The Effect of MSM and CD4+ Count on the Development of Cancer AIDS (AIDS-defining Cancer) and Non-cancer AIDS in the HAART Era

Author(s): Prosanta Mondal, Hyun J. Lim*, OHTN Cohort Study Team

Journal Name: Current HIV Research

Volume 16 , Issue 4 , 2018

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Graphical Abstract:


Background: The HIV epidemic is increasing among Men who have Sex with Men (MSM) and the risk for AIDS defining cancer (ADC) is higher among them.

Objective: To examine the effect of MSM and CD4+ count on time to cancer AIDS (ADC) and noncancer AIDS in competing risks setting in the HAART era.

Method: Using Ontario HIV Treatment Network Cohort Study data, HIV-positive adults diagnosed between January 1997 and October 2012 having baseline CD4+ counts ≤ 500 cells/mm3 were evaluated. Two survival outcomes, cancer AIDS and non-cancer AIDS, were treated as competing risks. Kaplan-Meier analysis, Cox cause-specific hazards (CSH) model and joint modeling of longitudinal and survival outcomes were used.

Results: Among the 822 participants, 657 (79.9%) were males; 686 (83.5%) received anti-retroviral (ARV) ever. Regarding risk category, the majority (58.5%) were men who have Sex with men (MSM). Mean age was 37.4 years (SD = 10.3). In the multivariate Cox CSH models, MSM were not associated with cancer AIDS but with non-cancer AIDS [HR = 2.92; P = 0.055, HR = 0.54; P = 0.0009, respectively]. However, in joint models of longitudinal and survival outcomes, MSM were associated with cancer AIDS but not with non-cancer AIDS [HR = 3.86; P = 0.013, HR = 0.73; P = 0.10]. CD4+ count, age, ARV ever were associated with both events in the joint models.

Conclusion: This study demonstrates the importance of considering competing risks, and timedependent biomarker in the survival model. MSM have higher hazard for cancer AIDS. CD4+ count is associated with both survival outcomes.

Keywords: AIDS-defining cancer, MSM, cause-specific hazard, joint model, competing risks, HAART.

