Estimating causal treatment effects from longitudinal HIV natural history studies using marginal structural models

Biometrics

Volumn 59, Issue 1, 2003, Pages 152-162

  Ko, Hyejin ^a   Hogan, Joseph W ^b   Mayer, Kenneth H ^b  

^a Samsung SDS   (South Korea)

^b BROWN UNIVERSITY   (United States)

Author keywords

Acquired immune deficiency syndrome (AIDS); CD4 count; Counterfactuals; Dropout; Endogenous treatment; HAART; Inverse probability weighting; Noncompliance; Observational study; Repeated measures; Selection bias; Semiparametric model; Sensitivity analysis

Indexed keywords

DISEASES; INVERSE PROBLEMS;

ACQUIRED IMMUNE DEFICIENCY SYNDROME; CD4 COUNT; COUNTERFACTUALS; DROPOUT; ENDOGENOUS TREATMENT; HIGHLY ACTIVE ANTI-RETROVIRAL THERAPIES; HIGHLY ACTIVE ANTIRETROVIRAL THERAPY REGIMEN; INVERSE-PROBABILITY WEIGHTING; NONCOMPLIANCE; OBSERVATIONAL STUDY; PROBABILITY WEIGHTING; REPEATED MEASURES; SELECTION BIAS; SEMI-PARAMETRIC MODELING;

SENSITIVITY ANALYSIS;

ABACAVIR; AMPRENAVIR; ANTIRETROVIRUS AGENT; ANTIVIRUS AGENT; CD4 ANTIGEN; DELAVIRDINE; DIDANOSINE; EFAVIRENZ; INDINAVIR; LAMIVUDINE; LAMIVUDINE PLUS ZIDOVUDINE; LOPINAVIR; NELFINAVIR; NEVIRAPINE; PROTEINASE INHIBITOR; RITONAVIR; RNA DIRECTED DNA POLYMERASE INHIBITOR; SAQUINAVIR; STAVUDINE; ZALCITABINE; ZIDOVUDINE;

EPIDEMIOLOGY; HUMAN IMMUNODEFICIENCY VIRUS; SENSITIVITY ANALYSIS;

ACQUIRED IMMUNE DEFICIENCY SYNDROME; ARTICLE; BIOSTATISTICS; CLINICAL TRIAL; COHORT ANALYSIS; DISEASE COURSE; FEMALE; FOLLOW UP; HIGHLY ACTIVE ANTIRETROVIRAL THERAPY; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; LEUKOCYTE COUNT; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; PROBABILITY; PROSPECTIVE STUDY; STATISTICAL MODEL; STATISTICAL PARAMETERS; TREATMENT OUTCOME; VIRUS INFECTION;

HUMAN IMMUNODEFICIENCY VIRUS; TURDUS;

EID: 0037366608     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/1541-0420.00018     Document Type: Article

References (24)

