A Comparative Study of Different Joint Modeling Approaches for HIV/AIDS Patients in Southern Iran
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Abstract
Background: The prevalence of HIV/AIDS has been increasing in Iran, especially amongst the young population, recently. The joint model (JM) is a statistical method that represents an effective strategy to incorporate all information of repeated measurements and survival outcomes simultaneously. In many theoretical studies, the population under the study were heterogeneous. This study aimed at comparing three approaches by considering heterogeneity in the patients.
Methods: This study was conducted on 750 archived files of patients infected with HIV in Fars Province, southern Iran, from 1994 to 2017. Proposed Approach (PA), Joint Latent Class Models (JLCM), and Separated Approach (SA) were compared to evaluate the influence covariates on the longitudinal and time-to-event outcomes in the heterogeneous HIV/AIDS patients.
Results: Gender (P<0.001) and HCV (P<0.01) were two significant covariates in the classification of HIV/AIDS patients. Time had a significant effect on CD4 (P<0.001) in both classes in the three approaches. In PA and SA, females had higher CD4 than males (P<0.001) in the first class. In JLCM, females had higher CD4 than males (P<0.01) in both classes. The patients with higher Hgb had also higher CD4 (P<0.001) in both classes in the three approaches. HCV reduced the CD4 significantly in both classes in PA (P<0.05) and SA (P<0.001). Within the survival sub-model, HCV reduced survival rate significantly in the second class in PA (P<0.05), JLCM (P<0.01) and SA (P<0.001).
Conclusion: PA was an appropriate approach for joint modeling longitudinal and survival outcomes for this heterogeneous population.
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17. Proust-Lima C, Taylor JM (2009). Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach. Biostatistics, 10:535-49.
18. Roustaei N, Ayatollahi SMT, Zare N (2017). A proposed approach for joint modeling of the longitudinal and time-to-event data in heterogeneous populations: An application to HIV/AIDS's disease. BioMed Research International, 2018:13.
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23. Zhang JY, Zhang Z, Lin F, et al (2010). Interleukin‐17–producing CD4+ T cells increase with severity of liver damage in patients with chronic hepatitis B. Hepatology, 51:81-91.
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25. Chun HM, Roediger MP, Hullsiek KH, et al (2012). Hepatitis B virus coinfection negatively impacts HIV outcomes in HIV seroconverters. J Infect Dis, 205(2):185-93.
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28. Delobel P, Sandres-Sauné K, Cazabat M, et al (2005). R5 to X4 switch of the predominant HIV-1 population in cellular reservoirs during effective highly active antiretroviral therapy. J Acquir Immune Defic Syndr, 38:382-92.
29. Brumme ZL, Brumme CJ, Chui C, et al (2007). Effects of human leukocyte antigen class I genetic parameters on clinical outcomes and survival after initiation of highly active antiretroviral therapy. The Journal of Infectious Diseases, 195:1694-1704.
30. Wu L, Liu W, Yi GY, Huang Y (2012). Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues. J Probab Stat, 2012:1-17.
31. Tseng Y-K, Hsieh F, Wang J-L (2005). Joint modelling of accelerated failure time and longitudinal data. Biometrika, 92:587-603.
32. Ibrahim JG, Chu H, Chen LM (2010). Basic concepts and methods for joint models of longitudinal and survival data. J Clin Oncol, 28:2796-2801.
33. Guo X, Carlin BP (2004). Separate and joint modeling of longitudinal and event time data using standard computer packages. Am Stat, 58:16-24.
2. National AIDS Committee Secretariat (2015). Ministry of Health and Medical Education, Islamic Republic of Iran AIDS Progress Report; On Monitoring of the United Nations General Assembly Special Session on HIV and AIDS.
3. Bengtson AM, Pence BW, O'Donnell J, et al (2015). Improvements in depression and changes in quality of life among HIV-infected adults. AIDS Care, 27:47-53.
4. Rai Y, Dutta T, Gulati AK (2010). Quality of life of HIV-infected people across different stages of infection. J Happiness Stud, 11:61-69.
5. Mellors JW, Munoz A, Giorgi JV, et al (1997). Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV-1 infection. Ann Intern Med, 126:946-954.
6. Lichtenstein KA, Armon C, Buchacz K, et al (2008). Initiation of antiretroviral therapy at CD4 cell counts≥ 350 cells/mm3 does not increase incidence or risk of peripheral neuropathy, anemia, or renal insufficiency. JAIDS Journal of Acquired Immune Deficiency Syndromes, 47:27-35.
7. Brombin C, Di Serio C, Rancoita PM (2016). Joint modeling of HIV data in multicenter observational studies: A comparison among different approaches. Stat Methods Med Res, 25:2472-2487.
8. Rizopoulos D (2012). Joint models for longitudinal and time-to-event data: With applications in R. ed. CRC Press, Florida, USA.
9. Chen Q, May RC, Ibrahim JG, et al (2014). Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates. Stat Med, 33:4560-76.
