AIDS and Behavior

, 15:1447

Medication Adherence: Tailoring the Analysis to the Data

  • Parya Saberi
  • Mallory O. Johnson
  • Charles E. McCulloch
  • Eric Vittinghoff
  • Torsten B. Neilands
Original Paper


The purpose of this paper is to explore more comprehensive methods to analyze antiretroviral non-adherence data. Using illustrative data and simulations, we investigated the value of using binary logistic regression (LR; dichotomized at 0% non-adherence) versus a hurdle model (combination of LR plus generalized linear model for >0% non-adherence) versus a zero-inflated negative binomial (ZINB) model (simultaneously modeling 0% non-adherence and >0% non-adherence). In simulation studies, the hurdle and ZINB models had similar power but both had higher power in comparison to LR alone. The hurdle model had higher power than ZINB in settings where covariate effects were restricted to one or the other part of the model (0% non-adherence or degree of non-adherence). Use of the hurdle and ZINB models are powerful and valuable approaches in analyzing adherence data which yield a more complete picture than LR alone. We recommend adoption of this methodology for future antiretroviral adherence research.


HIV Medication adherence Analysis Zero-inflated negative binomial model Hurdle model 


El objetivo de este trabajo es explorar de manera exhaustiva los métodos de análisis de los datos de no adherencia antiretroviral. Utilizando simulaciones y datos representativos, hemos investigado la utilidad de usar la regresión logística binaria (LR; dicotónica con no adherencia del 0%) versus un modelo de hurdle (combinación de LR más el modelo lineal generalizado para no adherencia >0%) versus un modelo de zero-inflated negative binomial (ZINB). En estudios de simulación, los modelos hurdle y ZINB han tenido potencia similar, pero ambos han tenido mayor potencia que el uso exclusivo de LR. El modelo de hurdle ha tenido mayor potencia que ZINB en configuraciones en las que los efectos de las co-variables se limitaban a una u otra parte del modelo (no adherencia del 0% o algún grado de no adherencia). El uso del modelos hurdle y de ZINB constituye un valioso y efectivo enfoque para analizar los datos de no adherencia, y ofrecerá un panorama más completo que el uso exclusivo de LR. Recomendamos que se adopte esta metodología en investigaciones futuras sobre la adherencia antiretroviral.


