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Referencias:

• Palella, F.J., Deloria-Knoll, M., Chmiel, J.S., Moorman, A.C., Wood, K.C., Greenberg, A.E., Holmberg, S.D., HIV Outpatient Study (HOPS) Investigators. Survival benefit of initiating antiretroviral therapy in HIV-infected persons in different CD4+ cell strata (2003) Annals of Internal Medicine, 138, pp. 620-626
• Orellana, L., Rotnitzky, A., Robins, J.M., Generalized marginal structural models for estimating optimal treatment regimes (2006), Technical Report, Department of Biostatistics, Harvard School of Public Health; van der Laan MJ. Causal effect models for intention to treat and realistic individualized treatment rules. Working Paper 203, U.C. Berkeley Division of Biostatistics Working Paper Series, 2006. (Available from: http://www.bepress.com/ucbbiostat/paper203.); Robins, J.M., Optimal structural nested models for optimal sequential decisions (2004) Proceedings of the Second Seattle Symposium on Biostatistics, , Lin DY, Heagerty P eds, Springer: New York
• Murphy, S.A., Optimal dynamic treatment regimes (2003) Journal of the Royal Statistical Society, Series B, 65, pp. 331-366
• Moodie, E.E.M., Richardson, T.S., Stephens, D., Demystifying optimal dynamic treatment regimes (2007) Biometrics, 63 (2), pp. 447-455
• Hernán, M.A., Lanoy, E., Costagliola, D., Robins, J.M., Comparison of dynamic treatment regimes via inverse probability weighting (2006) Basic and Clinical Pharmacology and Toxicology, 98, pp. 237-242
• Lanoy, M., Mary-Krause, P., Tattevin, R., Dray-Spira, C., Duvivier, P., Fischer, Y., Obadia, F., Costagliola, C., the Clinical Epidemiology Group. Predictors identified for losses to follow-up among HIV-seropositive patients (2006) Journal of Clinical Epidemiology, 59 (8), pp. 829-835
• Robins, J.M., Marginal structural models versus structural nested models as tools for causal inference (1999) Statistical Models in Epidemiology: The Environment and Clinical Trials, pp. 95-134. , Halloran ME, Berry D eds, Springer: New York
• Robins, J.M., Correcting for non-compliance in randomized trials using structural nested mean models (1994) Communications in Statistics, 23, pp. 2379-2412
• Scharfstein, D., Rotnitzky, A., Robins, J.M., Adjusting for non-ignorable drop-out using semiparametric non-response models (1999) Journal of the American Statistical Association, 94, pp. 1096-1120
• Robins, J.M., Sued, M., Lei-Gomez, Q., Rotnitzky, A., Performance of double-robust estimators when 'inverse probability' weights are highly variable. Discussion of demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data, by Kang and Schaffer (2007) Statistical Science, , in press
• Wang Y, Petersen M, Bangsberg D, van der Laan M. Diagnosing bias in the inverse probability of treatment weighted estimator resulting from violation of experimental treatment assignment. U. C. Berkeley Division of Biostatistics Working Paper Series, 2006. (Available from: http://www.bepress.com/ucbbiostat/paper211/.); Robins, J.M., Rotnitzky, A., Recovery of information and adjustment for dependent censoring using surrogate markers (1992) AIDS Epidemiology - Methodological Issues, pp. 297-331. , eds, Birkhäuser: Boston, MA, includes errata sheet
• Robins, J.M., Scharfstein, D., Rotnitzky, A., Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models (1999) Statistical Models in Epidemiology: The Environment and Clinical Trials, pp. 1-94. , Halloran ME, Beny D eds, Springer: New York

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