[1] | Lim et al. 2013, Joint modeling of longitudinal and event time data: application to HIV study, Journal of Medical Statistics and Informatics, 1:1.http://dx.doi.org/10.7243/2053-7662-1-1. |
[2] | Prentice, R. L, (1982) Covariate measurement errors and parameter estimation in a failure time regression model, Biometrika, 69, 331-342. |
[3] | Tsiatis AA, De Gruttola V and Wulfsohn MS. Modeling the relationship of survival to longitudinal data measured with error: applications to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association 1995; 90:27–37. |
[4] | Wulfsohn MS and Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics. 1997; 53:330-9. |
[5] | Tsiatis AA and Davidian M. Joint modeling of longitudinal and time-toevent data: an overview. Statistica Sinica. 2004; 14:809–834. |
[6] | De Gruttola V and Tu XM. Modelling progression of CD4-lymphocyte count and its relationship to survival time. Biometrics. 1994; 50:1003-14. |
[7] | Henderson R, Diggle P and Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics. 2000; 1:465-80. |
[8] | Wang Y and Taylor JMG. Jointly modeling longitudinal and event time data with application to acquired immunodeficiency syndrome. Journal of the American Statistical Association 2001; 96:895–905. |
[9] | Ding and Wang, Modeling Longitudinal Data with Nonparametric Multiplicative Random Effects Jointly with Survival Data, Biometrics. 2008; 64(2): 546–556. doi:10.1111/j.1541-0420.2007.00896.x. |
[10] | Nathoo F. S and C. B. Dean, “Spatial multistate transitional models for longitudinal event data,” Biometrics, vol. 64, no. 1, p. 271–279, 326, 2008. |
[11] | W. Ye, X. Lin, and J. M. G. Taylor, “Semiparametric modeling of longitudinal measurements and time to- event data-a two-stage regression calibration approach,” Biometrics, vol. 64, no. 4, pp. 1238–1246, 2008. |
[12] | P. S. Albert and J. H. Shih, “On estimating the relationship between longitudinal measurements and time-to-event data using a simple two-stage procedure,” Biometrics, vol. 66, no. 3, pp. 983–987, 2010. |
[13] | H. Jacqmin-Gadda, C. Proust-Lima, J. M. G. Taylor, and D. Commenges, “Score test for conditional independence between longitudinal outcome and time to event given the classes in the joint latent class model,” Biometrics, vol. 66, no. 1, pp. 11–19, 2010. |
[14] | Rizopoulos, D. (2012) Joint Models for Longitudinal and Time-to-Event Data: with Applications in R. Boca Raton: Chapman and Hall/CRC. |
[15] | Wu et al. (2010), L. Wu, W. Liu, and X. J. Hu, “Joint inference on HIV viral dynamics and immune suppression in presence of measurement errors,” Biometrics, vol. 66, no. 2, pp. 327–335, 2010. |
[16] | Y. Huang, G. Dagne, and L. Wu, “Bayesian inference on joint models of HIV dynamics for time-to-event and longitudinal data with skewness and covariate measurement errors,” Statistics in Medicine, vol. 30, no. 24, pp. 2930–2946, 2011. |
[17] | Yi- Kuang Tseng et al. A joint modeling approach for evaluating the efficacy of HAART treatment for AIDS patients in Taiwan. |
[18] | Rui Martins, Silva G.L, and Andreozzi. V. (2012), Spatial joint analysis of longitudinal and survival AIDS data in Brazil. |
[19] | Y. K. Tseng, F. Hsieh, and J. L. Wang, “Joint modelling of accelerated failure time and longitudinal data,” Biometrika, vol. 92, no. 3, pp. 587–603, 2005. |
[20] | Thiebaut R, et al. Joint modelling of bivariate longitudinal data with informative dropout and left-censoring, with application to the evolution of CD4+ cell count and HIV RNA viral load in response to treatment of HIV infection, Statist. Med. 2005; 24:65–82. |
[21] | Martin WG Brinkhof , François Dabis , Landon Myer et al., WHO: Early loss of HIV-infected patients on potent antiretroviral therapy programmes in lower income countries, http://www.who.int/bulletin/volumes/86/7/07-044248/en/. |
[22] | Poppa et al. 2003, British HIV Association (BHIVA)/British Association for sexual health and HIV guidelines on provision of adherence support to individuals receiving antiretroviral therapy. HIV Medicine, 5, 46-60. |
[23] | Gerardo Alvarez uria et al. 2013, Predictors of delayed antiretroviral therapy initiation, mortality and loss to follow up in HIV infected patients eligible for HIV treatment: Data from an HIV cohort study in India, BioMed Research International, 2013. |
[24] | Nelson K and Williams CM. Infectious Disease Epidemiology. Theory and Practice. 2nd ed. 2007. |
[25] | Langford SE, Ananworanich J and Cooper DA. Predictors of disease progression in HIV infection: a review. AIDS Res Ther. 2007; 4:11. |
[26] | Laird, N.M. and Ware, J.H. (1982). Random-effects models for longitudinal data. Biometrics 38, 963 – 974. |
[27] | Dalal RP, Macphail C, Mqhayi M, Wing J, Feldman C, et al. (2008) Characteristics and outcomes of adult patients lost to follow-up at an antiretroviral treatment clinic in Johannesburg, South Africa. J Acquir Immune Defic Syndr 47: 101–107. |
[28] | Geng EH, Emenyonu N, Bwana MB, Glidden DV, Martin JN (2008) Sampling based approach to determining outcomes of patients lost to follow-up in antiretroviral therapy scale-up programs in Africa. JAMA 300: 506–507. |
[29] | S. M. Ingle, M. May, K. Uebel et al., “Outcomes in patients waiting for antiretroviral treatment in the Free State Province, South Africa: prospective linkage study,” AIDS, vol. 24, no. 17, pp. 2717–2725, 2010. |
[30] | E. H. Geng, M. B. Bwana, W. Muyindike et al., “Failure to initiate antiretroviral therapy, loss to follow-up and mortality among HIV-infected patients during the Pre-ART period in Uganda,” Journal of Acquired Immune Deficiency Syndromes, vol. 63, no.2, pp. e64–e71, 2013. |
[31] | Bygrave H, Kranzer K, Hilderbrand K, Whittall J, Jouquet G, et al. (2010) Trends in Loss to Follow-Up among Migrant Workers on Antiretroviral Therapy in a Community Cohort in Lesotho. PLoS ONE 5(10): e13198.doi:10.1371/journal.pone.0013198. |