Joint modeling of longitudinal CD4 cell counts and time-to-default from HAART treatment: a comparison of separate and joint models


Abstract


In HAART and other follow-up clinical trials, both longitudinal and survival data are generated. Joint models are used to describe the joint behaviour of such data. This study has discussed Bayesian joint modeling approaches using a five years HAART data obtained from Jimma University Specialized Hospital HIV Outpatient Clinic, Ethiopia. The objective is to compare separate and joint models of longitudinal CD4 cells counts and default time processes of HIV/AIDS patients. A linear mixed effects model, assuming homogenous and heterogenous CD4 variances, is used for modeling the CD4 counts and a Weibull survival model is used for describing the default times. Then, both processes are linked using unobserved random effects through the use of a shared parameter model. The analysis of both the separate and the joint models reveal that the assumption of heterogenous (subject-specific) CD4 variances brings a substantial improvement in the model t. The parameter estimates of both the separate and joint models are consistent. However, the joint model is parsimonious and fits the data better. The final joint model relates the hazard of defaulting to two characteristics of the repeated CD4 counts; patient-specic slopes and CD4 variability.

DOI Code: 10.1285/i20705948v7n2p292

Keywords: Bayesian Analysis, Deviance Information Criteria (DIC), HAART Data, Joint Modeling, Longitudinal Data Analysis, Survival Data Analysis

References


. Gao F., Miller J. P., Miglior S., Beiser J. A., Torri V., Kass M. A. and Gordon M.O., (2011), A joint-modeling approach to assess the impact of biomarker variability on the risk of developing clinical outcomes, JP Journal of Biostatistics, 73-96.

. Guo, X. and Carlin, B.P. (2004). Separate and Joint modeling of longitudinal and event time data using standard computer packages. The American Statistician, Vol 58(1): 16-24.

. Henderson R., Diggle P. and Dobson A., (2000), Joint modeling of longitudinal measurements and event time data, Biostatistics 4, 465-480.

. Laird N. M. and Ware J. H., (1982), Random-effects models for longitudinal data, Biometrics 38, 963-974.

. Lyles R. H., Munõz A., Xu J., Taylor J. M. G. and Chmiel J. S., (1999), Adjusting for measurement error to assess health effects of variability in biomarkers, Stat.Med. 18, 1069-1086.

. Manatunga A., Schmotzer B., Lyles R. H., Small C., Guo Y. and Marcus M., (2005) Statistical issues related to modeling menstrual length, Proceedings of the American Statistical Association, Section on Statistics in Epidemiology,.

. Rizopoulos, D.( 2010), "JM: an R package for the joint modelling of longitudinal and time-to-event data", Journal of Statistical Software, vol. 35, no. 9, pp. 1-33.

. Sousa, I. (2011), "A Review on Joint Modelling of Longitudinal Measurements and Time-to-event", REV- STAT, vol. 9, no. 1, pp. 57-81.

. Spiegelhalter D.J., Best N.G., Carlin B.P. and Van der Linde A., (2002), Bayesian measures of model complexity and fit (with discussion), J. R. Stat. Soc. Ser. B 64, 583-616.

. Tsiatis, A. A., Degruttola, V., and Wulfsohn, M. S. (1995), “Modeling the Rela-tionship of Survival to Longitudinal Data Measured with Error, Applications to Survival and CD4 Counts in Patients with AIDS,” Journal of the American Statistical Association , 90, 27–37

. Wang, Y., and Taylor, J. M. G. (2001), “Jointly Modeling Longitudinal and Event Time Data with Application to Acquired Immunodeficiency Syndrome, ”Journal of the American Statistical Association, 96, 895–905.

. Wu, L. (2010), Mixed Effects Models for Complex Data, CRC Press

. Wulfsohn M. and Tsiatis A., (1997), A joint model for survival and longitudinal data measured with error, Biometrics 53, 330-339.


Full Text: pdf
Bookmark and Share


Creative Commons License
This work is licensed under a Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License.