Using bootstrap method to evaluate the estimates of nicotine equivalents from linear mixed model and generalized estimating equation

Journal of Applied Statistics

Volumn 36, Issue 4, 2009, Pages 453-463

  Liang, Qiwei ^a   Li, Huajiang ^a   Mendes, Paul ^a   Roethig, Hans ^a   Frost Pineda, Kim ^a  

^a Philip Morris USA   (United States)

Author keywords

Bootstrap estimates; Generalized estimation equations; Linear mixed models; Nicotine equivalents

Indexed keywords

EID: 70449364108     PISSN: 02664763     EISSN: 13600532     Source Type: Journal    
DOI: 10.1080/02664760802638074     Document Type: Article

References (23)

1. N.R. Chaganty, An alternative approach to the analysis of longitudinal data via generalized estimating equations, J. Statist. Plann. Infer. 63 (1997), pp. 39-54.
2. M. Chernick, Bootstrap Methods: A Guide for Practitioners and Researchers, 2nd ed., Wiley, Hoboken, New Jersey, 2008.
3. A.C. Davison and D.V. Hinkley, Bootstrap Methods and their Application, Cambridge University Press, NewYork, 1997, pp. 11-103.
4. E. Demidenko, Mixed Models: Theory and Applications, Wiley, Hoboken, New Jersey, 2004, pp. 30-210.
5. P.J. Diggle, P. Heagerty, K.Y. Liang, and S.L. Zeger, Analysis of Longitudinal Data, 2nd ed., Oxford University Press, NewYork, 2002, pp. 52-250.
6. B. Efron, Bootstrap confidence intervals, J. Amer. Statist. Assoc. 82 (1987), pp. 171-185.
7. B. Efron and R.J. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall, London, 1993.
8. H. El-shaarawi and W. Piegorsch, The Encyclopedia of Environmetrics, Wiley, West Sussex, England, 2001.
9. G.M. Fitzmaurice, N.M. Laird, and J.H. Ware, Applied Longitudinal Analysis, Wiley, Hoboken, New Jersey, 2004.
10. F. Hu and J.D. Kalbfleisch, The estimating function bootstrap, Can. J. Stat. 28 (2000), pp. 501-516.
11. N.M. Laird and J.H. Ware, Random effect models for longitudinal data, Biometrics 38 (1982), pp. 963-974.
12. K.Y. Liang and S.L. Zeger, Longitudinal data analysis using generalized linear models, Biometrika 73 (1986), pp. 13-22.
13. K.Y. Liang and S.L. Zeger, Inference based on estimating functions in the presence of nuisance parameters (with discussion), Statist. Sci. 10 (1995), pp. 15-199.
14. R.C. Littell, W.W. Stroup, and R.J. Freund, SAS for Linear Models 4th ed., SAS Institute Inc., Cary, NC, 2002.
15. R.C. Littell, G.A. Milliken, W.W. Stroup, R.D. Wolfinger, and O. Schabenberger, SAS for Mixed Models, 2nd ed., SAS Institute Inc., Cary, NC, 2002.
16. C. Lunneborg, Data Analysis by Re-sampling: Concepts and Applications, Duxbury Press, Belmont, CA, 2000.
17. B. Manly, Randomization, Bootstrap and Monte Carlo Methods in Biology, 3rd ed., Chapman & Hall, Boca Raton, Florida, 2007.
18. H. Roethig, B. Zedler, R. Kinser, S. Feng, B. Nelson, and Q. Liang, Short-term clinical exposure evaluation of a second-generation electrically heated cigarette smoking system, J. Clin. Pharmacol. 47 (2007), pp. 518-530.
19. SAS Institute Inc., SAS/STAT. User's Guide. Version 9.1, SAS Institute Inc., Cary, NC, 2006.
20. J.D. Singer, Using SAS PROC MIXED to fit multilevel models, hierarchical models, and growth models, J. Educ. Behav. Stat. 24 (1998), pp. 323-355.
21. J.D. Singer and J.B. Willett, Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence, Oxford University Press, NewYork, 2003.
22. G. Verbeke and G. Molenberghs, Linear Mixed Models for Longitudinal Data, Springer-Verlag, NewYork, 2000.
23. J.Yan and J. Fine, Estimating equations for association structures, Stat. Med. 23 (2004), pp. 859-874.