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The analysis of randomized and non-randomized AIDS treatment trials using a new approach to causal inference in longitudinal studies
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In Sechrest LA, ed Rockville, MD: NCHSR, US Public Health Service
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Robins JM. The analysis of randomized and non-randomized AIDS treatment trials using a new approach to causal inference in longitudinal studies. In: Sechrest LA, ed. Health Service Research Methodology: A Focus on AIDS. Rockville, MD: NCHSR, US Public Health Service; 1989:113-159.
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Correcting for non-compliance in randomized trials using structural nested mean models
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Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models
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Robins JM, Rotnitzky A, Scharfstein DO. Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. In: Halloran E, Berry D, eds. Statistical Models in Epidemiology. New York: Springer-Verlag; 2000:1-99.
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Estimation of the time-dependent accelerated failure time model in the presence of confounding factors
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Robins J. Estimation of the time-dependent accelerated failure time model in the presence of confounding factors. Biometrika. 1992;79:321-334.
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G-estimation of the effect of prophylaxis therapy for pneumocystic carinii pneumonia on the survival of AIDS patients
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Robins JM, Blevins D, Ritter G, Wulfsohn M. G-estimation of the effect of prophylaxis therapy for pneumocystic carinii pneumonia on the survival of AIDS patients. Epidemiology. 1992;3:319-336.
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7
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Estimation of the causal effects of time-varying exposures
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Robins JM, Hernan MA. Estimation of the causal effects of time-varying exposures. In: Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G, eds. Longitudinal Data Analysis. Boca Raton, FL: CRC Press; 2009: 553-599.
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Robins, J.M.1
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G-estimation and artificial censoring: Problems, challenges, and applications
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Joffe MM, Yang WP, Feldman HI. G-estimation and artificial censoring: Problems, challenges, and applications. Biometrics. 2011 Sep 23. doi: 10.1111/j.1541-0420.2011.01656.x. [Epub ahead of print.]
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G-estimation of causal effects, allowing for time-varying confounding
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Adjusting for differential rates of prophylaxis therapy for PCP in high-versus low-dose AZT treatment arms in an AIDS randomized trial
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Robins JM, Greenland S. Adjusting for differential rates of prophylaxis therapy for PCP in high-versus low-dose AZT treatment arms in an AIDS randomized trial. J Am Stat Assoc. 1994;89:737-749.
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Robins, J.M.1
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Causal mediation analyses with rank preserving models
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DOI 10.1111/j.1541-0420.2007.00766.x
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Ten Have TR, Joffe MM, Lynch KG, Brown GK, Maisto SA, Beck AT. Causal mediation analyses with rank preserving models. Biometrics. 2007;63:926-934. (Pubitemid 47352843)
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Marginal structural models and causal inference in epidemiology
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Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550-560. (Pubitemid 30660034)
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Robins JM. Marginal structural models versus structural nested models as tools for causal inference. In: Halloran ME, Berry D, eds. Statistical Models in Epidemiology: The Environment and Clinical Trials. New York: Springer-Verlag; 1999:95-134.
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A method for the analysis of randomized trials with compliance information: An application to the multiple risk factor intervention trial
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Robins JM, Tsiatis AA. Correcting for non-compliance in randomized trials using rank preserving structural failure time models. Commun Stat Theory Methods. 1991;20:2609-2631.
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Estimating the effect of zidovudine on Kaposi's sarcoma from observational data using a rank preserving structural failure-time model
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Causal models for estimating the effects of weight gain on mortality
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Robins JM. Causal models for estimating the effects of weight gain on mortality. Int J Obesity. 2008;32:S15-S41.
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Causal inference in continuous process with covariates observed at discrete times
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