Assessing the association between trends in a biomarker and risk of event with an application in pediatric HIV/AIDS

Annals of Applied Statistics

Volumn 3, Issue 3, 2009, Pages 1163-1182

  Brown, Elizabeth R ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Biomarker change; Disease progression; HIV AIDS; Joint longitudinal and survival models; Mother to child transmission

Indexed keywords

EID: 84865394029     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/09-AOAS251     Document Type: Article

References (24)

1. Brown, E. R., Ibrahim, J. G. and DeGruttola, V. (2005). A flexible B-spline model for multiple longitudinal biomarkers and survival. Biometrics 61 64-73.
2. Chen, M.-H., Shao, Q.-M. and Ibrahim, J. G. (2000). Monte Carlo Methods in Bayesian Computation. Springer-Verlag, New York.
3. de Boor, C. (2001). A Practical Guide to Splines (revised ed.). Springer-Verlag, New York.
4. Faucett, C. L. and Thomas, D. C. (1996). Simultaneously modelling censored survival data and repeatedly measured covariates: A Gibbs sampling approach. Statist. Med. 15 1663-1685.
5. Gelfand, A. E., Dey, D. K. and Chang, H. (1992). Model determination using predictive distributions with implementation via sampling-based methods (with discussion). In Bayesian Statistics 4 (J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith, eds.). Oxford University Press, Oxford.
6. Gelfand, A. E. and Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. J. Amer. Statist. Assoc. 85 398-409.
7. Gilks, W. R. and Wild, P. (1992). Adaptive rejection sampling for Gibbs sampling. Appl. Statist. 41 337-348.
8. Guay, L., Musoke, P., Fleming, T., Bagenda, D., Allen, M., Nakabiito, C., Sherman, J., Bakaki, P., Ducar, C., Deseyve, M., Emel, L., Mirochnick, M., Fowler, M., Mofenson, L., Miotti, P., Dransfield, K., Bray, D., Mmiro, F. and Jackson, J. (1999). Intrapartum and neonatal single-dose nevirapine compared with zidovudine for prevention of mother-to-child transmission of HIV-1 in Kampala, Uganda: HIVNET 012 randomised trial. Lancet 354795-802.
9. Han, J., Slate, E. H. and Pena, E. A. (2007). Parametric latent class joint model for a longitudinal biomarker and recurrent events. Statist. Med. 265285-5302.
10. Heagerty, P. J., Lumley, T. and Pepe, M. S. (2000). Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56337-344.
11. Jackson, J., Musoke, P., Fleming, T., Guay, L., Bagenda, D., Allen, M., Nakabiito, C., Sherman, J., Bakaki, P., Owor, M., Ducar, C., Deseyve, M., Mwatha, A., Emel, L., Duefield, C., Mirochnick, M., Fowler, M., Mofenson, L., Miotti, P., Gigliotti, M., Bray, D. and Mmiro, F. (2003). Intrapartum and neonatal single-dose nevirapine compared with zidovudine for prevention of mother-to-child transmission of HIV-1 in Kampala, Uganda: 18-month follow-up of the HIVNET 012 randomised trial. Lancet 362 859-868.
12. Lin, H., Turnbull, B. W., McCulloch, C. E. and Slate, E. H. (2002). Latent class models for joint analysis of longitudinal biomarker and event process data: Application to longitudinal prostate-specific antigen readings and prostate cancer. J. Amer. Statist. Assoc. 97.
13. Mellors, J. W., Margolick, J. B., Phair, J. P., Rinaldo, C. R., Detels, R., Jacobson, L. P. and Munoz, A. (2007). Prognostic value of HIV-1 RNA, CD4 cell count, and CD4 cell count slope for progression to AIDS and death in untreated HIV-1 infection. J. Amer. Med. Assoc. 297 2349-2350.
14. Neal, R. M. (2003). Slice sampling. Ann. Statist. 31 705-767.
15. Plummer, M., Best, N., Cowles, K. and Vines, K. (2006). CODA: Convergence diagnosis and output analysis for MCMC. R News 6 7-11. Available at http://CRAN.R-project.org/doc/Rnews/.
16. Proust-Lima, C., Joly, P., Dartigues, J. and Jacqmin-Gadda, H. (2009). Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach. Comput. Statist. Data Anal. 53 1142-1154.
17. Prentice, R. L. (1982). Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika 69 331-342.
18. Proust-Lima, C., Letenneur, L. and Jacqmin-Gadda, H. (2007). A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome. Statist. Med. 26 2229-2245.
19. R Development Core Team (2006). R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Available at http://www.R-project.org.
20. Spiegelhalter, D. J., Best, N. G., Carlin, B. P. and van der Linde, A. (2002). Bayesian measures of complexity and fit. J. Roy. Statist. Soc. Ser. B 64583-639.
21. Tsiatis, A. and Davidian, M. (2004). An overview of joint modeling of longitudinal and time-to-event data. Statist. Sinica 14 793-818.
22. Wulfsohn, M. S. and Tsiatis, A. A. (1997). A joint model for survival and longitudinal data measured with error. Biometrics 53 330-339.
23. Ye, W., Lin, X. and Taylor, J. M. G. (2008). Semiparametric modeling of longitudinal measurements and time-to-event data-A two-stage regression calibration approach. Biometrics 64 1238-1246.
24. Yu, M., Taylor, J. M. G. and Sandler, H. M. (2008). Individual prediction in prostate cancer studies using a joint longitudinal survival cure model. J. Amer. Statist. Assoc. 103 178-187.