Statistical learning of origin-specific statically optimal individualized treatment rules

International Journal of Biostatistics

Volumn 3, Issue 1, 2007, Pages

  van der Laan, Mark J ^a   Petersen, Maya L ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Causal inference; Counterfactual; Ddynamic treatment regime; Double robust estimating function; History adjusted marginal structural model; Inverse probability weighting

Indexed keywords

ANTIRETROVIRUS AGENT; PROTEINASE INHIBITOR; VIRUS RNA; ANTI HUMAN IMMUNODEFICIENCY VIRUS AGENT;

ARTICLE; CD4 LYMPHOCYTE COUNT; CONCEPTUAL FRAMEWORK; CONFOUNDING VARIABLE; COVARIANCE; DRUG TREATMENT FAILURE; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LONGITUDINAL STUDY; MATHEMATICAL MODEL; OBSERVATIONAL STUDY; PATIENT COMPLIANCE; PROBABILITY; STATISTICAL ANALYSIS; TREATMENT OUTCOME; TREATMENT PLANNING; VIRUS RESISTANCE; ANTIVIRAL RESISTANCE; BLOOD; CAUSAL INFERENCE; COMPUTER SIMULATION; COUNTERFACTUAL; DOUBLE ROBUST ESTIMATING FUNCTION; DYNAMIC TREATMENT REGIME; FEMALE; GROWTH, DEVELOPMENT AND AGING; HISTORY-ADJUSTED MARGINAL STRUCTURAL MODEL; INVERSE PROBABILITY WEIGHTING; MALE; METHODOLOGY; PERSONALIZED MEDICINE; STATISTICAL MODEL;

CAUSAL INFERENCE; COUNTERFACTUAL; DOUBLE ROBUST ESTIMATING FUNCTION; DYNAMIC TREATMENT REGIME; HISTORY-ADJUSTED MARGINAL STRUCTURAL MODEL; INVERSE PROBABILITY WEIGHTING;

ANTI-HIV AGENTS; COMPUTER SIMULATION; DRUG RESISTANCE, VIRAL; FEMALE; HIV; HIV INFECTIONS; HUMANS; INDIVIDUALIZED MEDICINE; LONGITUDINAL STUDIES; MALE; MODELS, STATISTICAL; RNA, VIRAL; TREATMENT OUTCOME;

EID: 33947703570     PISSN: 15574679     EISSN: None     Source Type: Journal    
DOI: 10.2202/1557-4679.1040     Document Type: Article

References (25)

1. S. G. Deeks. Treatment of antiretroviral-drug-resistant HIV-1 infection. Lancet, 362(9400):2002-2011, 2003.
2. DHHS. Guidelines for the use of antiretroviral agents in HIV-1 infected adults and adolescents. Technical report, Panel on Clinical Practices for Treatment of HIV Infection, Department of Health and Human Services, March 23, 2004.
3. M.A. Hernan, E. Lanoy, D. Costagliola, and J.M. Robins. Comparison of dynamic treatment regimes via inverse probability weighting. Basic & Clinical Pharmacology & Toxicology, 98:237-242, 2006.
4. P. W. Lavori and R. Dawson. A design for testing clinical strategies: biased adaptive within-subject randomization. Journal of the Royal Statstical Society, Series A, 163:29-38, 2000.
5. E. E. M. Moodie, T. S. Richardson, and D. A. Stephens. Demystifying optimal dynamic treatment regimes. Biometrics, In Press, 2007.
6. S.A. Murphy. Optimal dynamic treatment regimes. Journal of the Royal Statistical Society: Series B, 65(2):331-355, 2003.
7. S.A. Murphy, M.J. van der Laan, J.M. Robins, and Conduct Problems Prevention Research Group. Marginal mean models for dynamic treatment regimes. Journal of the American Statistical Association, 96:1410-1424, 2002.
8. J. Neyman. On the application of probability theory to agricultural experiments. Statistical Science, 5:465-480, 1990.
9. M.L. Petersen, S.G. Deeks, J.N. Martin, and M.J. van der Laan. History-adjusted marginal structural models to estimate time-varying effect modification. American Journal of Epidemiology, 2007a. In Press.
10. M.L. Petersen, S.G. Deeks, and M.J. van der Laan. Individualized treatment rules: Generating candidate clinical trials. Statstics in Medicine, 2007b. In Press.
11. J. M. Robins, M. A. Hernan, and B. Brumback. Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5):550-560, 2000.
12. J.M. Robins. A new approach to causal inference in mortality studies with sustained exposure periods - application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12):1393-1512, 1986.
13. J.M. Robins. Addendum to: "A new approach to causal inference in mortality studies with a sustained exposure period - application to control of the healthy worker survivor effect" [Math. Modelling 7 (1986), no. 9-12, 1393-1512]. Comput. Math. Appl., 14(9-12):923-945, 1987.
14. J.M. Robins. Information recovery and bias adjustment in proportional hazards regression analysis of randomized trials using surrogate markers. In Proceedings of the Biopharmaceutical Section, pages 24-33, Alexandria, VA, 1993. American Statistical Association.
15. J.M. Robins. Correcting for non-compliance in randomized trials using structural nested mean models. Communications in Statistics, 23:2379-2412, 1994.
16. J.M. Robins. Causal inference from complex longitudinal data. In M. Berkane, editor, Latent Variable Modeling and Applications to Causality, Lecture Notes in Statistics, pages 69-117. Springer Verlag, New York, 1997.
17. J.M. Robins. Marginal structural models versus structural nested models as tools for causal inference. In M.E. Halloran and D. Berry, editors, Statistical models in epidemiology, the environment, and clinical trials, volume 116, pages 95-134. Springer-Verlag, New York, 1999.
18. J.M. Robins. Robust estimation in sequentially ignorable missing data and causal inference models. In Proceedings of the Bayesian Statstical Science Section., pages 6-10, Alexandria, VA, 2000. American Statistical Association.
19. J.M. Robins. Optimal structural nested models for optimal sequential decisions. In D.Y. Lin and P.J. Heagerty, editors, Proceedings of the 2nd Seattle Symposium in Biostatistics, volume 179 of Lecture Notes in Statistics, pages 189-326. Springer Verlag, 2004.
20. J.M. Robins and A. Rotnitzky. Recovery of information and adjustment for dependent censoring using surrogate markers. In AIDS Epidemiology, Methodological issues. Bikhäuser, 1992.
21. D.B. Rubin. Bayesian inference for causal effects: the role of randomization. Annals of Statistics, 6:34-58, 1978.
22. S. Sinisi and M.J. van der Laan. The deletion/substitution/addition algorithm in loss function based estimation: Applications in genomics. Journal of Statistical Methods in Molecular Biology, 3(1), 2004. Article 18.
23. M.J. van der Laan and M. L. Petersen. Causal effect models for realistic individualized treatment and intention to treat rules. International Journal of Biostatistics, In Press, 2007.
24. M.J. van der Laan, M.L. Petersen, and M.M. Joffe. History-adjusted marginal structural models and statically-optimal dynamic treatment regimens. The International Journal of Biostatistics, 1(1):10-20, 2005.
25. M.J. van der Laan and J.M. Robins. Unified methods for censored longitudinal data and causality. Springer, New York, 2003.