analgesic trials. J Am Stat Assoc. 1997 ; 92: 1235-1244.
8. Kenward MG Selection models for repeated measurements with non-random dropout: an illustration of sensitivity. Stat Med. 1998 ; 17: 2723-2732.
9. Gelman A., Carlin JB, Stern HS, Rubin DB Model checking and sensitivity analysis. In: Bayesian Data Analysis. New York, NY: Chapman & Hall ; 1995: 161-201.
10. Gelman A., Van MI, Verbeke G., Heitjan DF, Meulders M. Multiple imputation for model checking: completed-data plots with missing and latent data. Biometrics. 2005 ; 61: 74-85.
11. Post TM, Freijer JI, Ploeger BA, Danhof M. Extensions to the visual predictive check to facilitate model performance evaluation. J Pharmacokinet Pharmacodyn. 2008 ; 35: 185-202.
12. Friberg L., de Greef R., Kerbusch T., Karlsson M. Modeling and simulation of the time course of asenapine exposure response and dropout patterns in acute schizophrenia. Clin Pharmacol Ther. 2009 ; 86: 84-91.
13. Bergstrand M., Karlsson MO Handling data below the limit of quantification in mixed effect models. AAPS J. 2009 ; 11: 371-380.
14. Beal SL Ways to fit a PK model with some data below the quantification limit. J Pharmacokinet Pharmacodyn. 2001 ; 28: 481-504.
15. Ahn JE, Karlsson MO, Dunne A., Ludden TM Likelihood based approaches to handling data below the quantification limit using NONMEM VI. J Pharmacokinet Pharmacodyn. 2008 ; 35: 401-421.
16. Kaciroti NA, Schork MA, Raghunathan T., Julius S. A Bayesian sensitivity model for intention-to-treat analysis on binary outcomes with dropouts. Stat Med. 2009 ; 28: 572-585.
17. Troxel A., Ma G., Heitjan DF An index of local sensitivity to nonignorability. Stat Sin. 2004 ; 14: 1221-1237.
18. Zhang J., Heitjan DF A simple local sensitivity analysis tool for nonignorable coarsening: application to dependent censoring. Biometrics. 2006 ; 62: 1260-1268.
19. Xie H., Heitjan DF Local sensitivity to nonignorability: dependence on the assumed dropout mechanism. Stat Biopharm Res. 2009 ; 1: 243-257.
20. Ma G., Troxel AB, Heitjan DF An index of local sensitivity to nonignorable drop-out in longitudinal modelling. Stat Med. 2005 ; 24: 2129-2150.
21. Gomeni R., Lavergne A., Merlo-Pich E. Modelling placebo response in depression trials using a longitudinal model with informative dropout. Eur J Pharm Sci. 2009 ; 36: 4-10.
22. Wang Y., Sung C., Dartois C., et al. Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development. Clin Pharmacol Ther. 2009 ; 86: 167-174.