The effect of longitudinal body weight and CD4 cell progression for the survival of HIV/AIDS patients

References

• Adams, M., & Luguterah, A. (2013). Longitudinal analysis of change in CD4+ cell counts of HIV-1 patients on antiretroviral therapy (ART) in the Builsa district hospital. European Scientific Journal, 9(33), 1857–7881. https://doi.org/https://doi.org/10.19044/esj.2013.v9n33p
   Google Scholar
• Ahunie, M. A., & Ebrahim, E. A. (2017). Mortality predictors of HIV-infected patients on antiretroviral therapy in Debre Tabor General Hospital and Woreta Health Center. South Gondar Zone, Northwest Ethiopia.
   Google Scholar
• Ayalew, J., Moges, H., & Worku, A. (2014). Identifying factors related to the survival of AIDS patients under the follow-up of antiretroviral therapy (ART): The case of South Wollo. International Journal of Data Envelopment Analysis and Operations Research, 1(2), 21–18. https://doi.org/https://doi.org/10.12691/ijdeaor-1-2-2
   Google Scholar
• Brennan, A. T., Maskew, M., Sanne, I., & Fox, M. P. (2013). The interplay between CD 4 cell count, viral load suppression and duration of antiretroviral therapy on mortality in a resource‐limited setting. Tropical Medicine & International Health, 18(5), 619–631. https://doi.org/https://doi.org/10.1111/tmi.12079
   PubMed Web of Science ®Google Scholar
• Collett, D. (2015). Modelling survival data in medical research. CRC press.
   Google Scholar
• Cox, D. R. (1984). Interaction. In International statistical review/Revue Internationale de Statistique (pp. 1–24).  International Statistical Institute (ISI). https://www.jstor.org/stable/1403234
   Google Scholar
• Damtew, B., Mengistie, B., & Alemayehu, T. (2015). Survival and determinants of mortality in adult HIV/Aids patients initiating antiretroviral therapy in Somali Region, Eastern Ethiopia. Pan African Medical Journal, 22(1), 1937–8688. https://doi.org/https://doi.org/10.11604/pamj.2015.22.138.4352
   Google Scholar
• Egger, S., Petoumenos, K., Kamarulzaman, A., Hoy, J., Sungkanuparph, S., Chuah, J., Falster, K., Zhou, J., & Law, M. G. (2009). Long-term patterns in CD4 response is determined by an interaction between baseline CD4 cell count, viral load and time: The Asia Pacific HIV Observational Database (APHOD). Journal of Acquired Immune Deficiency Syndromes, 50(5), 513. https://doi.org/https://doi.org/10.1097/QAI.0b013e31819906d3
   PubMed Web of Science ®Google Scholar
• Farhadian, M., Mohammadi, Y., Mirzaei, M., & Shirmohammadi-Khorram, N. (2021). Factors related to baseline CD4 cell counts in HIV/AIDS patients: Comparison of poisson, generalized poisson and negative binomial regression models. BMC Research Notes, 14(1), 1–7. https://doi.org/https://doi.org/10.1186/s13104-021-05523-w
   PubMed Web of Science ®Google Scholar
• Florence, E., Lundgren, J., Dreezen, C., Fisher, M., Kirk, O., Blaxhult, A., Panos, G., Katlama, C., Vella, S., & Phillips, A. (2003). Factors associated with a reduced CD4 lymphocyte count response to HAART despite full viral suppression in the EuroSIDA study. HIV Medicine, 4(3), 255–262. https://doi.org/https://doi.org/10.1046/j.1468-1293.2003.00156.x
   PubMedGoogle Scholar
• Gezae, K. E., Abebe, H. T., & Gebretsadik, L. G. (2019). Incidence and predictors of LTFU among adults with TB/HIV co-infection in two governmental hospitals, Mekelle, Ethiopia, 2009–2016: Survival model approach. BMC Infectious Diseases, 19(1), 1–9. https://doi.org/https://doi.org/10.1186/s12879-019-3756-2
   PubMedGoogle Scholar
• Grover, G., Swain, P., Deo, V., & Varshney, M. (2015). A joint modeling approach to assess the impact of CD4 cell count on the risk of loss to follow up in HIV/AIDS patients on antiretroviral therapy. International Journal of Statistics and Applications, 5(3), 99–108. https://doi.org/https://doi.org/10.5923/j.statistics.20150503.01
   Google Scholar