10. Faucett CL, Thomas DC (1996). Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach. Stat Med, 15:1663-1685.
11. Wulfsohn MS, Tsiatis AA (1997). A joint model for survival and longitudinal data measured with error. Biometrics:330-9.
12. Tsiatis AA, Davidian M (2004). Joint modeling of longitudinal and time-to-event data: an overview. Stat Sin, 14:809-834.
13. Proust-Lima C, Sene M, Taylor JM, Jacqmin-Gadda H (2014). Joint latent class models for longitudinal and time-to-event data: a review. Stat Methods Med Res, 23:74-90.
14. Liu Y, Liu L, Zhou J (2015). Joint latent class model of survival and longitudinal data: An application to CPCRA study. Computational Statistics and Data Analysis , 91:40-50.
15. Lin H, Turnbull BW, McCulloch CE, Slate EH (2002). Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data. J Am Stat Assoc, 97:53-65.
16. Jacqmin‐Gadda H, Proust‐Lima C, Taylor JM, Commenges D (2010). Score test for conditional independence between longitudinal outcome and time to event given the classes in the joint latent class model. Biometrics, 66:11-19.
17. Proust-Lima C, Taylor JM (2009). Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach. Biostatistics, 10:535-49.
18. Roustaei N, Ayatollahi SMT, Zare N (2017). A proposed approach for joint modeling of the longitudinal and time-to-event data in heterogeneous populations: An application to HIV/AIDS's disease. BioMed Research International, 2018:13.
19. Han J, Slate EH, Pena EA (2007). Parametric latent class joint model for a longitudinal biomarker and recurrent events. Stat Med, 26:5285-5302.
20. Liu L, Huang X (2009). Joint analysis of correlated repeated measures and recurrent events processes in the presence of death, with application to a study on acquired immune deficiency syndrome. J R Stat Soc Ser C Appl Stat, 58:65-81.
21. Sajadi MM, Pulijala R, Redfield RR, Talwani R (2012). Chronic immune activation and decreased CD4 counts associated with Hepatitis C Infection in HIV-1 Natural Viral Suppressors. AIDS, 26:1879-1884.
22. Lim HJ, Mondal P, Skinner S (2013). Joint modeling of longitudinal and event time data: application to HIV study. Journal of Medical Statistics and Informatics, 1(1):1.
23. Zhang JY, Zhang Z, Lin F, et al (2010). Interleukin‐17–producing CD4+ T cells increase with severity of liver damage in patients with chronic hepatitis B. Hepatology, 51:81-91.
24. Branch AD, Van Natta ML, Vachon M-L, et al (2012). Mortality in hepatitis C virus–infected patients with a diagnosis of AIDS in the era of combination antiretroviral therapy. Clin Infect Dis, 55:137-144.
25. Chun HM, Roediger MP, Hullsiek KH, et al (2012). Hepatitis B virus coinfection negatively impacts HIV outcomes in HIV seroconverters. J Infect Dis, 205(2):185-93.
26. Peters PJ, Marston BJ (2011). Preventing deaths in persons with HIV/hepatitis B virus coinfection: a call to accelerate prevention and treatment efforts. (ed)^(eds), Oxford University Press,
27. Rezaianzadeh A, Abbastabar H, Rajaeefard A, et al (2017). Determinant factors of survival time in a cohort study on HIV patient using by time-varying cox model: Fars province, south of Iran. IJER, 4:145-155.
28. Delobel P, Sandres-Sauné K, Cazabat M, et al (2005). R5 to X4 switch of the predominant HIV-1 population in cellular reservoirs during effective highly active antiretroviral therapy. J Acquir Immune Defic Syndr, 38:382-92.
29. Brumme ZL, Brumme CJ, Chui C, et al (2007). Effects of human leukocyte antigen class I genetic parameters on clinical outcomes and survival after initiation of highly active antiretroviral therapy. The Journal of Infectious Diseases, 195:1694-1704.
30. Wu L, Liu W, Yi GY, Huang Y (2012). Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues. J Probab Stat, 2012:1-17.
31. Tseng Y-K, Hsieh F, Wang J-L (2005). Joint modelling of accelerated failure time and longitudinal data. Biometrika, 92:587-603.
32. Ibrahim JG, Chu H, Chen LM (2010). Basic concepts and methods for joint models of longitudinal and survival data. J Clin Oncol, 28:2796-2801.
33. Guo X, Carlin BP (2004). Separate and joint modeling of longitudinal and event time data using standard computer packages. Am Stat, 58:16-24.
| Files | ||
| Issue | Vol 49 No 9 (2020) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijph.v49i9.4099 | |
| PMCID | PMC7898094 | |
| PMID | 33643954 | |
| Keywords | ||
| Longitudinal studies; Survival analysis HIV Iran | ||
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How to Cite
1.
ROUSTAEI N, JAMALI J, TAGHI AYATOLLAHI SM, ZARE N. A Comparative Study of Different Joint Modeling Approaches for HIV/AIDS Patients in Southern Iran. Iran J Public Health. 2020;49(9):1776-1786.
