Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men.

By Miguel A. Hern'an, Babette A. Brumback, James M. Robins. 2000

1,846 citations recorded by the source corpus.

Subfields: Infectious Diseases, Statistics and Probability

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Citations in the corpus

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “However, these methods for the static setting cannot be extended directly to time-varying treatments (Hernán et al., 2000; Schisterman et al., 2009).”
Propensity score estimation: machine learning and classification methods as alternatives to logistic regression (2010, methodology): “For instance, inverse probability of treatment weighting [43] begins with the calculation of a propensity score, which is then transformed and stabilized to create weights.”
Mining Electronic Health Records: A Survey (2017, methodology): “Hernan et al. [Hern´an et al. 2000] used marginal models to analyze the causal effect of zidovudine on the survival of human immunodeﬁciency virus-positive men participating in the Multicenter AIDS Cohort Study.”
A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health. (2008, background): “, marginal structural models (8)) or examining a very limited set of feedback mechanisms (e.”
Estimating the optimal dynamic antipsychotic treatment regime: Evidence from the sequential multiple assignment randomized CATIE Schizophrenia Study (2012, background): “Although initially proposed to estimate static treatment regimes (Robins et al., 2000; Hernán et al., 2000)—i.”
A Survey of Learning Causality with Data: Problems and Methods (2018, background): “In [58], Hernan proposed to stabilize weights to handle this issue in IPTW.”
The effect of combined antiretroviral therapy on the overall mortality of HIV-infected individuals (2010, methodology): “Formally, under the assumption that all time-varying predictors of both cART and mortality were included in the analysis, our weighted model estimates the parameters of a marginal structural Cox model [19,36] and can be used to mimic a sequentially randomized trial in which patients were assigned to”
Clopidogrel use and long-term clinical outcomes after drug-eluting stent implantation. (2007, methodology): “2 (SAS Institute Inc, Cary, NC) with robust SEs was used to estimate treatment effects, 95% confidence intervals (CIs), and P values.(17) Weighted Cox proportional hazards regression models and adjusted cumulative incidence curves were constructed using inverse probability weights.”
Use of stabilized inverse propensity scores as weights to directly estimate relative risk and its confidence intervals (2009, methodology): “A robust variance estimator [12, 19, 20] has also been used to obtain standard error of the treatment effect.”
University patenting and scientific productivity: a quantitative study of Italian academic inventors (2008, methodology): “(2004) in using the IPTW, a technique first introduced in biostatistics (Hernan et al., 2000; Robins et al., 2000).”
A Bayesian Nonparametic Approach for Estimating Individualized Treatment-Response Curves (2016, methodology): “…models (van der Laan and Petersen, 2007), doubly robust learning (Tsiatis, 2007; Zhao et al., 2015) with applications to longitudinal data analysis (Hernán et al., 2000), survival analysis (Lunceford et al., 2002), and adaptive treatment selections in clinical trials (Murphy et al., 2007a,b).”
The Impact of Academic Patenting on the Rate, Quality and Direction of (Public) Research Output (2009, background): “Although this modification does not influence the consistency of IPTW estimators, it does increase their efficiency (Hernán et al. [2000]).”
Demystifying Optimal Dynamic Treatment Regimes (2007, methodology): “We follow Hernán, Brumback, and Robins (2000) in using last observation carried forward to account for missed follow-up visits.”
A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves (2016, methodology): “…models (van der Laan and Petersen, 2007), doubly robust learning (Tsiatis, 2007; Zhao et al., 2015) with applications to longitudinal data analysis (Hernán et al., 2000), survival analysis (Lunceford et al., 2002), and adaptive treatment selections in clinical trials (Murphy et al., 2007a,b).”