Beyrer C, Baral SD. Griensven Fv, et al Global epidemiology of HIV infection in men who have sex with men. Lancet 2012; 380(9839): 367-77.
CDC. Prevalence and awareness of HIV infection among men who have sex with men—21 cities, United States, 2008. MMWR Morb Mortal Wkly Rep 2010; 59: 1201-7.
Sullivan P, Hamouda O, Delpech V, et al. Reemergence of the HIV epidemic among men who have sex with men in North America, western Europe, and Australia, 1996–2005. Ann Epidemiol 2009; 19: 423-31.
Bourgeois AC, Edmunds M, Awan A, Jonah L, Varsaneux O, Siu W. HIV in Canada - Surveillance report, 2016. Can Commun Dis Rep 2017; 43(12): 248-55.
Opportunistic Infections What Are Opportunistic Infections? May 15, 2017 Available at https: // Accessed April 08, 2018
Appendix A. AIDS-Defining Conditions December 05, 2008 Available at http: // mmwrhtml/ rr5710a2.htm Accessed on April 08, 2018.
Ancelle-Park R, Klein JP, Stroobant A, et al. Expanded European AIDS case definition. Lancet 1993; 341(8842): 441.
Castro KG, Ward JW, Slutsker L, et al. 1993 revised classification system for HIV infection and expanded surveillance case definition for AIDS among adolescents and adults. CID 1993; 17.
Ebrahim SH, Abdullah AS, McKenna M, Hamers FF. AIDS defining cancers in Western Europe, 1994–2001. AIDS Patient Care STDS 2004; 18(9): 501-8.
Shiels MS, Pfeiffer RM, Gail MH, et al. Cancer burden in the hiv-infected population in the United States. J Natl Cancer Inst 2011; 103: 753-62.
Spano J-P, Costagliola D, Katlama C, Mounier N, Oksenhendler E, Khayat D. AIDS-related malignancies: State of the art and therapeutic challenges. J Clin Oncol 2008; 26(29): 4834-42.
Silverberg MJ, Lau B, Achenbach CJ, et al. Cumulative incidence of cancer among hiv-infected individuals in north america. Ann Intern Med 2015; 163(7): 507-18.
Simard EP, Pfeiffer RM, Engels EA. Cumulative incidence of cancer among people with AIDS in the United States. Cancer 2011; 117(5): 1089-96.
Hernández-Ramírez RU, Shiels MS, Dubrow R, Engels EA. Cancer risk in HIV-infected people in the USA from 1996 to 2012: a population-based, registry-linkage study. Lancet HIV 2017; 4(11): e495-504.
Shiels MS, Cole SR, Wegner S, et al. Effect of HAART on incident cancer and noncancer AIDS events among male HIV seroconverters. J Acquir Immune Defic Syndr 2008; 48(4): 485-90.
Gingues S, Gill MJ. The impact of highly active antiretroviral therapy on the incidence and outcomes of AIDS-defining cancers in Southern Alberta. HIV Med 2006; 7(6): 369-77.
Chiu CG, Smith D, Salters KA, et al. Overview of cancer incidence and mortality among people living with HIV/AIDS in British Columbia, Canada: Implications for HAART use and NADM development. BMC Cancer 2017; 17(1): 270.
Guo X, Carlin BP. Separate and joint modeling of longitudinal and event time data using standard computer packages. Am Stat 2004; 58(1): 16-24.
Henderson R, Diggle P, Dobson A. Joint modeling of longitudinal measurements and event time data. Biostatistics 2000; 1(4): 465-80.
Ibrahim JG, Chu H, Chen LM. Basic concepts and methods for joint models of longitudinal and survival data. J Clin Oncol 2010; 28(26): 2796-801.
Lim HJ, Mondal P, Skinner S. Joint modeling of longitudinal and event time data: application to HIV study. Int J Med Inform 2013; 1: 1.
Wu L, Liu W, Yi GY, Huang Y. Analysis of longitudinal and survival data: Joint modeling, inference methods, and issues. Journal of Probability and Statistics. 2012.
Gould AL, Boye ME, Crowther MJ, et al. Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group. Stat Med 2015; 34(14): 2181-95.
Rizopoulos D. Joint Models for Longitudinal and Time-to-Event Data With Applications in R. FL: Chapman & Hall/CRC Biostatistics Series 2012.
Wang P, Shen W, Boye ME. Joint modeling of longitudinal outcomes and survival using latent growth modeling approach in a mesothelioma trial. Health Serv Outcomes Res Methodol 2012; 12(2-3): 182-99.
Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics 1997; 53(91): 330-9.
Prentice RL, Kalbfleisch JD, Peterson AV, Flournoy N, Farewell VT, Breslow NE. The analysis of failure times in the presence of competing risks. Biometrics 1978; 34(4): 541-54.
Shiels MS, Cole SR, Chmiel JS, et al. A comparison of ad hoc methods to account for non-cancer AIDS and deaths as competing risks when estimating the effect of HAART on incident cancer AIDS among HIV-infected men. J Clin Epidemiol 2010; 63(4): 459-67.
Deslandes E, Chevret S. Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data. BMC Med Res Methodol 2010; 10: 69.
Elashoff RM, Li G, Li N. An approach to joint analysis of longitudinal measurements and competing risks failure time data. Stat Med 2007; 26(14): 2813-35.
Elashoff RM, Li G, Li N. A joint model for longitudinal measurements and survival data in the presence of multiple failure types. Biometrics 2008; 64(3): 762-71.
Hu WH, Li G, Li N. A Bayesian approach to joint analysis of longitudinal measurements and competing risks failure time data. Stat Med 2009; 28(11): 1601-19.
Li N, Elashoff RM, Li G. Robust joint modeling of longitudinal measurements and competing risks failure type data. Biom J 2009; 51(1): 19-30.
Li N, Elashoff RM, Li G, Tseng CH. Joint analysis of bivariate longitudinal ordinal outcomes and competing risks survival times with nonparametric distributions for random effects. Stat Med 2012; 31(16): 1707-21.
Williamson PR, Kolamunnage-Dona R, Philipson P, Marson AG. Joint modeling of longitudinal and competing risks data. Stat Med 2008; 27(30): 6426-38.
Touloumi G, Pantazis N, Babiker AG, et al. Differences in HIV RNA levels before the initiation of antiretroviral therapy among 1864 individuals with known HIV-1 seroconversion dates. AIDS 2004; 18(12): 1697705.
McMurchy D, Challacombe L, Edmiston M, et al. Gaining trust, ensuring security: the evolution of an Ontario HIV cohort study. In: Flood CM (ed.), Data Data Everywhere: Access and Accountability? Montreal and Kingston: Queen’s Policy Studies Series, McGill-Queen’s University Press, 2010, pp. 1-28
Rourke SB, Gardner S, Burchell AN, et al. Cohort profile: the ontario hiv treatment network cohort study (OCS). Int J Epidemiol 2013; 42(2): 402-11.
Gooley TA, Leisenring W, Crowley J, Storer BE. Estimation of failure probabilities in the presence of competing risks: new representations of old estimators. Stat Med 1999; 18(6): 695-706.
Cox DR. Regression models and life-tables (with discussion). J R Stat Soc 1972; 34(2): 187-220.
Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc 1958; 53(282): 457-81.
Latouche A, Boisson V, Chevret S, Porcher R. Misspecified regression model for the subdistribution hazard of a competing risk. Stat Med 2007; 26: 965-74.
Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics 1982; 38(4): 963-74.
Rizopoulos DJM. an R package for the joint modelling of longitudinal and time-to-event data. J Stat Softw 2010; 35(9): 1-33.
Orem J, Otieno MW, Remick SC. AIDS-associated cancer in developing nations. Curr Opin Oncol 2004; 16(5): 468-76.
Suárez-García I, Jarrín I, Iribarren JA, et al. Incidence and risk factors of AIDS-defining cancers in a cohort of HIV-positive adults: Importance of the definition of incident cases. Enferm Infecc Microbiol Clin 2013; 31(5): 304-12.
Bohlius J, Schmidlin K, Costagliola D, et al. Incidence and risk factors of HIV-related non-Hodgkin’s lymphoma in the era of combination antiretroviral therapy: a European multicohort study. Antivir Ther 2009; 14(8): 1065-74.
Biggar RJ, Chaturvedi AK, Goedert JJ, Engels EA. AIDS-related cancer and severity of immunosuppression in persons with AIDS. J Natl Cancer Inst 2007; 99(12): 962-72.
Clifford GM, Polesel J, Rickenbach M, et al. Cancer risk in the Swiss HIV Cohort Study: associations with immunodeficiency, smoking, and highly active antiretroviral therapy. J Natl Cancer Inst 2005; 97(6): 425-32.
Bonnet F, Balestre E, Thiébaut R, et al. Factors associated with the occurrence of aids related non-hodgkin lymphoma in the era of highly active antiretroviral therapy: aquitaine cohort, France. Clin Infect Dis 2006; 42: 411-7.
Andersen PK, Gill RD. Cox’s regression model for counting processes: A large sample study. Ann Stat 1982; 10(4): 1100-20.

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Article Details

Year: 2018
Published on: 14 January, 2019
Page: [288 - 296]
Pages: 9
DOI: 10.2174/1570162X17666181205130532

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