  1. 1.
    Paterson DL, Swindells S, Mohr J, et al. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med. 2000;133(1):21–30.PubMedGoogle Scholar
  2. 2.
    Chesney MA, Ickovics J, Hecht FM, Sikipa G, Rabkin J. Adherence: a necessity for successful HIV combination therapy. AIDS. 1999;13(Suppl A):S271–8.PubMedGoogle Scholar
  3. 3.
    Blower SM, Aschenbach AN, Gershengorn HB, Kahn JO. Predicting the unpredictable: transmission of drug-resistant HIV. Nat Med. 2001;7(9):1016–20.PubMedCrossRefGoogle Scholar
  4. 4.
    Mannheimer S, Friedland G, Matts J, Child C, Chesney M. The consistency of adherence to antiretroviral therapy predicts biologic outcomes for human immunodeficiency virus-infected persons in clinical trials. Clin Infect Dis. 2002;34(8):1115–21.PubMedCrossRefGoogle Scholar
  5. 5.
    Liu H, Golin CE, Miller LG, et al. A comparison study of multiple measures of adherence to HIV protease inhibitors. Ann Intern Med. 2001;134(10):968–77.PubMedGoogle Scholar
  6. 6.
    Walsh JC, Mandalia S, Gazzard BG. Responses to a 1 month self-report on adherence to antiretroviral therapy are consistent with electronic data and virological treatment outcome. AIDS. 2002;16(2):269–77.PubMedCrossRefGoogle Scholar
  7. 7.
    Hugen PW, Langebeek N, Burger DM, et al. Assessment of adherence to HIV protease inhibitors: comparison and combination of various methods, including MEMS (electronic monitoring), patient and nurse report, and therapeutic drug monitoring. J Acquir Immune Defic Syndr. 2002;30(3):324–34.PubMedGoogle Scholar
  8. 8.
    Duong M, Piroth L, Peytavin G, et al. Value of patient self-report and plasma human immunodeficiency virus protease inhibitor level as markers of adherence to antiretroviral therapy: relationship to virologic response. Clin Infect Dis. 2001;33(3):386–92.PubMedCrossRefGoogle Scholar
  9. 9.
    Kumar AK, Ramachandran G, Kumar P, Kumaraswami V, Swaminathan S. Can urine lamivudine be used to monitor antiretroviral treatment adherence? MedGenMed. 2006;8(4):53.PubMedGoogle Scholar
  10. 10.
    Gandhi M, Ameli N, Bacchetti P, et al. Protease inhibitor levels in hair strongly predict virologic response to treatment. AIDS. 2009;23(4):471–8.PubMedCrossRefGoogle Scholar
  11. 11.
    Grossberg R, Zhang Y, Gross R. A time-to-prescription-refill measure of antiretroviral adherence predicted changes in viral load in HIV. J Clin Epidemiol. 2004;57(10):1107–10.PubMedCrossRefGoogle Scholar
  12. 12.
    Saberi P, Caswell NH, Amodio-Groton M, Alpert P. Pharmacy-refill measure of adherence to Efavirenz can predict maintenance of HIV viral suppression. AIDS Care. 2008;20(6):741–5.PubMedCrossRefGoogle Scholar
  13. 13.
    Mullahy J. Specification and testing of some modified count data models. J Econom. 1986;33(3):341–65.CrossRefGoogle Scholar
  14. 14.
    The Healthy Living Project Team. Effects of a behavioral intervention to reduce risk of transmission among people living with HIV. J Acquir Immune Defic Syndr. 2007;44(2):213–21.Google Scholar
  15. 15.
    Chesney MA, Ickovics JR, Chambers DB, et al. Self-reported adherence to antiretroviral medications among participants in HIV clinical trials: the AACTG adherence instruments. Patient Care Committee & Adherence Working Group of the Outcomes Committee of the Adult AIDS Clinical Trials Group (AACTG). AIDS Care. 2000;12(3):255–66.PubMedCrossRefGoogle Scholar
  16. 16.
    Gordillo V, del Amo J, Soriano V, Gonzalez-Lahoz J. Sociodemographic and psychological variables influencing adherence to antiretroviral therapy. AIDS. 1999;13(13):1763–9.PubMedCrossRefGoogle Scholar
  17. 17.
    Ammassari A, Murri R, Pezzotti P, et al. Self-reported symptoms and medications side effects influence adherence to highly active antiretroviral therapy in persons with HIV infection. J Acquir Immune Defic Syndr. 2001;28(5):445–9.PubMedGoogle Scholar
  18. 18.
    Simons JS, Neal DJ, Gaher RM. Risk of marijuana-related problems among college students: an application of zero-inflated negative binomial regression. Am J Drug Alcohol Abuse. 2006;32(1):41–53.PubMedCrossRefGoogle Scholar
  19. 19.
    Fairlie AM, DeJong W, Stevenson JF, Lavigne AM, Wood MD. Fraternity and sorority leaders and members: a comparison of alcohol use, attitudes, and policy awareness. Am J Drug Alcohol Abuse. 2010;36(4):187–93.PubMedCrossRefGoogle Scholar
  20. 20.
    Singh N, Berman SM, Swindells S, et al. Adherence of human immunodeficiency virus-infected patients to antiretroviral therapy. Clin Infect Dis. 1999;29(4):824–30.PubMedCrossRefGoogle Scholar
  21. 21.
    Kleeberger CA, Phair JP, Strathdee SA, Detels R, Kingsley L, Jacobson LP. Determinants of heterogeneous adherence to HIV-antiretroviral therapies in the multicenter AIDS cohort study. J Acquir Immune Defic Syndr. 2001;26(1):82–92.PubMedCrossRefGoogle Scholar
  22. 22.
    Lambert D. Zero-inflated poisson regression with an application to defects in manufacturing. Technometrics. 1992;34(1):1–14.CrossRefGoogle Scholar
  23. 23.
    Cameron AC, Trivedi PK. Econometric models based on count data. Comparisons and applications of some estimators and tests. J Appl Econom. 1986;1(1):29–53.CrossRefGoogle Scholar
  24. 24.
    Long JS. Regression models for categorical and limited dependant variables. 1st ed. Thousand Oaks: Sage Publications, Inc; 1997.Google Scholar
  25. 25.
    Rose CE, Martin SW, Wannemuehler KA, Plikaytis BD. On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data. J Biopharm Stat. 2006;16(4):463–81.PubMedCrossRefGoogle Scholar
  26. 26.
    Horton NJ, Kim E, Saitz R. A cautionary note regarding count models of alcohol consumption in randomized controlled trials. BMC Med Res Methodol. 2007;7:9.PubMedCrossRefGoogle Scholar
  27. 27.
    Berchialla P, Baldi I, Notaro V, Barone-Monfrin S, Bassi F, Gregori D. Flexibility of Bayesian generalized linear mixed models for oral health research. Stat Med. 2009;28(28):3509–22.PubMedCrossRefGoogle Scholar
  28. 28.
    Fu AZ, Qiu Y, Radican L, Wells BJ. Health care and productivity costs associated with diabetic patients with macrovascular comorbid conditions. Diabetes Care. 2009;32(12):2187–92.PubMedCrossRefGoogle Scholar
  29. 29.
    Afifi AA, Kotlerman JB, Ettner SL, Cowan M. Methods for improving regression analysis for skewed continuous or counted responses. Annu Rev Public Health. 2007;28:95–111.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Parya Saberi
    • 1
  • Mallory O. Johnson
    • 1
  • Charles E. McCulloch
    • 2
  • Eric Vittinghoff
    • 2
  • Torsten B. Neilands
    • 1
  1. 1.Department of MedicineUniversity of CaliforniaSan FranciscoUSA
  2. 2.Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoUSA

Personalised recommendations