1. Anastos, K., Kalish, L. A., Hessol, N., Weiser, B., Melnick, S., Burns, D., Delapenha, R., DeHovitz, J., Cohen, M., Meyer, W., Bremer, J., and Kovacs, A. (1999). The relative value of CD4+ cell count and quantitative HIV-1 RNA in predicting survival in HIV-1 infected women: Results of the Women's Interagency HIV Study. AIDS 13, 1717-1726.
2. Angrist, J. D., Imbens G. W., and Rubin D. B. (1996). Identification of causal effects using instrumental variables (with discussion). Journal of the American Statistical Association 91, 444-472.
3. Carpenter, C. C. J., Cooper, D. A., Fischl, M. A., Gatell, J. M., Gazzard, B. G., Hammer, S. M., Hirsch, M. S., Jacobsen, D. M., Katzenstein, D. A., Montaner, J. S. G., Richman, D. D., Saag, M. S., Schechter, M., Schooley, R. T., Thompson, M. A., Vella, S., Yeni, P. G., and Volberding, P. A. (2000). Antiretroviral therapy in adults: Updated recommendations of the International AIDS Society - USA Panel. Journal of American Medical Association 283, 381-391.
4. Daniels, M. J. and Hughes M. D. (1997). Meta-analysis for the evaluation of potential surrogate markers. Statistics in Medicine 16, 1965-1982.
5. Detels, R., Muñoz, A., McFarlane, G., Kingsley, L. A., Margolick, J. B., Giorgi, J., Schrager, L. K., and Phair, J. P. (1998). Effectiveness of potent antiretroviral therapy on time to AIDS and death in men with known HIV infection duration. Journal of American Medical Association 280, 1497-1503.
6. Dornadula, G., Zhang, H., VanUitert, B., Stern, J., Livornese L., Jr., Ingerman, M. J., Witek, J., Kedanis, R. J., Natkin, J., DeSimone, J., and Pomerantz, R. J. (1999). Residual HIV-1 RNA in blood plasma of patients taking suppressive highly active antiretroviral therapy. Journal of American Medical Association - Express 282, 1627-1632.
7. Heckman, J. J. (1997). Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations. Journal of Human Resources 32, 441-462.
8. Hernán, M. A., Brumback, B., and Robins, J. M. (2000). Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 11, 561-570.
9. Hernán, M. A., Brumback, B., and Robins, J. M. (2001). Marginal structural models to estimate the joint causal effect of nonrandomized treatments. Journal of the American Statistical Association 96, 440-447.
10. Little, R. J. A. and Rubin, D. B. (1987). Statistical Inference with Missing Data. New York: Wiley.
11. Mellors, J. W., Munoz, A., Giorgi, J. V., Margolick, J. B., Tassoni, C. J., Gupta, P., Kingsley, L. A., Todd, J. A., Saah, A. J., Detels, R., Phair, J. P., and Rinaldo, C. R., Jr. (1997). Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV-1 infection. Annals of Internal Medicine 126, 946-954.
12. Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. (1996). Applied Linear Statistical Models, 4th edition. Chicago: Richard D. Irwin.
13. Paredes, R., Mocroft, A., Kirk, O., Lazzarin, A., Barton, S. E., Lunzen, J. V., Katzenstein, T. L., Antunes, F., Lundgren, J. D., and Clote, B. (2000). Predictors of virological success and ensuing failure in HIV-positive patients starting highly active antiretroviral therapy in Europe. Archives of Internal Medicine 160, 1123-1132.
14. Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge: Cambridge University Press.
15. Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 90, 106-121.
16. Robins, J. M. (1997). Causal inference from complex longitudinal data. In Latent Variable Modeling and Applications to Causality, M. Berkane (ed), 69-117. New York: Springer-Verlag.
17. Robins, J. M. (1999a). Association, causation, and marginal structural models. Synthese 121, 151-179.
18. Robins, J. M. (1999b). Marginal structural models versus structural nested models as tools for causal inference. In Statistical Models in Epidemiology: The Environment and Clinical Trials, M. E. Halloran and D. Berry (eds), 95-134. New York: Springer-Verlag.
19. Robins, J. M., Greenland, S., and Hu, F. C. (1999). Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary response (with discussion). Journal of the American Statistical Association 94, 687-712.
20. Robins, J. M., Hernán, M. A., and Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology 11, 550-560.
21. Rosenbaum, P. R. and Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association 79, 516-524.
22. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66, 688-701.
23. Smith, D. K., Warren, D. L., Vlahov, D., Schuman, P., Stein, M. D., Greenberg, B. L., and Holmberg S. D. (1997). Design and baseline participant characteristics of the human immunodeficiency virus epidemiology research (HER) study: A prospective cohort study of human immunodeficiency virus infection in the U.S. women. American Journal of Epidemiology 146, 459-469.
24. Wasserman, L. (2000). Comment on Dawid, A. P., "Causal inference without counterfactuals." Journal of the American Statistical Association 95, 442-443.