• Hickey, G. L., Philipson, P., Jorgensen, A., & Kolamunnage-Dona, R. (2016). Joint modelling of time-to-event and multivariate longitudinal outcomes: Recent developments and issues. BMC Medical Research Methodology, 16(1), 1–15. https://doi.org/https://doi.org/10.1186/s12874-016-0212-5
   PubMedGoogle Scholar
• Hughes, R., Sterne, J., Walsh, J., Bansi, L., Gilson, R., Orkin, C., Hill, T., Ainsworth, J., Anderson, J., & Gompels, M. (2011). Long‐term trends in CD4 cell counts and impact of viral failure in individuals starting antiretroviral therapy: UK Collaborative HIV Cohort (CHIC) study. HIV Medicine, 12(10), 583–593. https://doi.org/https://doi.org/10.1111/j.1468-1293.2011.00929.x
   PubMed Web of Science ®Google Scholar
• Kaufmann, G. R., Perrin, L., Pantaleo, G., Opravil, M., Furrer, H., Telenti, A., Hirschel, B., Ledergerber, B., Vernazza, P., & Bernasconi, E. (2003). CD4 T-lymphocyte recovery in individuals with advanced HIV-1 infection receiving potent antiretroviral therapy for 4 years: The Swiss HIV Cohort Study. Archives of Internal Medicine, 163(18), 2187–2195. https://doi.org/https://doi.org/10.1001/archinte.163.18.2187
   PubMedGoogle Scholar
• Kempthorne, O. (1952). The design and analysis of experiments. Journal of the Royal Statistical Society. Series C (Applied Statistics). https://doi.org/https://doi.org/10.2307/2985331
   Google Scholar
• Mengesha, S., Belayihun, B., & Kumie, A. (2014). Predictors of survival in HIV-infected patient after Initiation of HAART in Zewditu Memorial Hospital, Addis Ababa, Ethiopia. International Scholarly Research Notices, 2014, 1–6. https://doi.org/https://doi.org/10.1155/2014/250913
   Google Scholar
• Reda, A. A., Biadgilign, S., Deribew, A., Gebre, B., & Deribe, K. (2013). Predictors of change in CD4 lymphocyte count and weight among HIV infected patients on anti-retroviral treatment in Ethiopia: A retrospective longitudinal study. PLoS One, 8(4), e58595. https://doi.org/https://doi.org/10.1371/journal.pone.0058595
   PubMed Web of Science ®Google Scholar
• Seid, A., Getie, M., Birlie, B., & Getachew, Y. (2014). Joint modeling of longitudinal CD4 cell counts and time-to-default from HAART treatment: A comparison of separate and joint models. Electronic Journal of Applied Statistical Analysis, 7(2), 292–314. https://doi.org/https://doi.org/10.1285/i20705948v7n2p292
   Google Scholar
• Seyoum, A., & Temesgen, Z. (2017). Joint longitudinal data analysis in detecting determinants of CD4 cell count change and adherence to highly active antiretroviral therapy at Felege Hiwot Teaching and Specialized Hospital, North-west Ethiopia (Amhara Region). AIDS Research and Therapy, 14(1), 1–13. https://doi.org/https://doi.org/10.1186/s12981-017-0141-3
   PubMedGoogle Scholar
• Tadege, M. (2018). Time to death predictors of HIV/AIDS infected patients on antiretroviral therapy in Ethiopia. BMC Research Notes, 11(1), 1–6. https://doi.org/https://doi.org/10.1186/s13104-018-3863-y
   PubMedGoogle Scholar
• Tegegne, A. S. (2021). Predictors associated with the variation of CD4 cell count and Body Mass Index (BMI) for HIV positive adults under ART. Scientific African, 12, e00820. https://doi.org/https://doi.org/10.1016/j.sciaf.2021.e00820
   Google Scholar
• Temesgen, A., & Kebede, T. (2016). Joint modeling of longitudinal CD4 count and weight measurements of HIV/tuberculosis co-infected patients at Jimma University specialized hospital. Annals of Data Science, 3(3), 321–338. https://doi.org/https://doi.org/10.1007/s40745-016-0085-9
   Google Scholar
• UNAIDS. (2016). Global AIDS update: Enormous gains, persistent challenges. https://www.unaids.org
   Google Scholar
• UNAIDS. (2018). UNAIDS/AIDS REPORT. Springer. p. 7–10. https://www.unaids.org
   Google Scholar
• WHO. (2017). Global Health Observatory (GHO) data, HIV/AIDS: global situation and trend. Retrieved 15 October, 2017, from http://www.who.int/gho/hiv/en/
   Google Scholar